{"meta":{"query_hash":"aa7fda349ab4","filters":{"venue":"International Journal of Statistics in Medical Research"},"cohort_total":407,"direct_labels_cover":2,"predictions_cover":407,"exported":407,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/aa7fda349ab4","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Statistics+in+Medical+Research"},"results":[{"id":"W1688523251","doi":"10.6000/1929-6029.2015.04.02.8","title":"Control Charts for Skewed Distributions: Johnson’s Distributions","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process capability; Control chart; Normality; Process (computing); Normal distribution; Statistical process control; Computer science; Field (mathematics); Quality (philosophy); Identification (biology); Distribution (mathematics); Stability (learning theory); Data mining; Process capability index; Work in process; Reliability engineering; Statistics; Mathematics; Engineering; Operations management; Machine learning","score_opus":0.3086161599301115,"score_gpt":0.5905829413268467,"score_spread":0.28196678139673526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1688523251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016916368,0.000700562,0.9786855,0.00025477083,0.000108430846,0.0001821637,0.00015309878,0.00079484,0.0022042114],"genre_scores_gemma":[0.6998968,0.0014054918,0.29228017,0.0004174952,0.00042759947,0.0009263804,0.0008725624,0.00035028844,0.0034231273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.981532,0.007950696,0.0012019037,0.0032588555,0.005062525,0.000994048],"domain_scores_gemma":[0.8617585,0.107248,0.009471914,0.008513742,0.012064412,0.00094340136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030089911,0.001586579,0.0021709122,0.005142818,0.0013679165,0.004461023,0.0023762058,0.0020213856,0.0034544328],"category_scores_gemma":[0.13108592,0.0005811271,0.0018546962,0.0036688414,0.004214769,0.005467778,0.0020837719,0.0034649395,0.00076228485],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005112371,0.00019224377,0.017198939,0.0005374755,0.00033881288,0.0014899011,0.0021938654,0.3186884,0.002880302,0.45130014,0.008216344,0.1964523],"study_design_scores_gemma":[0.000050224007,0.00021805643,0.0033431493,0.00014847553,0.0000658632,0.00049608987,0.00035385363,0.873694,0.0018340675,0.112924606,0.006727913,0.0001436954],"about_ca_topic_score_codex":0.0057659787,"about_ca_topic_score_gemma":0.0026777736,"teacher_disagreement_score":0.030089911,"about_ca_system_score_codex":0.0023209222,"about_ca_system_score_gemma":0.001990401,"threshold_uncertainty_score":0.1591326},"labels":[],"label_agreement":null},{"id":"W1821467623","doi":"10.6000/1929-6029.2015.04.03.6","title":"Assessment of Statistical Approaches to Model Low Count Data: An Empirical Application to Youth Delinquency","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Negative binomial distribution; Statistics; Count data; Juvenile delinquency; Poisson regression; Mathematics; Ordinary least squares; Econometrics; Poisson distribution; Binomial regression; Wald test; Regression analysis; Psychology; Statistical hypothesis testing; Demography; Population; Criminology","score_opus":0.6814575399529732,"score_gpt":0.6357197224865806,"score_spread":0.045737817466392605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1821467623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08596503,0.0011411445,0.90794456,0.0015085606,0.00009776372,0.0004567833,0.00036160077,0.0005976344,0.0019268196],"genre_scores_gemma":[0.5404991,0.0007859228,0.45580196,0.00026767742,0.0001033761,0.00093079096,0.00043955754,0.00018282447,0.0009887686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94706506,0.04594449,0.0011433555,0.0022358396,0.0032559054,0.000355406],"domain_scores_gemma":[0.6501219,0.32915616,0.008048331,0.0056634457,0.006258287,0.0007517712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.066071875,0.001451424,0.0016362305,0.00373736,0.0012472384,0.0035228124,0.0026524628,0.0020131692,0.0029136492],"category_scores_gemma":[0.1929616,0.00075597095,0.0024740018,0.0037454348,0.0020907163,0.0032469572,0.0026430315,0.0037377651,0.00046431902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038479065,0.00063687784,0.21628076,0.001584607,0.0033415984,0.0008777996,0.0048322966,0.3816728,0.0015718692,0.12643932,0.005570422,0.25680685],"study_design_scores_gemma":[0.00002676475,0.0004350648,0.012920045,0.00028513966,0.00011394263,0.0002597415,0.0011090899,0.92964035,0.00039226157,0.051788133,0.002966245,0.00006317288],"about_ca_topic_score_codex":0.0061748913,"about_ca_topic_score_gemma":0.0043591605,"teacher_disagreement_score":0.066071875,"about_ca_system_score_codex":0.0018020254,"about_ca_system_score_gemma":0.0029761544,"threshold_uncertainty_score":0.34942567},"labels":[],"label_agreement":null},{"id":"W1825717997","doi":"10.6000/1929-6029.2015.04.02.5","title":"Reliability Analysis for Two Components Connected in Parallel with Lindley Probability Model","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Reliability (semiconductor); Estimator; Statistics; Confidence interval; Moment (physics); Extension (predicate logic); Maximum likelihood; Applied mathematics; Combinatorics; Power (physics); Physics; Computer science; Thermodynamics","score_opus":0.357863702380387,"score_gpt":0.5117207094614673,"score_spread":0.1538570070810803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1825717997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09859561,0.0014669293,0.8922125,0.00073135487,0.00006798179,0.00008387956,0.00030691747,0.00032019557,0.006214578],"genre_scores_gemma":[0.9561172,0.0012378332,0.03371205,0.00009047458,0.00009439691,0.00016030556,0.00031775062,0.00006136991,0.008208537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791867,0.0005876256,0.000101537196,0.00053156144,0.0006133676,0.00024720753],"domain_scores_gemma":[0.99576,0.0023137575,0.0007855028,0.000266937,0.00072384335,0.00014994471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002868267,0.0011615814,0.00156797,0.0023798405,0.0007947823,0.0016139768,0.0024471774,0.001394016,0.0039636274],"category_scores_gemma":[0.0070280693,0.0006308504,0.0013520953,0.0018507342,0.0014765032,0.002983124,0.0012672233,0.0013286087,0.0005482814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016754415,0.000043202497,0.0042155995,0.00014681264,0.00013529877,0.0005663766,0.00031060947,0.8998012,0.0014494712,0.081337735,0.0011093224,0.010716809],"study_design_scores_gemma":[0.0000060859056,0.00003796885,0.0006822567,0.000010363745,0.00003903292,0.000111305846,0.00003945892,0.9761579,0.00020495018,0.02227763,0.00041590867,0.00001716359],"about_ca_topic_score_codex":0.009497521,"about_ca_topic_score_gemma":0.0049606063,"teacher_disagreement_score":0.009497521,"about_ca_system_score_codex":0.0023599188,"about_ca_system_score_gemma":0.0011537563,"threshold_uncertainty_score":0.01888448},"labels":[],"label_agreement":null},{"id":"W1826791789","doi":"10.6000/1929-6029.2015.04.02.9","title":"Examining Biliary Acid Constituents among Gall Bladder Patients: A Bayes Study Using the Generalized Linear Model","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Bayes' theorem; Mathematics; Gallstones; Set (abstract data type); Class (philosophy); Flexibility (engineering); Data set; Prior probability; Statistics; Internal medicine; Medicine; Computer science; Artificial intelligence; Bayesian probability","score_opus":0.38638351311207336,"score_gpt":0.5333538565799479,"score_spread":0.14697034346787458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1826791789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9171196,0.002370815,0.07657029,0.0016362111,0.000057395864,0.0001555058,0.00012642628,0.000033218235,0.0019305837],"genre_scores_gemma":[0.97993696,0.001159311,0.017798461,0.00017455139,0.00011396545,0.00006297582,0.0000985032,0.000013812215,0.000641429],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935939,0.005053848,0.00022093045,0.0005039347,0.0004281013,0.00019940529],"domain_scores_gemma":[0.9297096,0.06581915,0.0018899022,0.0013175613,0.00082600693,0.0004377852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018295066,0.00050280214,0.0011269957,0.0013737783,0.0007787091,0.0012677026,0.0007770042,0.0014277113,0.0018192952],"category_scores_gemma":[0.0621069,0.00049102306,0.0017636099,0.0009106038,0.0010777819,0.0016402283,0.0010034733,0.0011523399,0.00020896955],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013814552,0.0008141624,0.8016224,0.00046679462,0.0013744418,0.0031026932,0.005891073,0.01775587,0.0012966145,0.050743047,0.001536787,0.11401465],"study_design_scores_gemma":[0.00036539746,0.0030930827,0.19471973,0.000626913,0.002050301,0.009286312,0.0089127505,0.6723257,0.0010272257,0.10242699,0.004940201,0.0002254799],"about_ca_topic_score_codex":0.003521375,"about_ca_topic_score_gemma":0.0021015923,"teacher_disagreement_score":0.018295066,"about_ca_system_score_codex":0.00045151525,"about_ca_system_score_gemma":0.00097381655,"threshold_uncertainty_score":0.09675473},"labels":[],"label_agreement":null},{"id":"W1828732580","doi":"10.6000/1929-6029.2015.04.03.1","title":"Assessment of the Performance of Imputation Techniques in Observational Studies with Two Measurements","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Euskal Herriko Unibertsitatea; Eusko Jaurlaritza","keywords":"Missing data; Statistics; Imputation (statistics); Markov chain Monte Carlo; Observational study; Propensity score matching; Sample size determination; Econometrics; Monte Carlo method; Mathematics","score_opus":0.6416042088483054,"score_gpt":0.6419404073228557,"score_spread":0.00033619847455035323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1828732580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15373288,0.011057544,0.82580465,0.0016197952,0.00041751252,0.0024361643,0.0018122207,0.00082099636,0.0022981623],"genre_scores_gemma":[0.5406044,0.0021915303,0.45174757,0.00042646925,0.00016980972,0.002948772,0.0011886287,0.00019072622,0.0005320669],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.75543356,0.21323912,0.013229409,0.007029166,0.010058723,0.0010100009],"domain_scores_gemma":[0.32767117,0.604445,0.030087328,0.023029374,0.013844929,0.00092220993],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.30562538,0.0015602405,0.0024177306,0.0030184803,0.0011174205,0.0021812797,0.0023152716,0.0035628967,0.0019781732],"category_scores_gemma":[0.4772981,0.0009190237,0.005859033,0.0044999537,0.0016371531,0.0025044803,0.0027030625,0.0025949404,0.00042442573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008482501,0.000598813,0.41882327,0.008654515,0.028242204,0.0013107762,0.004173324,0.1684113,0.0035528932,0.016151555,0.004364213,0.33723465],"study_design_scores_gemma":[0.0012388221,0.007652266,0.18603441,0.0034751196,0.009874633,0.003003384,0.0013612418,0.7373487,0.0076124477,0.028072042,0.013737836,0.00058910163],"about_ca_topic_score_codex":0.0019440175,"about_ca_topic_score_gemma":0.0014894501,"teacher_disagreement_score":0.6943746,"about_ca_system_score_codex":0.001012618,"about_ca_system_score_gemma":0.0022088995,"threshold_uncertainty_score":0.85628754},"labels":[],"label_agreement":null},{"id":"W1835043643","doi":"10.6000/1929-6029.2015.04.03.8","title":"Application of Generalized Additive Models to the Evaluation of Continuous Markers for Classification Purposes","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biomarker; Receiver operating characteristic; Logistic regression; Statistics; Computer science; Binary classification; Binary number; Artificial intelligence; Pattern recognition (psychology); Mathematics; Data mining; Machine learning; Support vector machine; Biology","score_opus":0.5261117184945352,"score_gpt":0.5915120428687901,"score_spread":0.06540032437425491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1835043643","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018371603,0.0009163846,0.97907287,0.00047447195,0.0000984476,0.00014170304,0.000107625405,0.00023508939,0.000581853],"genre_scores_gemma":[0.53962773,0.0015215983,0.45560473,0.0003434224,0.00025129406,0.00095809816,0.00032827494,0.00010459149,0.0012602427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98118883,0.014869891,0.0005916388,0.0011667811,0.001855083,0.00032785482],"domain_scores_gemma":[0.9635121,0.031200454,0.0018098512,0.0012738357,0.0019259262,0.00027775977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021131188,0.0022903297,0.00211453,0.0033869364,0.0006033499,0.0028118175,0.0020664965,0.0015732364,0.0012402401],"category_scores_gemma":[0.056665484,0.0006318565,0.002881814,0.002943448,0.0018009561,0.0014209666,0.0019840724,0.0027633614,0.00039475676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035857336,0.00025089888,0.015133497,0.0006847896,0.0013685849,0.00055054575,0.0007044603,0.7352711,0.0020825288,0.06548541,0.0019671582,0.17614242],"study_design_scores_gemma":[0.000027524573,0.00023583342,0.0015127254,0.00007148372,0.00011391853,0.00011838957,0.00006955537,0.93427736,0.00050508545,0.06207856,0.00093635457,0.000053224063],"about_ca_topic_score_codex":0.0031485192,"about_ca_topic_score_gemma":0.0024983708,"teacher_disagreement_score":0.021131188,"about_ca_system_score_codex":0.0014483057,"about_ca_system_score_gemma":0.0016612042,"threshold_uncertainty_score":0.11175376},"labels":[],"label_agreement":null},{"id":"W1845661996","doi":"10.6000/1929-6029.2015.04.03.7","title":"Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":465,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; U.S. President’s Emergency Plan for AIDS Relief; Centers for Disease Control and Prevention; Georgia State University","keywords":"Categorical variable; Missing data; Imputation (statistics); Computer science; Data mining; Multivariate statistics; Statistics; Econometrics; Mathematics; Machine learning","score_opus":0.4574323852140853,"score_gpt":0.598124388630447,"score_spread":0.14069200341636173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1845661996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000455444,0.00042478228,0.99764484,0.00058207277,0.00009465556,0.000118528726,0.00015312551,0.00023053425,0.00029601407],"genre_scores_gemma":[0.01588058,0.0008454687,0.9807349,0.0004601938,0.00018612848,0.0009774745,0.00048757828,0.00015563502,0.000272068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8830714,0.10319077,0.004312978,0.0028399157,0.0060067456,0.0005781569],"domain_scores_gemma":[0.8534623,0.11462827,0.009551147,0.015827341,0.0055745654,0.0009564669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08471342,0.0012580785,0.0018463501,0.0038641454,0.001255518,0.0022958433,0.0042369333,0.0029396694,0.0061546452],"category_scores_gemma":[0.24677205,0.0016232072,0.00330751,0.00815137,0.002762179,0.0035261235,0.0037996487,0.0076816776,0.001948381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002704508,0.00012536177,0.011071838,0.0030209436,0.001137503,0.000794199,0.0020298238,0.07060639,0.001732735,0.533648,0.045549855,0.33001298],"study_design_scores_gemma":[0.00017169664,0.00034877664,0.003682172,0.001663818,0.00031110697,0.0008867013,0.00031621195,0.34190813,0.0023484917,0.59224,0.055801608,0.00032140204],"about_ca_topic_score_codex":0.0028182743,"about_ca_topic_score_gemma":0.004388935,"teacher_disagreement_score":0.08471342,"about_ca_system_score_codex":0.0013162857,"about_ca_system_score_gemma":0.0071257423,"threshold_uncertainty_score":0.44801277},"labels":[],"label_agreement":null},{"id":"W1850812664","doi":"10.6000/1929-6029.2015.04.03.3","title":"Comparative Analysis of the Effects of Three Antithrombotic Regimens on Clinical Outcomes of Patients with Atrial Fibrillation and Recent Percutaneous Coronary Intervention with Stent. A Retrospective Cohort Study","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Clopidogrel; Atrial fibrillation; Internal medicine; Aspirin; Conventional PCI; Warfarin; Percutaneous coronary intervention; Cardiology; Stroke (engine); Antithrombotic; Retrospective cohort study; Myocardial infarction","score_opus":0.11455589306820659,"score_gpt":0.4763226006438735,"score_spread":0.3617667075756669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1850812664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992501,0.000276424,0.000065208726,0.000007912361,0.000006233613,0.000016796554,0.000252222,0.000001314829,0.00012371787],"genre_scores_gemma":[0.99934095,0.000100526995,0.000064104606,0.000012634218,0.000010543617,0.000018480692,0.0003978704,0.0000016773948,0.000053131105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985411,0.0003406541,0.00020901242,0.00045866214,0.0002729311,0.00017764437],"domain_scores_gemma":[0.99708945,0.0005371381,0.0013592853,0.00034355777,0.00028606656,0.00038448695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015296107,0.00042741137,0.000624055,0.0008478335,0.00048573705,0.00075163756,0.00043649727,0.0006943141,0.0012105717],"category_scores_gemma":[0.003201736,0.00042462302,0.001391704,0.0011809776,0.000322797,0.00059046276,0.0005084632,0.000634546,0.00023189727],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013090686,0.000110221066,0.9970921,0.000018070272,0.00039434378,0.00007393086,0.00006249957,0.000028918128,0.00016776359,0.000011236246,0.000036404326,0.0006954059],"study_design_scores_gemma":[0.00007404565,0.0010087596,0.9978492,0.0000067388246,0.00024195145,0.0002834524,0.00017350809,0.00014573954,0.00006746789,0.000016547125,0.0001238059,0.0000086891005],"about_ca_topic_score_codex":0.002029534,"about_ca_topic_score_gemma":0.00199381,"teacher_disagreement_score":0.002029534,"about_ca_system_score_codex":0.0003496737,"about_ca_system_score_gemma":0.0003995011,"threshold_uncertainty_score":0.008089423},"labels":[],"label_agreement":null},{"id":"W1865722537","doi":"10.6000/1929-6029.2015.04.03.4","title":"Comparative Study of Human and Automated Screening for Antinuclear Antibodies by Immunofluorescence on HEp-2 Cells","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Commission","keywords":"IIf; Concordance; Anti-nuclear antibody; Indirect immunofluorescence; Immunofluorescence; Antibody; Medicine; Pathology; Internal medicine; Biology; Immunology; Autoantibody","score_opus":0.1614452498817089,"score_gpt":0.5150128195648975,"score_spread":0.3535675696831886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1865722537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99399865,0.0008254297,0.0039497428,0.00001560358,0.000013016308,0.000025798921,0.00011677656,0.000042823973,0.0010121824],"genre_scores_gemma":[0.99409086,0.00021616014,0.004956828,0.000027767968,0.000015997224,0.00002651625,0.00037109258,0.000010014493,0.0002846001],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99695504,0.0014040229,0.00014605942,0.00051585643,0.0008534627,0.00012556586],"domain_scores_gemma":[0.99650735,0.0019319499,0.00035089263,0.0003788094,0.0007233801,0.00010765691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029232926,0.0002870445,0.000281732,0.0010133963,0.00016636636,0.00056302914,0.00028129315,0.00037592728,0.001022243],"category_scores_gemma":[0.0033800805,0.00016117423,0.00017535624,0.00040361713,0.0003344839,0.000272328,0.00038775158,0.00019561668,0.00028740102],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006272314,0.00054157915,0.5183756,0.00052596885,0.0004052594,0.0004290074,0.0010333435,0.0015815366,0.40233693,0.00038248263,0.0005260235,0.067589894],"study_design_scores_gemma":[0.00012966001,0.004566334,0.7329881,0.00007040611,0.000256839,0.0024002383,0.00040218068,0.0154566895,0.24012578,0.00019166201,0.0033580342,0.000053992586],"about_ca_topic_score_codex":0.0010096278,"about_ca_topic_score_gemma":0.0009462553,"teacher_disagreement_score":0.0029232926,"about_ca_system_score_codex":0.0003920616,"about_ca_system_score_gemma":0.00019477264,"threshold_uncertainty_score":0.015460074},"labels":[],"label_agreement":null},{"id":"W1866467299","doi":"10.6000/ijsmr.v2i1.822","title":"Editorial - In the Kingdom of the Blind, the One-Eyed Man is King: The Case for The International Journal of Statistics in Medical Research","year":2013,"lang":"en","type":"editorial","venue":"International Journal of Statistics in Medical Research","topic":"Public Health Policies and Education","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kingdom; Medical research; Statistics; Psychology; Medicine; Mathematics; Geology; Pathology","score_opus":0.25518790499142835,"score_gpt":0.633006505619131,"score_spread":0.3778186006277026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866467299","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020566133,0.0017097942,0.00006510788,0.023345958,0.9738729,0.000019332405,0.000034729732,0.000044760498,0.0008868764],"genre_scores_gemma":[0.00026517684,0.0016698946,0.000108979984,0.019910103,0.9728574,0.000023871171,0.000024713909,0.00005370384,0.00508603],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9822712,0.0028811677,0.0021867573,0.001528073,0.010275756,0.0008570311],"domain_scores_gemma":[0.91898257,0.030099228,0.004659615,0.0020064039,0.034046214,0.010205917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01570727,0.0042658555,0.0061254455,0.0070075304,0.0071554575,0.016390605,0.0057728062,0.021736352,0.019827595],"category_scores_gemma":[0.07728543,0.0015470505,0.0037150646,0.0034576813,0.0053224824,0.0062221414,0.0021702638,0.023819346,0.015964784],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001972233,0.000011689953,0.000021679092,0.00014877637,0.000015304468,0.0000717125,0.0000131887955,0.000011557419,0.000019435627,0.00015936368,0.9976361,0.0018715202],"study_design_scores_gemma":[0.000102167156,0.00004156043,0.00040066836,0.0014008392,0.00010196402,0.0003839949,0.00016981082,0.00021980918,0.00011608243,0.0021791526,0.99484044,0.000043557076],"about_ca_topic_score_codex":0.0036523251,"about_ca_topic_score_gemma":0.012435779,"teacher_disagreement_score":0.021736352,"about_ca_system_score_codex":0.005122446,"about_ca_system_score_gemma":0.009199102,"threshold_uncertainty_score":0.08306897},"labels":[],"label_agreement":null},{"id":"W1872753067","doi":"10.6000/1929-6029.2015.04.03.5","title":"A Contribution to the Genetic Epidemiology of Structured Populations","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inbreeding; Population; Genetic divergence; Divergence (linguistics); Statistics; Mating; Biology; Mating system; Sample (material); Econometrics; Evolutionary biology; Mathematics; Genetic diversity; Genetics; Demography; Physics","score_opus":0.11622830327734236,"score_gpt":0.4895433162741286,"score_spread":0.37331501299678627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1872753067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016311195,0.026559968,0.9291236,0.011221175,0.0035859009,0.00007535329,0.00055233133,0.00019308148,0.012377383],"genre_scores_gemma":[0.34606382,0.07703845,0.5184303,0.0081040505,0.027703324,0.00028965753,0.001181125,0.00033617913,0.020853026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975757,0.0013016975,0.00011215572,0.00052030146,0.00039555808,0.00009468583],"domain_scores_gemma":[0.9893884,0.00699553,0.0010232261,0.0011332358,0.0009428594,0.00051665044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030219231,0.0006943534,0.0011973634,0.0032563442,0.0008668363,0.002814653,0.0013608543,0.0016736543,0.003239752],"category_scores_gemma":[0.016156957,0.00062603876,0.0012614651,0.0028957124,0.0019695912,0.0023647759,0.002508665,0.0027209816,0.00088732113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000321688,0.00012589466,0.026065486,0.0007525692,0.0004064941,0.00063025963,0.0007346016,0.037732787,0.0026003816,0.7355463,0.016047519,0.1793255],"study_design_scores_gemma":[0.00002259966,0.00007167432,0.009163073,0.00032522585,0.00011329612,0.0014771839,0.0002002346,0.052172534,0.000493005,0.81012976,0.1257166,0.00011480164],"about_ca_topic_score_codex":0.0017117971,"about_ca_topic_score_gemma":0.0015688782,"teacher_disagreement_score":0.0032563442,"about_ca_system_score_codex":0.0008916784,"about_ca_system_score_gemma":0.0017123268,"threshold_uncertainty_score":0.015981674},"labels":[],"label_agreement":null},{"id":"W1935404031","doi":"10.6000/1929-6029.2015.04.02.1","title":"Statistics and Policy Decisions: Issues in Statistical Analyses","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Observational study; Foundation (evidence); Rest (music); Psychology; Order (exchange); Observational methods in psychology; Management science; Actuarial science; Positive economics; Political science; Statistics; Economics; Medicine; Law; Mathematics","score_opus":0.28188928361793203,"score_gpt":0.5962521327074818,"score_spread":0.31436284908954976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1935404031","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017278994,0.013144884,0.114126526,0.8401434,0.023570992,0.00059396005,0.00044194545,0.00038418605,0.0058662076],"genre_scores_gemma":[0.11724155,0.015552753,0.3309389,0.44717005,0.075777866,0.007845436,0.0007090996,0.0013871744,0.0033772644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.13676798,0.7802652,0.034957804,0.007865138,0.037811406,0.0023323942],"domain_scores_gemma":[0.04005785,0.89667517,0.011174503,0.0239854,0.026024474,0.0020826072],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.7096869,0.0026981977,0.0076583293,0.011558797,0.007153046,0.025628885,0.012608037,0.027112454,0.004421897],"category_scores_gemma":[0.9060052,0.0030569332,0.0044514406,0.019771032,0.06405168,0.026734661,0.009829465,0.051559824,0.0029453533],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008028931,0.00022983812,0.00324231,0.0028109776,0.0010905578,0.0006399269,0.009898455,0.003597708,0.00027850756,0.62724096,0.23959592,0.11057204],"study_design_scores_gemma":[0.0003150226,0.00017252796,0.0014772947,0.005897466,0.00020486052,0.00029847084,0.002675325,0.010393482,0.00042797785,0.8797862,0.09809639,0.00025504935],"about_ca_topic_score_codex":0.010773326,"about_ca_topic_score_gemma":0.005752886,"teacher_disagreement_score":0.7096869,"about_ca_system_score_codex":0.013223003,"about_ca_system_score_gemma":0.03126433,"threshold_uncertainty_score":0.35800785},"labels":[],"label_agreement":null},{"id":"W1938396017","doi":"10.6000/1929-6029.2015.04.02.6","title":"Non-Parametric Test for Ordered Medians: The Jonckheere Terpstra Test","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Statistics; Test (biology); Population; Sample size determination; Kruskal–Wallis one-way analysis of variance; Parametric statistics; Mathematics; Medicine; Mann–Whitney U test; Biology","score_opus":0.09337988744516092,"score_gpt":0.4106866710945514,"score_spread":0.3173067836493905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1938396017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06494272,0.010261303,0.87103915,0.0040742746,0.0031140957,0.004073358,0.0046757744,0.0020660993,0.03575325],"genre_scores_gemma":[0.5800659,0.0034983298,0.38657504,0.0025630768,0.00149984,0.0121374745,0.0025075315,0.00076229125,0.010390486],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9230819,0.039139904,0.0060546272,0.010385159,0.01947877,0.0018597224],"domain_scores_gemma":[0.70704997,0.25134936,0.01483984,0.012870952,0.01215227,0.00173761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03705203,0.0014841252,0.0036102384,0.0039441255,0.0019693784,0.0043566655,0.0034253367,0.0034778027,0.023685964],"category_scores_gemma":[0.2764167,0.00066103565,0.0028298292,0.005299064,0.0049391966,0.0054566944,0.0032864253,0.006706039,0.0037900798],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005494234,0.0010140846,0.12356412,0.007099224,0.0034414716,0.0039779744,0.0053928527,0.011186054,0.0050726742,0.16746342,0.056848332,0.60944563],"study_design_scores_gemma":[0.0010936703,0.00991443,0.15979509,0.005349551,0.0023553327,0.014763088,0.008546223,0.14663363,0.015277379,0.4001516,0.23490465,0.0012153022],"about_ca_topic_score_codex":0.0012728755,"about_ca_topic_score_gemma":0.0007101768,"teacher_disagreement_score":0.03705203,"about_ca_system_score_codex":0.0016345263,"about_ca_system_score_gemma":0.004310646,"threshold_uncertainty_score":0.19595224},"labels":[],"label_agreement":null},{"id":"W1945131993","doi":"10.6000/1929-6029.2015.04.02.4","title":"Predicting Upcoming Glucose Levels in Patients with Type 1 Diabetes Using a Generalized Autoregressive Conditional Heteroscedasticity Modelling Approach","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diabetes Management and Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Australian Government; Therapeutic Innovation Australia; QIMR Berghofer Medical Research Institute","keywords":"Heteroscedasticity; Autoregressive model; Autoregressive conditional heteroskedasticity; Volatility (finance); Econometrics; Type 2 diabetes; Continuous glucose monitoring; Diabetes mellitus; Type 1 diabetes; Computer science; Mathematics; Medicine; Endocrinology","score_opus":0.1601417147411025,"score_gpt":0.42779309247260694,"score_spread":0.26765137773150444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1945131993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74158967,0.0016899179,0.2522953,0.0011138752,0.00011553659,0.000077262244,0.00091054844,0.0005141185,0.0016937872],"genre_scores_gemma":[0.9814741,0.0004917254,0.016804954,0.000053285275,0.000049601826,0.000033233544,0.00065611704,0.00001274917,0.00042429403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948186,0.00023139834,0.000045124383,0.00010622435,0.00007467262,0.000060700302],"domain_scores_gemma":[0.9985476,0.0010364699,0.00018441479,0.00006199779,0.00011252417,0.000056959143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014361953,0.00053632085,0.0006775425,0.0008095584,0.00018086299,0.0007501688,0.00057927397,0.00055640563,0.0005085473],"category_scores_gemma":[0.0035543207,0.00025033017,0.0011233798,0.0008493812,0.00013583335,0.00037155757,0.0004451448,0.00084919913,0.00015490185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050666026,0.00025997098,0.17394733,0.00013073096,0.00056892267,0.0004859253,0.00027492206,0.70516145,0.0018644865,0.0026743105,0.0016499078,0.11247537],"study_design_scores_gemma":[0.0000075798107,0.00010410941,0.0129637085,0.000012036607,0.000052645235,0.000042613938,0.000036604542,0.9853001,0.00025102944,0.0010437648,0.00017142296,0.00001423488],"about_ca_topic_score_codex":0.012880912,"about_ca_topic_score_gemma":0.010139606,"teacher_disagreement_score":0.012880912,"about_ca_system_score_codex":0.0003919349,"about_ca_system_score_gemma":0.0009153478,"threshold_uncertainty_score":0.025611877},"labels":[],"label_agreement":null},{"id":"W1953119886","doi":"10.6000/1929-6029.2015.04.02.3","title":"Measurement and Mismeasurement of Social Development in Infants Later Diagnosed with Autism Spectrum Disorder","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; Marcus Foundation; Georgia Research Alliance; Simons Foundation","keywords":"Autism; Normative; Autism spectrum disorder; Developmental psychology; Psychology; Cohort; Cognitive psychology; Developmental disorder; Medicine","score_opus":0.11737391240369718,"score_gpt":0.4117426585068517,"score_spread":0.29436874610315455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953119886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99928683,0.00008419534,0.00013747493,0.00007260514,0.000011783735,0.0000081768385,0.00008068796,0.0000046911773,0.000313599],"genre_scores_gemma":[0.9989505,0.0001852171,0.0003748376,0.00006219546,0.00000953811,0.000042284584,0.00018109658,0.0000051151733,0.00018921235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99710435,0.001007983,0.00026927795,0.0005354376,0.0007211061,0.0003618037],"domain_scores_gemma":[0.9930928,0.0018550821,0.0027426102,0.0005602111,0.0011772421,0.0005721048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004952552,0.0004267795,0.0003893037,0.0011850517,0.00093104714,0.001328283,0.0009638154,0.0008709937,0.0005121373],"category_scores_gemma":[0.023478236,0.00042176322,0.0004782193,0.000646426,0.0011973954,0.00084103644,0.0026588961,0.0017840178,0.00029727435],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083964864,0.00008540163,0.99039406,0.000016675498,0.000030008714,0.00026194382,0.0039342283,0.000047211997,0.0005345814,0.0000947683,0.00018936739,0.004327812],"study_design_scores_gemma":[0.0000012893547,0.00014972077,0.996516,0.000017600925,0.000014651416,0.0005193346,0.002037525,0.00012289554,0.00030748404,0.00006840212,0.00023513379,0.00000986039],"about_ca_topic_score_codex":0.012205447,"about_ca_topic_score_gemma":0.012077713,"teacher_disagreement_score":0.012205447,"about_ca_system_score_codex":0.0009813242,"about_ca_system_score_gemma":0.0006981954,"threshold_uncertainty_score":0.02619189},"labels":[],"label_agreement":null},{"id":"W1953676460","doi":"10.6000/1929-6029.2015.04.02.7","title":"Using Propensity Score Matching in Clinical Investigations: A Discussion and Illustration","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Propensity score matching; Observational study; Matching (statistics); Randomized controlled trial; Computer science; Percutaneous coronary intervention; Conventional PCI; Medicine; Internal medicine; Pathology","score_opus":0.7586343223110931,"score_gpt":0.6327789393899821,"score_spread":0.12585538292111098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953676460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018233143,0.24302876,0.61421716,0.12458963,0.004662345,0.000594711,0.00022279809,0.00020140017,0.010659882],"genre_scores_gemma":[0.054301772,0.34571314,0.5487906,0.029469231,0.016488757,0.0024546098,0.00022397056,0.0002002901,0.0023576291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92981905,0.059020977,0.0041441866,0.001928033,0.004647887,0.00043982983],"domain_scores_gemma":[0.8712616,0.118281856,0.0032009583,0.0027333016,0.004106166,0.0004160357],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.100616746,0.0015546868,0.002121398,0.0065338523,0.0016136296,0.005459166,0.0042427955,0.008066099,0.0033518965],"category_scores_gemma":[0.13419615,0.0010452385,0.0028473742,0.009377269,0.010781373,0.006543349,0.004204204,0.011495111,0.0012305763],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077542674,0.00007275546,0.0023745003,0.005257128,0.00028454178,0.0009612371,0.0011395209,0.00433808,0.000312549,0.8353381,0.02546428,0.12437972],"study_design_scores_gemma":[0.00008571346,0.00017939715,0.0017458012,0.0072770854,0.00014513233,0.0024015938,0.00043199077,0.011143108,0.00045881496,0.8305512,0.14544255,0.00013766417],"about_ca_topic_score_codex":0.0027505832,"about_ca_topic_score_gemma":0.0022687442,"teacher_disagreement_score":0.89938325,"about_ca_system_score_codex":0.0027116153,"about_ca_system_score_gemma":0.0043982957,"threshold_uncertainty_score":0.53211856},"labels":[],"label_agreement":null},{"id":"W1955533988","doi":"10.6000/1929-6029.2015.04.02.2","title":"On the Relationship between the Reliability and Accuracy of Bio-Behavioral Diagnoses: Simple Math to the Rescue","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medical diagnosis; Kappa; Mathematics; Statistics; Cohen's kappa; Equivalence (formal languages); Statistic; Youden's J statistic; Combinatorics; Psychology; Medicine; Discrete mathematics; Pathology; Receiver operating characteristic; Geometry","score_opus":0.5558141781385264,"score_gpt":0.5812588006269411,"score_spread":0.025444622488414725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955533988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1341599,0.0065764543,0.77505845,0.024420489,0.0010906957,0.00052711956,0.0011731823,0.0006519246,0.056341715],"genre_scores_gemma":[0.7232857,0.0022991614,0.263975,0.0032376773,0.0008563174,0.0008568435,0.00046585553,0.00021929806,0.0048041972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9737587,0.01648511,0.0013512783,0.004404689,0.0036696224,0.00033057702],"domain_scores_gemma":[0.67391473,0.29891717,0.006809124,0.012113494,0.0076805525,0.0005650052],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.050236385,0.0016805313,0.0025172362,0.0054590907,0.0011623529,0.003649302,0.0026481356,0.00340312,0.0062343245],"category_scores_gemma":[0.346624,0.0011535442,0.0022944752,0.0028601263,0.012176323,0.0069989855,0.0051569487,0.0038336436,0.0016561358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067970145,0.00027947934,0.09642831,0.0010403782,0.002039261,0.0007740232,0.005428107,0.083076954,0.0019086055,0.5116899,0.016437253,0.2802181],"study_design_scores_gemma":[0.0001393438,0.0005725859,0.024059296,0.0006378466,0.00024916808,0.0010784668,0.0009277313,0.15573576,0.0008009255,0.807289,0.008322484,0.00018746378],"about_ca_topic_score_codex":0.006811299,"about_ca_topic_score_gemma":0.0024795025,"teacher_disagreement_score":0.9497636,"about_ca_system_score_codex":0.00295246,"about_ca_system_score_gemma":0.0012868623,"threshold_uncertainty_score":0.26567858},"labels":[],"label_agreement":null},{"id":"W1966608570","doi":"10.6000/1929-6029.2014.03.04.9","title":"Examining the Probabilities of Type I Error for Unadjusted All Pairwise Comparisons and Bonferroni Adjustment Approaches in Hypothesis Testing for Proportions","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bonferroni correction; Type I and type II errors; Mathematics; Statistics; Pairwise comparison; Sample size determination; Type (biology); Quadratic equation; Sample (material); Econometrics; Biology","score_opus":0.8913149896988433,"score_gpt":0.6204963477873874,"score_spread":0.2708186419114559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966608570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058765724,0.008946106,0.91610926,0.001576658,0.002689049,0.0060981456,0.0007312322,0.00067036215,0.00441348],"genre_scores_gemma":[0.46863595,0.0018806816,0.50568104,0.0017462556,0.0006449886,0.018901184,0.00049711874,0.0003795498,0.0016331685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.43959275,0.41524157,0.03919471,0.04575938,0.056059077,0.004152508],"domain_scores_gemma":[0.14309168,0.7882522,0.029428294,0.026513,0.011785549,0.00092934613],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.39926824,0.0023217073,0.0042913323,0.0043508057,0.002021808,0.0039112237,0.0042616995,0.005399123,0.004824124],"category_scores_gemma":[0.643722,0.0012988949,0.008147255,0.0049700653,0.008208929,0.0054795444,0.004158182,0.007872821,0.00064630585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020431548,0.0016826958,0.22585334,0.02377225,0.033861637,0.0040402473,0.012141229,0.032944076,0.007037727,0.109774254,0.012906183,0.5155548],"study_design_scores_gemma":[0.00381733,0.03582375,0.21362849,0.014809521,0.027577026,0.008277795,0.0098181665,0.27558976,0.05037222,0.29305804,0.06546494,0.0017629301],"about_ca_topic_score_codex":0.0011171534,"about_ca_topic_score_gemma":0.00093457825,"teacher_disagreement_score":0.60073173,"about_ca_system_score_codex":0.0032355634,"about_ca_system_score_gemma":0.005219381,"threshold_uncertainty_score":0.7408092},"labels":[],"label_agreement":null},{"id":"W1967141176","doi":"10.6000/1929-6029.2014.03.04.5","title":"Determinants of Wasting Among Under-Five Children in Ethiopia: (A Multilevel Logistic Regression Model Approach)","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wasting; Logistic regression; Descriptive statistics; Malnutrition; Developing country; Multilevel model; Demography; Environmental health; Body mass index; Regression analysis; Medicine; Pediatrics; Geography; Statistics; Mathematics; Sociology","score_opus":0.10999079293665139,"score_gpt":0.45779029222978557,"score_spread":0.3477994992931342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967141176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900468,0.00046602447,0.0060319556,0.001004721,0.000058457106,0.00011849351,0.001248716,0.000036510144,0.0009883754],"genre_scores_gemma":[0.9925074,0.00032934675,0.005401994,0.00005925333,0.00003043243,0.00015211775,0.0006981952,0.000007835046,0.0008135171],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985043,0.0009278894,0.00006735847,0.00016636585,0.00008182636,0.00025238036],"domain_scores_gemma":[0.99833137,0.000995264,0.0002427978,0.000084443476,0.00017143409,0.00017475316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023808551,0.00059696886,0.0007314323,0.0011594484,0.00074034964,0.0014211432,0.0012099429,0.00068166887,0.0037467303],"category_scores_gemma":[0.0039432673,0.0005606704,0.0030011097,0.0009826064,0.00020246409,0.00061556045,0.0014631143,0.0018589522,0.00029623462],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003880827,0.00056776515,0.9659435,0.00018121218,0.00207655,0.0007586839,0.00093768805,0.011655341,0.00032345994,0.0022518618,0.0023312205,0.012584605],"study_design_scores_gemma":[0.00020612663,0.0016006364,0.5708765,0.0006310579,0.0032935785,0.0007305664,0.0059417365,0.4075911,0.0005029762,0.0045364215,0.0039720526,0.00011734789],"about_ca_topic_score_codex":0.039432913,"about_ca_topic_score_gemma":0.026839398,"teacher_disagreement_score":0.039432913,"about_ca_system_score_codex":0.0008640169,"about_ca_system_score_gemma":0.0018715032,"threshold_uncertainty_score":0.07840675},"labels":[],"label_agreement":null},{"id":"W1967332399","doi":"10.6000/1929-6029.2015.04.01.2","title":"Evaluating Treatment Effect in Multicenter Trials with Small Centers Using Survival Modeling","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Yale University","keywords":"Clinical trial; Medicine; Multicenter study; Overall survival; Multicenter trial; Estimation; Disease; Proportional hazards model; Internal medicine; Randomized controlled trial","score_opus":0.8737339108880722,"score_gpt":0.6646733206934199,"score_spread":0.20906059019465228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967332399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43305844,0.007750409,0.5434781,0.0042317472,0.0005687152,0.006071302,0.0013135364,0.0008445635,0.0026832647],"genre_scores_gemma":[0.8947222,0.0005031331,0.098682456,0.0007191073,0.00017958868,0.004105443,0.000596925,0.00006232065,0.00042876514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.62615114,0.35387048,0.008321812,0.0074727507,0.0027122814,0.0014715281],"domain_scores_gemma":[0.24135444,0.6919509,0.033454493,0.026992373,0.004182087,0.0020656781],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.42627826,0.0024688472,0.005822203,0.0023665088,0.0010470353,0.0033671183,0.003834421,0.0038976078,0.003592005],"category_scores_gemma":[0.48417568,0.0018206206,0.007951573,0.0027300774,0.0040247925,0.004460568,0.004185663,0.0040934607,0.00032941074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.033843286,0.0017699942,0.092591226,0.0022954035,0.023618713,0.00057090324,0.00094655075,0.7287297,0.0015363786,0.041946456,0.002206542,0.06994488],"study_design_scores_gemma":[0.007859149,0.011496003,0.016436035,0.00044548936,0.0068618506,0.00021988188,0.00024148957,0.8918184,0.0019031562,0.059403904,0.0031669864,0.00014767564],"about_ca_topic_score_codex":0.0029404268,"about_ca_topic_score_gemma":0.0016823547,"teacher_disagreement_score":0.42627826,"about_ca_system_score_codex":0.0028658598,"about_ca_system_score_gemma":0.0041538663,"threshold_uncertainty_score":0.70750105},"labels":[],"label_agreement":null},{"id":"W1972605536","doi":"10.6000/1929-6029.2014.03.04.12","title":"Increasing Early Awareness of Hazard of Children with ADHD’s ODD and Aggression by Structural Equation Modeling (SEM)","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Attention Deficit Hyperactivity Disorder","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"CBCL; Aggression; Impulsivity; Psychology; Attention deficit hyperactivity disorder; Conduct disorder; Clinical psychology; Anxiety; Structural equation modeling; Psychiatry","score_opus":0.08119451542844504,"score_gpt":0.4497507356825175,"score_spread":0.3685562202540725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972605536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9245314,0.00070598396,0.06778083,0.0019222394,0.00012416864,0.00032378142,0.0016221193,0.00042095623,0.0025685614],"genre_scores_gemma":[0.96779466,0.00026942982,0.029994832,0.00010288942,0.000030023191,0.00039033373,0.0009369443,0.000035433462,0.00044546448],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9911885,0.0063855043,0.0004556026,0.0010011455,0.00066233135,0.00030694235],"domain_scores_gemma":[0.9655993,0.027399192,0.00329045,0.0018274473,0.0013608808,0.000522726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012817859,0.0011303595,0.0008307913,0.002414192,0.0008546228,0.0015337477,0.0010080818,0.0006471974,0.0024739054],"category_scores_gemma":[0.031061865,0.0006537336,0.0028215165,0.0021136748,0.0004856768,0.0013171554,0.0015785978,0.0019902948,0.00019872197],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009301163,0.00024642586,0.9686376,0.00013573516,0.0011540162,0.00010484931,0.001692872,0.0045276093,0.00018300487,0.0019208683,0.0013301932,0.019973814],"study_design_scores_gemma":[0.00009385454,0.0012213078,0.6976129,0.0006659909,0.0023776707,0.000532997,0.004470804,0.26973748,0.0014054939,0.015590805,0.006179941,0.00011081233],"about_ca_topic_score_codex":0.011846321,"about_ca_topic_score_gemma":0.016936868,"teacher_disagreement_score":0.012817859,"about_ca_system_score_codex":0.0013688522,"about_ca_system_score_gemma":0.0035641093,"threshold_uncertainty_score":0.067788124},"labels":[],"label_agreement":null},{"id":"W1973688299","doi":"10.6000/1929-6029.2014.03.03.11","title":"A Simple Approach to Sample Size Calculation for Count Data in Matched Cohort Studies","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Research Resources; National Institutes of Health","keywords":"Overdispersion; Count data; Statistics; Poisson distribution; Sample size determination; Confounding; Poisson regression; Mathematics; Matching (statistics); Cohort; Zero-inflated model; Sample (material); Econometrics; Medicine; Population","score_opus":0.318769553307775,"score_gpt":0.5792475884654095,"score_spread":0.26047803515763446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973688299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085300853,0.00033627223,0.99433374,0.00044702465,0.00028470572,0.0026569895,0.00020877944,0.0002555025,0.0006240607],"genre_scores_gemma":[0.0148556875,0.00025601985,0.9740811,0.0004900823,0.00020617025,0.0091072135,0.00018174153,0.000100537254,0.0007214626],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90773165,0.06637136,0.0058870553,0.007170256,0.01225956,0.0005801279],"domain_scores_gemma":[0.9075662,0.07118847,0.003964925,0.011363682,0.0053412877,0.000575462],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10187251,0.0018540994,0.0031299074,0.00533323,0.0015572032,0.002720064,0.0053172214,0.005666463,0.009001076],"category_scores_gemma":[0.27258176,0.0016557178,0.0040492634,0.0052474816,0.0024937678,0.003103827,0.004188007,0.005820545,0.0019752204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089285144,0.000354463,0.0074767484,0.0028363655,0.002134807,0.0006531035,0.0013847685,0.021387592,0.0039630514,0.28022283,0.023445208,0.6552482],"study_design_scores_gemma":[0.0029515314,0.0018546993,0.008875308,0.0019429799,0.0013373137,0.0023621756,0.0003483332,0.1739485,0.007631456,0.67855036,0.119765356,0.00043206225],"about_ca_topic_score_codex":0.0018608458,"about_ca_topic_score_gemma":0.002075243,"teacher_disagreement_score":0.8981275,"about_ca_system_score_codex":0.001736831,"about_ca_system_score_gemma":0.0029982077,"threshold_uncertainty_score":0.53875977},"labels":[],"label_agreement":null},{"id":"W1975337807","doi":"10.6000/1929-6029.2012.01.02.04","title":"Generalized Augmentation for Control of the k-Familywise Error Rate","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"False discovery rate; Multiple comparisons problem; Statistical hypothesis testing; Null hypothesis; Benchmark (surveying); Null (SQL); Statistics; Set (abstract data type); Computer science; Alternative hypothesis; Algorithm; Mathematics; Artificial intelligence; Data mining; Biology","score_opus":0.6604030492929751,"score_gpt":0.6823768039884841,"score_spread":0.021973754695508996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975337807","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00463601,0.00023349035,0.9932336,0.00034626678,0.00017816451,0.00027138618,0.00010970045,0.000501806,0.00048948376],"genre_scores_gemma":[0.19289179,0.00033523905,0.79927206,0.000501339,0.00042861598,0.003999532,0.00026161395,0.00030890456,0.0020008516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86227787,0.114225075,0.0038981605,0.008698445,0.00930851,0.0015919564],"domain_scores_gemma":[0.4877058,0.40473908,0.017260116,0.07734813,0.011532572,0.0014143727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.15076502,0.0028408153,0.0050913794,0.0021291513,0.0020454554,0.0030551143,0.006789295,0.0061140764,0.006414191],"category_scores_gemma":[0.35227785,0.0014387552,0.004644878,0.0031607507,0.011388826,0.0063159685,0.0073090713,0.009830742,0.0012703169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003889725,0.00043629686,0.014693886,0.0014168485,0.0016481469,0.00150223,0.002456671,0.17472668,0.011004168,0.52774066,0.0073903427,0.25309435],"study_design_scores_gemma":[0.0003852796,0.0010907986,0.00447999,0.00029836193,0.00039289184,0.0007721153,0.000134107,0.698029,0.009695558,0.27452,0.0099484045,0.00025357038],"about_ca_topic_score_codex":0.001689343,"about_ca_topic_score_gemma":0.0013078762,"teacher_disagreement_score":0.15076502,"about_ca_system_score_codex":0.0027427764,"about_ca_system_score_gemma":0.0042328355,"threshold_uncertainty_score":0.79733115},"labels":[],"label_agreement":null},{"id":"W1977200598","doi":"10.6000/1929-6029.2012.01.02.01","title":"Estimating the Complier Average Causal Effect for Exponential Survival in the Presence of Mid-Trial Switching","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Counterfactual thinking; Estimator; Econometrics; Treatment effect; Delta method; Aggregate (composite); Statistics; Economics; Computer science; Mathematics; Psychology; Medicine; Social psychology","score_opus":0.25044803211524236,"score_gpt":0.5602161870279413,"score_spread":0.30976815491269893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977200598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04126619,0.00084331376,0.9547841,0.00075313664,0.00009684498,0.0005276779,0.0001915882,0.00027481667,0.0012623176],"genre_scores_gemma":[0.60352,0.00081876956,0.39004433,0.0011282555,0.00025055133,0.0017007156,0.0005573994,0.000116667245,0.0018632744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.867619,0.11656214,0.0036960442,0.0063608633,0.0046596806,0.001102371],"domain_scores_gemma":[0.4663152,0.47892046,0.021283198,0.029304385,0.0033378007,0.00083901256],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22666231,0.0010805159,0.003418424,0.0024862115,0.0007448143,0.0028057685,0.0029208031,0.0034756798,0.003880213],"category_scores_gemma":[0.4054307,0.0010204065,0.004716446,0.0021031809,0.0035831495,0.003889138,0.003038819,0.0044930335,0.00044507714],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049132546,0.00068681716,0.0722191,0.002357856,0.0059781303,0.0013740123,0.0031198768,0.16262402,0.0021754832,0.4339933,0.0030463105,0.3075118],"study_design_scores_gemma":[0.0008662608,0.002404455,0.016890556,0.00041739238,0.0020060376,0.00083521754,0.00027698593,0.53650373,0.0027140654,0.43192843,0.004985086,0.0001717876],"about_ca_topic_score_codex":0.0016139968,"about_ca_topic_score_gemma":0.0012806894,"teacher_disagreement_score":0.22666231,"about_ca_system_score_codex":0.0013412131,"about_ca_system_score_gemma":0.0022201901,"threshold_uncertainty_score":0.95366305},"labels":[],"label_agreement":null},{"id":"W1979528456","doi":"10.6000/1929-6029.2013.02.01.07","title":"Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Overcrowding; Logistic regression; Triage; Emergency department; Medicine; Emergency medicine; Cohort; Hospital admission; Regression analysis; Medical emergency; Statistics; Internal medicine","score_opus":0.06603192859949607,"score_gpt":0.4298231758801462,"score_spread":0.3637912472806501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979528456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.861046,0.0005521654,0.12828219,0.0011331015,0.00023496721,0.0010234255,0.0035484761,0.001334892,0.0028448259],"genre_scores_gemma":[0.94084036,0.00019255547,0.05209901,0.000122897,0.000050891107,0.00064286607,0.005293627,0.00006178,0.0006960571],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956456,0.0025212357,0.0003908018,0.0006776505,0.00048227192,0.00028247302],"domain_scores_gemma":[0.96800184,0.024277909,0.0016733522,0.0009939367,0.0045272205,0.00052579644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017930565,0.0020427404,0.0011080855,0.0028040453,0.00065174076,0.0017781081,0.0019669929,0.0010593897,0.0014453421],"category_scores_gemma":[0.035248995,0.00066227594,0.0017813314,0.0011915631,0.00041619482,0.0011394249,0.0015065272,0.0020226086,0.00071444496],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001163711,0.0017984519,0.53255236,0.00018129645,0.001015151,0.0003211358,0.0003183843,0.3513128,0.0008870084,0.0010165722,0.0065328497,0.10290022],"study_design_scores_gemma":[0.000069694914,0.00028969898,0.02257612,0.000057709454,0.00011107166,0.00006337902,0.00008601143,0.9748457,0.00060359197,0.00076106226,0.00051581324,0.000020178484],"about_ca_topic_score_codex":0.013168491,"about_ca_topic_score_gemma":0.0069035413,"teacher_disagreement_score":0.017930565,"about_ca_system_score_codex":0.0015433456,"about_ca_system_score_gemma":0.0032424354,"threshold_uncertainty_score":0.094827056},"labels":[],"label_agreement":null},{"id":"W1980915258","doi":"10.6000/1929-6029.2014.03.02.4","title":"A Bayesian Shared Parameter Model for Analysing Longitudinal Skewed Responses with Nonignorable Dropout","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Skew; Skewness; Markov chain Monte Carlo; Deviance information criterion; Missing data; Deviance (statistics); Bayesian probability; Computer science; Random effects model; Statistics; Data set; Dropout (neural networks); Mixed model; Econometrics; Mathematics; Machine learning","score_opus":0.18773300399977777,"score_gpt":0.521479591404308,"score_spread":0.3337465874045302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980915258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010165461,0.0003662144,0.9876336,0.00039815862,0.000051834995,0.00020350795,0.0004046162,0.00018230126,0.0005943663],"genre_scores_gemma":[0.37715015,0.0018234159,0.60351324,0.00089789723,0.00034752177,0.003755244,0.0026405747,0.00024331902,0.009628655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98389983,0.011284446,0.0005967405,0.0022033663,0.0014141245,0.00060147216],"domain_scores_gemma":[0.9664257,0.026304014,0.002120072,0.002557856,0.0020671308,0.00052527763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035179485,0.0016501271,0.00386046,0.0023523443,0.0010477528,0.0021991082,0.005737891,0.0038457743,0.005441587],"category_scores_gemma":[0.06307007,0.0014172541,0.00299036,0.003012786,0.0027099433,0.003844921,0.0030062487,0.004036676,0.0011050159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013981871,0.00044857257,0.016596517,0.00088764477,0.0012286457,0.001061019,0.0017643037,0.40240172,0.0033890528,0.36295125,0.005629301,0.20224378],"study_design_scores_gemma":[0.00017797116,0.00033312643,0.003269154,0.0001646699,0.00031351778,0.00028972904,0.00014764292,0.83083624,0.00063498045,0.15977894,0.0039435537,0.000110428526],"about_ca_topic_score_codex":0.007513809,"about_ca_topic_score_gemma":0.0058881682,"teacher_disagreement_score":0.035179485,"about_ca_system_score_codex":0.0017966758,"about_ca_system_score_gemma":0.0038670923,"threshold_uncertainty_score":0.1860491},"labels":[],"label_agreement":null},{"id":"W1981620646","doi":"10.6000/1929-6029.2013.02.02.04","title":"Comparison of Post Hoc Multiple Pairwise Testing Procedures as Applied to Small k-Group Logrank Tests","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health","keywords":"Bonferroni correction; Multiple comparisons problem; Pairwise comparison; Post-hoc analysis; Post hoc; False discovery rate; Statistics; Type I and type II errors; Log-rank test; Statistical hypothesis testing; Mathematics; Censoring (clinical trials); Computer science; Medicine; Survival analysis; Internal medicine; Biology","score_opus":0.302127508600054,"score_gpt":0.5532020893034955,"score_spread":0.25107458070344146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981620646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06220643,0.0003618494,0.9295013,0.00040251447,0.00052768365,0.0027683736,0.00040925754,0.001029337,0.0027933463],"genre_scores_gemma":[0.28635314,0.00019000378,0.704615,0.00024728224,0.000107213906,0.0068797753,0.0002922031,0.00041379192,0.0009015183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8051486,0.17247267,0.0041400497,0.0064792777,0.01040033,0.0013591119],"domain_scores_gemma":[0.42054722,0.5268731,0.012099343,0.028897146,0.010093744,0.0014895197],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14203994,0.0015062583,0.0025586782,0.002661958,0.0017535357,0.0023598257,0.0033928778,0.0016405422,0.007682891],"category_scores_gemma":[0.45768026,0.00078249676,0.0030296766,0.0028481486,0.0040039103,0.0042330865,0.002662391,0.0041603334,0.00076133304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.023663811,0.0022498076,0.029650599,0.0034134302,0.0075485567,0.0012853624,0.0060968366,0.06943594,0.013796531,0.18210682,0.014864138,0.6458881],"study_design_scores_gemma":[0.003668732,0.037457958,0.05165072,0.0010316178,0.0025586316,0.0015452575,0.0030307767,0.4971972,0.039883517,0.33169714,0.029504614,0.0007738259],"about_ca_topic_score_codex":0.0009782908,"about_ca_topic_score_gemma":0.0011221777,"teacher_disagreement_score":0.85796005,"about_ca_system_score_codex":0.0017762394,"about_ca_system_score_gemma":0.004031354,"threshold_uncertainty_score":0.751188},"labels":[],"label_agreement":null},{"id":"W1985076143","doi":"10.6000/1929-6029.2014.03.03.6","title":"Research Article: Survival Analysis of Under Five Mortality in Rural Parts of Ethiopia","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Demography; Proportional hazards model; Child mortality; Socioeconomic status; Covariate; Medicine; Survival analysis; Context (archaeology); Population; Infant mortality; Mortality rate; Survival function; Geography; Statistics; Environmental health; Mathematics","score_opus":0.11044906847435074,"score_gpt":0.5332120894064045,"score_spread":0.42276302093205376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985076143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965724,0.0003624462,0.0008937093,0.00008819252,0.000013055587,0.000018484552,0.0014444941,0.000006329269,0.00060087297],"genre_scores_gemma":[0.9977933,0.00033785534,0.00048647824,0.000018887687,0.0000120146215,0.000019795918,0.00082535326,0.0000025334134,0.0005037891],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997749,0.000095941665,0.000022155187,0.000030325236,0.00002877303,0.00004793244],"domain_scores_gemma":[0.99882156,0.000571524,0.00030282472,0.000053193,0.0001559727,0.00009487616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007338955,0.0001508311,0.00017922251,0.0010448213,0.00022077428,0.00034737063,0.00014820021,0.00014838275,0.0017758083],"category_scores_gemma":[0.0018890711,0.00005087066,0.00037730363,0.0010223615,0.00009877817,0.00019716828,0.00022799936,0.00025707434,0.00017532145],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085017056,0.00017074365,0.95843583,0.00018597824,0.0002036563,0.00079181493,0.00069635676,0.0048606466,0.0009002317,0.0005560127,0.0012447402,0.031103762],"study_design_scores_gemma":[0.000019455081,0.000527093,0.9834299,0.000093994735,0.000113424605,0.0013732725,0.0020049934,0.007869235,0.0006660609,0.0005275421,0.0033521426,0.000022829548],"about_ca_topic_score_codex":0.0035242473,"about_ca_topic_score_gemma":0.0026189499,"teacher_disagreement_score":0.0035242473,"about_ca_system_score_codex":0.00025371453,"about_ca_system_score_gemma":0.00041413208,"threshold_uncertainty_score":0.0070074797},"labels":[],"label_agreement":null},{"id":"W1987360072","doi":"10.6000/1929-6029.2014.03.04.6","title":"Imputation of Missing Data for a Continuous Variable with an Ordinal form of Risk Function: When to Apply the Transformation?","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Ordinal regression; Ordinal data; Polytomous Rasch model; Mathematics; Logistic regression; Econometrics; Ordered logit; Regression analysis; Item response theory; Psychometrics","score_opus":0.06278040116163851,"score_gpt":0.42978702840040744,"score_spread":0.3670066272387689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987360072","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047641013,0.015782712,0.8987901,0.02900785,0.002114791,0.0014704141,0.0010622009,0.0008794725,0.003251435],"genre_scores_gemma":[0.2980153,0.010731886,0.6759106,0.005763153,0.0026009423,0.0034134188,0.0011362518,0.00057893054,0.0018495237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91967285,0.06933178,0.0036485225,0.003036579,0.003752182,0.00055811],"domain_scores_gemma":[0.85559523,0.11076535,0.014697949,0.011165717,0.006877214,0.00089857867],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0899721,0.0007427138,0.0017807515,0.0011100601,0.0005124862,0.0018374764,0.002413317,0.001701581,0.0066689854],"category_scores_gemma":[0.20347941,0.0005115211,0.0016082911,0.0027464726,0.0020089478,0.0026594794,0.0016284339,0.0029367134,0.002098082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003322865,0.00036824986,0.048578944,0.0057726027,0.0010578766,0.0004510248,0.0019688797,0.005393121,0.0020791274,0.031782612,0.03274855,0.86647624],"study_design_scores_gemma":[0.0029901743,0.0074226256,0.14129628,0.025403704,0.0021526997,0.006960235,0.0031818221,0.12939118,0.017385162,0.4799314,0.18263838,0.0012463159],"about_ca_topic_score_codex":0.00069001026,"about_ca_topic_score_gemma":0.0006547188,"teacher_disagreement_score":0.9100279,"about_ca_system_score_codex":0.00062307494,"about_ca_system_score_gemma":0.0015526467,"threshold_uncertainty_score":0.47582364},"labels":[],"label_agreement":null},{"id":"W1993456571","doi":"10.6000/1929-6029.2014.03.03.3","title":"Comparative Risk-Benefit Analysis of Different Classes of Biologic Agents in Patients with Psoriasis: A Case Study on the Pros and Cons of Mixed Treatment Comparison in Synthesizing Complex Evidence Networks","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Psoriasis: Treatment and Pathogenesis","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psoriatic arthritis; Medicine; Psoriasis; Placebo; Adverse effect; Internal medicine; Randomization; Biologic Agents; Ustekinumab; Arthritis; Rheumatology; TNF inhibitor; Randomized controlled trial; Tumor necrosis factor alpha; Immunology; Alternative medicine; Etanercept; Disease; Infliximab; Pathology","score_opus":0.18773638305101512,"score_gpt":0.4383742288688274,"score_spread":0.25063784581781223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993456571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20237258,0.3488076,0.40547368,0.010163519,0.001346362,0.016003039,0.005124807,0.0003934776,0.010314936],"genre_scores_gemma":[0.8059526,0.026710648,0.15427776,0.0016603766,0.00032757863,0.008928762,0.0014822328,0.00008550322,0.0005744322],"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6854678,0.2885445,0.014754719,0.005361387,0.005248215,0.00062333577],"domain_scores_gemma":[0.4219865,0.5499935,0.013587929,0.009241482,0.0046166233,0.0005740052],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19315735,0.0019147951,0.0056076082,0.005606218,0.000836176,0.00394382,0.0022120671,0.003878323,0.005744364],"category_scores_gemma":[0.38443944,0.0013510449,0.018088,0.0037695495,0.0013409595,0.0044891112,0.0023861977,0.0023394243,0.00032947105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.08227873,0.0012383099,0.04249242,0.11007273,0.35129228,0.0018465269,0.0020448978,0.07097761,0.002140662,0.03866453,0.0036666687,0.29328468],"study_design_scores_gemma":[0.031589568,0.013203117,0.022200214,0.037250254,0.5738308,0.0020435837,0.001375669,0.15785286,0.0039433315,0.13487756,0.02123823,0.00059490936],"about_ca_topic_score_codex":0.0017586915,"about_ca_topic_score_gemma":0.001920226,"teacher_disagreement_score":0.8068427,"about_ca_system_score_codex":0.0037561057,"about_ca_system_score_gemma":0.0024193067,"threshold_uncertainty_score":0.99498063},"labels":[],"label_agreement":null},{"id":"W1999469479","doi":"10.6000/1929-6029.2014.03.04.4","title":"Estimating the Population Standard Deviation with Confidence Interval: A Simulation Study under Skewed and Symmetric Conditions","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidence interval; Estimator; Statistics; Standard deviation; Coverage probability; Range (aeronautics); Interval estimation; Interval (graph theory); Mathematics; Sample size determination; Population; CDF-based nonparametric confidence interval; Robust confidence intervals; Population mean; Medicine; Engineering","score_opus":0.13182437283567777,"score_gpt":0.537956396631617,"score_spread":0.4061320237959392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999469479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7404907,0.0016634833,0.25236806,0.00070287817,0.00008129983,0.00064301805,0.00050813804,0.0001694691,0.0033730415],"genre_scores_gemma":[0.9043243,0.0006025663,0.093836054,0.00006574839,0.000025286885,0.0003877977,0.00042094622,0.00003209076,0.00030516458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9854917,0.011521119,0.0004178229,0.00072949124,0.0014689096,0.00037086155],"domain_scores_gemma":[0.7591379,0.21982972,0.006459918,0.0067085233,0.0069410997,0.0009228518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033236828,0.0008196743,0.0012257419,0.0024514964,0.00070177735,0.0013019355,0.001718325,0.0018069973,0.0012662477],"category_scores_gemma":[0.111676455,0.00042732622,0.0013269678,0.002660065,0.0015070488,0.0023942522,0.0017408321,0.0019900647,0.00014958059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011551948,0.0006182522,0.03607649,0.00045187282,0.00044412076,0.00065375405,0.00070993236,0.8856727,0.001068297,0.03455438,0.0014457249,0.037149336],"study_design_scores_gemma":[0.00023940744,0.0005504864,0.004438,0.0000958012,0.00012036508,0.00028135796,0.0003027317,0.98401546,0.0010595548,0.008294292,0.0005444465,0.000058046604],"about_ca_topic_score_codex":0.007055008,"about_ca_topic_score_gemma":0.0034427093,"teacher_disagreement_score":0.033236828,"about_ca_system_score_codex":0.0013152901,"about_ca_system_score_gemma":0.001452454,"threshold_uncertainty_score":0.17577529},"labels":[],"label_agreement":null},{"id":"W2000836571","doi":"10.6000/1929-6029.2015.04.01.10","title":"Inferential Procedures for Comparing the Accuracy and Intrinsic Measures of Multivariate Receiver Operating Characteristic (MROC) Curve","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Grants Commission; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Receiver operating characteristic; Multivariate statistics; Sensitivity (control systems); Multivariate analysis; Measure (data warehouse); Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Computer science; Data mining","score_opus":0.19233957179186317,"score_gpt":0.44406478633007207,"score_spread":0.2517252145382089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000836571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011367035,0.0015758584,0.9834208,0.0003176774,0.00010544527,0.000500779,0.0010150757,0.0006957231,0.0010015987],"genre_scores_gemma":[0.23424599,0.0014740006,0.7571922,0.00036046302,0.00039777491,0.003650172,0.0020320453,0.00020061228,0.0004468848],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93075,0.048633337,0.0053169006,0.0053248825,0.009392744,0.00058208214],"domain_scores_gemma":[0.67105794,0.29434636,0.014840046,0.012105345,0.007007035,0.00064328045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.053681664,0.0024307822,0.0033074913,0.011577254,0.0010013182,0.0029852039,0.0026283944,0.0024077937,0.0028840576],"category_scores_gemma":[0.2266727,0.0005553532,0.0028602702,0.0070040645,0.0028698388,0.003221665,0.0024986335,0.004168516,0.000718462],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010476481,0.000685429,0.062885016,0.003699007,0.003799423,0.000747175,0.0014022967,0.13933223,0.0051936363,0.12776306,0.00852606,0.64491904],"study_design_scores_gemma":[0.00016017194,0.001947687,0.028735315,0.00093194674,0.0010407405,0.0012059321,0.0009750785,0.7072138,0.009536589,0.23425849,0.013587399,0.00040683328],"about_ca_topic_score_codex":0.0012750026,"about_ca_topic_score_gemma":0.00089085894,"teacher_disagreement_score":0.053681664,"about_ca_system_score_codex":0.001505595,"about_ca_system_score_gemma":0.0024112174,"threshold_uncertainty_score":0.28389913},"labels":[],"label_agreement":null},{"id":"W2001949697","doi":"10.6000/1929-6029.2013.02.03.1","title":"A Comparative Economic Analysis of Immunization Programs for Pertussis and Measles: The Use of ARIMA Model to Study the Epidemiological Situation in England and Wales","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Measles; Autoregressive integrated moving average; Epidemiology; Immunization; Environmental health; Medicine; Virology; Statistics; Time series; Vaccination; Immunology; Mathematics","score_opus":0.3314323678360809,"score_gpt":0.5137474647855205,"score_spread":0.1823150969494396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001949697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9826309,0.0023283726,0.009448548,0.00050145667,0.00004916559,0.0002255601,0.00060520286,0.000023106317,0.004187591],"genre_scores_gemma":[0.99391514,0.0007637096,0.0032541745,0.000046294303,0.00001969631,0.00019116674,0.00041689476,0.0000070941414,0.0013857835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966648,0.0025674284,0.000088774796,0.00015049273,0.00022930797,0.00029915269],"domain_scores_gemma":[0.9921761,0.0063781296,0.0005394449,0.0001639293,0.000507702,0.0002347467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068923295,0.0006985043,0.0015730619,0.0025341457,0.00036212266,0.0010982722,0.00085877214,0.0008714711,0.0033943357],"category_scores_gemma":[0.015495361,0.0004806396,0.0023954166,0.0013213331,0.00042149474,0.0011941554,0.0011881029,0.00082415814,0.00013831144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003861144,0.0008487504,0.06252275,0.0007886384,0.0021482306,0.0006515662,0.00017618471,0.8875074,0.0009422491,0.01730012,0.0012365308,0.022016454],"study_design_scores_gemma":[0.0005399697,0.002510227,0.049333654,0.00014898602,0.001097497,0.0002174044,0.00042818798,0.9403084,0.00037708398,0.0036335737,0.0013361365,0.000068962974],"about_ca_topic_score_codex":0.032995995,"about_ca_topic_score_gemma":0.014411233,"teacher_disagreement_score":0.032995995,"about_ca_system_score_codex":0.005174084,"about_ca_system_score_gemma":0.0019725412,"threshold_uncertainty_score":0.065607905},"labels":[],"label_agreement":null},{"id":"W2005234338","doi":"10.6000/1929-6029.2014.03.01.5","title":"A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Covariate; Bayesian probability; Proportional hazards model; Poisson distribution; Statistics; Computer science; Robustness (evolution); Confidence interval; Mathematics","score_opus":0.43149752995332713,"score_gpt":0.598887335163738,"score_spread":0.1673898052104109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005234338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009760159,0.00018945253,0.998005,0.00031136608,0.00003103899,0.00004657936,0.0000709267,0.00006339948,0.00030629276],"genre_scores_gemma":[0.11969455,0.0018656392,0.86906415,0.0006858422,0.00059086713,0.0014766206,0.0007355276,0.00018168938,0.0057051],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9880676,0.008230827,0.00040649946,0.0013327226,0.001626098,0.00033633],"domain_scores_gemma":[0.97956127,0.016913746,0.0010051398,0.0009274055,0.0012874963,0.00030487624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02573941,0.0013827822,0.0023681498,0.0020639817,0.0009345645,0.0024165853,0.004637928,0.0023741752,0.0049274852],"category_scores_gemma":[0.04627407,0.0014635866,0.002424905,0.002257087,0.0021304574,0.0030933644,0.0027800102,0.0054331417,0.0010591263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021650318,0.00012720928,0.0027817048,0.00041311316,0.00036161274,0.00039524894,0.00048274238,0.34436753,0.0011156416,0.53821856,0.0053330017,0.10618706],"study_design_scores_gemma":[0.000091744994,0.00008700128,0.00050847896,0.00007229132,0.000094403156,0.00019397183,0.00004160691,0.75713646,0.00028729666,0.2354697,0.0059576505,0.000059356265],"about_ca_topic_score_codex":0.008080094,"about_ca_topic_score_gemma":0.00695557,"teacher_disagreement_score":0.02573941,"about_ca_system_score_codex":0.0021267515,"about_ca_system_score_gemma":0.0045358324,"threshold_uncertainty_score":0.13612467},"labels":[],"label_agreement":null},{"id":"W2011282534","doi":"10.6000/1929-6029.2014.03.03.8","title":"Total Hip and Knee Replacement in Eastern Libya: A Post-Conflict Case Series","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Orthopedic surgery; Osteoarthritis; Incidence (geometry); Total hip replacement; Complication; Arthroplasty; Total knee replacement; Surgery; Joint replacement; Knee replacement; Patient satisfaction; Physical therapy","score_opus":0.04343963066075726,"score_gpt":0.4129519446213489,"score_spread":0.3695123139605916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011282534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905235,0.004449935,0.00040927398,0.000987014,0.00014809117,0.00006659757,0.000089824825,0.000018931638,0.0033067928],"genre_scores_gemma":[0.9974239,0.001109097,0.00020281765,0.00034765215,0.00019140757,0.000013746795,0.000046345333,0.000005324848,0.00065966934],"study_design_codex":"case_report","study_design_gemma":"case_report","domain_scores_codex":[0.99917233,0.00011134904,0.00013572472,0.00014045506,0.00013740268,0.0003027768],"domain_scores_gemma":[0.998938,0.00016422209,0.00039115964,0.000056832603,0.000062641906,0.00038706732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037725506,0.0011837403,0.0007496272,0.0020271286,0.003825223,0.0014154736,0.0010490218,0.0023242647,0.0037036932],"category_scores_gemma":[0.0016771724,0.0008715667,0.0006038725,0.002316811,0.0015787338,0.0013767639,0.0018495288,0.0016182407,0.0006315567],"study_design_candidate":"case_report","study_design_consensus":"case_report","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027586702,0.00007940096,0.046426997,0.00007131296,0.000024224471,0.9502926,0.00087004324,0.00004986815,0.00025823942,0.000120608034,0.00034169687,0.0014373702],"study_design_scores_gemma":[0.000008422529,0.000069254726,0.024387272,0.000041957628,0.000015365411,0.97310984,0.001474675,0.0000861912,0.000093902105,0.00010083097,0.00060012896,0.000012133683],"about_ca_topic_score_codex":0.0036075844,"about_ca_topic_score_gemma":0.0063203783,"teacher_disagreement_score":0.003825223,"about_ca_system_score_codex":0.0013861537,"about_ca_system_score_gemma":0.0010931877,"threshold_uncertainty_score":0.012390077},"labels":[],"label_agreement":null},{"id":"W2011977356","doi":"10.6000/1929-6029.2013.02.02.05","title":"Applying Mixed-Effects Location Scale Modeling to Examine Within-Person Variability in Physical Activity Self-Efficacy","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Self-efficacy; Physical activity; Scale (ratio); Psychology; Mixed model; Construct (python library); Multilevel modelling; Multilevel model; Demography; Medicine; Physical therapy; Statistics; Social psychology; Geography; Computer science; Mathematics","score_opus":0.1290864398610911,"score_gpt":0.514346299280857,"score_spread":0.3852598594197658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011977356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46635744,0.0008300768,0.5226353,0.00041353764,0.0005594529,0.0032361378,0.0028499449,0.0016893257,0.0014288604],"genre_scores_gemma":[0.7649533,0.00021405534,0.22114654,0.00017201075,0.000086151376,0.0098984605,0.0017595836,0.00016556948,0.0016043008],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96313983,0.02980066,0.0011573983,0.004113375,0.0012850136,0.00050369464],"domain_scores_gemma":[0.9244103,0.06262007,0.0034188402,0.006797082,0.0023673,0.00038651173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038193036,0.0020480314,0.0022538365,0.0023572552,0.0010093864,0.0021203756,0.0031335887,0.0018367653,0.0056329546],"category_scores_gemma":[0.085926495,0.0010904507,0.008147809,0.0024456228,0.0008507809,0.0013421528,0.0022466253,0.0022274165,0.00082179176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009085862,0.0036929615,0.5010518,0.0016666934,0.031066922,0.0011840365,0.006818769,0.25177154,0.0040861745,0.020537442,0.0071037146,0.16193412],"study_design_scores_gemma":[0.0008796526,0.005570025,0.064513065,0.00021623423,0.0033863005,0.00030069577,0.0014208239,0.90338314,0.0018656286,0.012112142,0.006139291,0.00021304228],"about_ca_topic_score_codex":0.013266721,"about_ca_topic_score_gemma":0.009056612,"teacher_disagreement_score":0.038193036,"about_ca_system_score_codex":0.001241367,"about_ca_system_score_gemma":0.0014902147,"threshold_uncertainty_score":0.20198649},"labels":[],"label_agreement":null},{"id":"W2019234839","doi":"10.6000/1929-6029.2014.03.02.3","title":"Deployment of Six Sigma Methodology in Pars Plana Vitrectomy","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Retinal and Macular Surgery","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pars plana; Vitrectomy; DMAIC; Six Sigma; Medicine; Complication; Surgery; Ophthalmology; Operations management; Visual acuity; Engineering","score_opus":0.18689692416923992,"score_gpt":0.517804820278746,"score_spread":0.33090789610950605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019234839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.877537,0.007879377,0.10013783,0.0020575528,0.0002225921,0.0018447876,0.0005871569,0.00043141283,0.009302166],"genre_scores_gemma":[0.93104726,0.0016850855,0.065933704,0.00011924889,0.000037033962,0.00033903576,0.00024828393,0.000013925176,0.0005763184],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9901029,0.004799205,0.0010001361,0.00064670335,0.0031279258,0.00032319245],"domain_scores_gemma":[0.98843586,0.0036564902,0.0033735,0.00061131996,0.003525055,0.00039777052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077590565,0.00056278607,0.00043520494,0.003822849,0.0007913494,0.0015359919,0.00062520214,0.00040057136,0.0010429432],"category_scores_gemma":[0.011047305,0.00027100396,0.0007830518,0.0026892102,0.0007073919,0.000665585,0.0008924238,0.0005197842,0.00019026123],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001013438,0.00096465193,0.2794103,0.0023588436,0.00026700905,0.00031665227,0.004698698,0.009371711,0.0053692097,0.003106851,0.0018961234,0.6912264],"study_design_scores_gemma":[0.0004572242,0.016864065,0.8058906,0.0033397847,0.00093506335,0.0020005046,0.021370891,0.063397855,0.034429133,0.015499614,0.03540079,0.00041452068],"about_ca_topic_score_codex":0.003824647,"about_ca_topic_score_gemma":0.0034976434,"teacher_disagreement_score":0.0077590565,"about_ca_system_score_codex":0.0021642242,"about_ca_system_score_gemma":0.0049436376,"threshold_uncertainty_score":0.04103428},"labels":[],"label_agreement":null},{"id":"W2019809184","doi":"10.6000/1929-6029.2014.03.02.2","title":"The Bivariate Erlang and its Application in Modeling Recurrence Times of Kidney Dialysis Data","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bivariate analysis; Erlang (programming language); Exponential function; Exponential distribution; Mathematics; Marginal distribution; Statistics; Applied mathematics; Computer science; Random variable; Mathematical analysis; Theoretical computer science","score_opus":0.1898892495168076,"score_gpt":0.5180767990388435,"score_spread":0.3281875495220359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019809184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0511146,0.0009011986,0.9449198,0.0005584807,0.000072720155,0.000054441978,0.00038899176,0.0003104264,0.0016792845],"genre_scores_gemma":[0.874941,0.0023811806,0.11805485,0.00018463223,0.00019551438,0.00021444957,0.0007148476,0.00012895394,0.0031845672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978308,0.0012553153,0.00013487984,0.00033104973,0.00024121228,0.00020667783],"domain_scores_gemma":[0.9862613,0.010875652,0.0011612931,0.00080294604,0.00058357994,0.00031525487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007799875,0.00071251835,0.0010040748,0.0022764965,0.00089907245,0.0018677892,0.0014896175,0.001147465,0.002320186],"category_scores_gemma":[0.030304734,0.00048493137,0.0016352347,0.0035501602,0.0012175234,0.0020589856,0.0017558034,0.0019250847,0.00045420422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020411857,0.000096195334,0.031596143,0.0001658746,0.00022840912,0.00035670228,0.00053999486,0.66765773,0.00071080326,0.2364508,0.0018358205,0.060157377],"study_design_scores_gemma":[0.00000807931,0.000048840273,0.0031272937,0.0000330271,0.00004846311,0.00020308315,0.00010605282,0.90197843,0.0001791027,0.092574045,0.0016563867,0.00003713776],"about_ca_topic_score_codex":0.009568546,"about_ca_topic_score_gemma":0.0061726826,"teacher_disagreement_score":0.009568546,"about_ca_system_score_codex":0.0013534055,"about_ca_system_score_gemma":0.0014646783,"threshold_uncertainty_score":0.04125017},"labels":[],"label_agreement":null},{"id":"W2020401994","doi":"10.6000/1929-6029.2013.02.04.1","title":"Progression and Death as Competing Risks in Ovarian Cancer","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proportional hazards model; Ovarian cancer; Econometrics; Gray (unit); Complement (music); Statistics; Statistical model; Computer science; Medicine; Cancer; Mathematics; Internal medicine; Biology","score_opus":0.7015313884885316,"score_gpt":0.7235348038349219,"score_spread":0.022003415346390276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020401994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4379496,0.015233926,0.528373,0.0076181176,0.00044694086,0.0006755373,0.0014785912,0.00021360425,0.0080106715],"genre_scores_gemma":[0.9649848,0.0017214288,0.030480288,0.00040723124,0.00016150759,0.00043298263,0.0005317701,0.000035359844,0.0012445385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98226017,0.015543092,0.00030609372,0.0006196599,0.0009773856,0.00029362898],"domain_scores_gemma":[0.85804296,0.13313489,0.0047607124,0.0025109919,0.0008664703,0.00068397884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029748041,0.0007200701,0.0009974707,0.0015750214,0.0004655431,0.0020515898,0.0015941951,0.0015080774,0.002825003],"category_scores_gemma":[0.07184928,0.00033674808,0.0026561008,0.0014812802,0.0020035973,0.0018022406,0.002019039,0.0027004327,0.00014960341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029569822,0.00030187934,0.11391811,0.0022236102,0.0018688239,0.001576994,0.0022383344,0.3211821,0.00071438105,0.42070404,0.0051248814,0.12718986],"study_design_scores_gemma":[0.00022677281,0.00078647764,0.021322431,0.00032842066,0.0005376906,0.0018259542,0.00039505,0.4270991,0.0005504776,0.54218495,0.0046298173,0.00011284433],"about_ca_topic_score_codex":0.0014869963,"about_ca_topic_score_gemma":0.0015064799,"teacher_disagreement_score":0.029748041,"about_ca_system_score_codex":0.001434814,"about_ca_system_score_gemma":0.0016473023,"threshold_uncertainty_score":0.15732455},"labels":[],"label_agreement":null},{"id":"W2020755786","doi":"10.6000/1929-6029.2014.03.03.10","title":"The Current State of Validation of Administrative Healthcare Databases in Italy: A Systematic Review","year":2014,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Database; Scopus; Health care; MEDLINE; Medical diagnosis; Medicine; Epidemiology; Diagnosis code; Family medicine; Environmental health; Pathology; Computer science; Political science","score_opus":0.7176374467324228,"score_gpt":0.7330345204838055,"score_spread":0.015397073751382662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020755786","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040257866,0.9920266,0.0005853788,0.00095014315,0.00014498865,0.0004001013,0.0013149136,0.000015868349,0.00053620106],"genre_scores_gemma":[0.14278558,0.84146726,0.0055711544,0.0030638282,0.0004185556,0.0032769232,0.003272679,0.000045369074,0.00009865971],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.7814181,0.10074997,0.0795738,0.010354594,0.025852676,0.0020510105],"domain_scores_gemma":[0.33464223,0.5401805,0.0832837,0.013112223,0.027568465,0.0012129389],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14377047,0.0015664874,0.009449487,0.028173486,0.0012143627,0.009555911,0.005261786,0.003312054,0.003053996],"category_scores_gemma":[0.43341166,0.002230128,0.0067711873,0.03415728,0.0040770364,0.0077661932,0.0041958834,0.0014944659,0.0003437544],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002679108,0.000034991226,0.0122413235,0.9200746,0.0098386835,0.00011419628,0.0013487097,0.00015221545,0.00010323455,0.0009758149,0.0019439118,0.052904435],"study_design_scores_gemma":[0.00018750846,0.00016068593,0.020776704,0.9306231,0.026082082,0.00048649998,0.0017093725,0.00022750035,0.00026095487,0.0006379456,0.018775625,0.000072032395],"about_ca_topic_score_codex":0.010262183,"about_ca_topic_score_gemma":0.015875133,"teacher_disagreement_score":0.85622954,"about_ca_system_score_codex":0.009531979,"about_ca_system_score_gemma":0.026426304,"threshold_uncertainty_score":0.76034},"labels":[],"label_agreement":null},{"id":"W2021820291","doi":"10.6000/1929-6029.2013.02.01.01","title":"Use of Natriuretic Peptides as a Guidance for Treating Patients with Chronic Heart Failure: Unresolved Issues and Novel Insights","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Heart failure; Natriuretic peptide; Medicine; Adverse effect; Intensive care medicine; Internal medicine; Cardiology","score_opus":0.0492489963296377,"score_gpt":0.40172477598039175,"score_spread":0.35247577965075405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021820291","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011727406,0.8152325,0.0025300258,0.17383383,0.0047321273,0.000028490858,0.00009154006,0.000023618602,0.002355063],"genre_scores_gemma":[0.036863927,0.8565714,0.010552951,0.06462299,0.029592616,0.00017544416,0.00015927383,0.00005005626,0.0014114036],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9910987,0.0047645397,0.0014054045,0.00086701626,0.0016098773,0.00025442726],"domain_scores_gemma":[0.933008,0.05100458,0.0030484176,0.0016254133,0.010195832,0.0011177754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018188389,0.0006570103,0.0020080789,0.0014187119,0.00052918657,0.003550467,0.0021827528,0.0047841524,0.0028949962],"category_scores_gemma":[0.042596992,0.00028457737,0.0010984899,0.0014375274,0.0028113066,0.0055214465,0.0015358237,0.006092628,0.0010268302],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005635187,0.00023468184,0.0026092601,0.017286297,0.0003918097,0.00030989584,0.00066872616,0.00068025535,0.0006918983,0.039354537,0.062817454,0.87439173],"study_design_scores_gemma":[0.00041531687,0.001128803,0.008379265,0.053003505,0.0010513837,0.0015317439,0.002830777,0.0027555358,0.0009079915,0.10198683,0.8257693,0.00023958951],"about_ca_topic_score_codex":0.0020027442,"about_ca_topic_score_gemma":0.0028132095,"teacher_disagreement_score":0.018188389,"about_ca_system_score_codex":0.001983454,"about_ca_system_score_gemma":0.006329009,"threshold_uncertainty_score":0.09619051},"labels":[],"label_agreement":null},{"id":"W2022449543","doi":"10.6000/1929-6029.2015.04.01.4","title":"Establishing Reliability When Multiple Examiners Evaluate a Single Case-Part II: Applications to Symptoms of Post-Traumatic Stress Disorder (PTSD)","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nomothetic; Reliability (semiconductor); Nomothetic and idiographic; Psychology; Clinical psychology; Applied psychology; Scale (ratio); Social psychology","score_opus":0.31742517388876523,"score_gpt":0.5349617170035508,"score_spread":0.21753654311478554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022449543","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48777175,0.0022645458,0.48133963,0.00146084,0.0011369191,0.0047971285,0.0004793671,0.00034857623,0.020401157],"genre_scores_gemma":[0.79567957,0.0005276027,0.19750603,0.00030543847,0.000349171,0.0045821127,0.00027674908,0.00007142961,0.0007019456],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8176381,0.13945012,0.01456835,0.008833248,0.018526208,0.0009839552],"domain_scores_gemma":[0.6310087,0.21157685,0.039266363,0.048665818,0.06798827,0.0014939184],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.18540785,0.0007590211,0.0010303962,0.0038921358,0.002354113,0.0018861764,0.0012994844,0.0013226804,0.0009464546],"category_scores_gemma":[0.3516797,0.00085857045,0.0012206731,0.0030436823,0.003944485,0.001968967,0.004210449,0.0014669516,0.0006355355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010980099,0.00049621717,0.709286,0.001240831,0.0007794168,0.0003894541,0.024951747,0.0028835856,0.005945056,0.012415531,0.002499415,0.23801485],"study_design_scores_gemma":[0.00034763364,0.00747749,0.83005196,0.0013084658,0.0012890536,0.004245713,0.021447713,0.04613614,0.02443937,0.040178057,0.022719434,0.00035907121],"about_ca_topic_score_codex":0.0010300301,"about_ca_topic_score_gemma":0.0024583382,"teacher_disagreement_score":0.8145921,"about_ca_system_score_codex":0.0009182991,"about_ca_system_score_gemma":0.0021869089,"threshold_uncertainty_score":0.9805421},"labels":[],"label_agreement":null},{"id":"W2024255505","doi":"10.6000/1929-6029.2014.03.02.13","title":"Adjusting Complex Heterogeneity in Treatment Assignment in Observational Studies","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Pfizer","keywords":"Propensity score matching; Observational study; Estimator; Statistics; Cluster analysis; Weighting; Inverse probability weighting; Regression; Econometrics; Partial least squares regression; Inverse probability; Regression analysis; Mathematics; Computer science; Medicine","score_opus":0.7635707781039781,"score_gpt":0.6555082445573583,"score_spread":0.10806253354661977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024255505","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07731473,0.0019295643,0.9159342,0.0016056146,0.0002080651,0.0010543059,0.0005708875,0.0003142696,0.0010684988],"genre_scores_gemma":[0.7966609,0.0009358511,0.19835387,0.00077335356,0.00019356134,0.00117531,0.0007230762,0.00007986195,0.0011042238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90199816,0.08303126,0.0037433747,0.006287108,0.003933434,0.0010067185],"domain_scores_gemma":[0.6825175,0.26866412,0.024050055,0.020871887,0.0032657941,0.0006305654],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.11067636,0.00080741505,0.0019576512,0.0017775622,0.0012375101,0.002512443,0.0028770098,0.002223079,0.002849713],"category_scores_gemma":[0.2671361,0.0005663551,0.0037289506,0.003896388,0.002240835,0.0020698612,0.0022025881,0.002153332,0.00034127015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014310388,0.00048788852,0.15544923,0.0019857623,0.006298916,0.0015672327,0.0022259748,0.332089,0.0022753573,0.23930746,0.0060784,0.25080377],"study_design_scores_gemma":[0.0012927151,0.0008758025,0.035909686,0.0005050878,0.0023024427,0.0006866016,0.0005234845,0.5193001,0.002289517,0.42757338,0.008479255,0.0002618929],"about_ca_topic_score_codex":0.0051290747,"about_ca_topic_score_gemma":0.0036985346,"teacher_disagreement_score":0.88932365,"about_ca_system_score_codex":0.0017857748,"about_ca_system_score_gemma":0.0029487102,"threshold_uncertainty_score":0.5853195},"labels":[],"label_agreement":null},{"id":"W2025372553","doi":"10.6000/1929-6029.2014.03.01.8","title":"A Bayes Study of Bile Acid Constituents on Cholelithiasis and Carcinoma of the Gallbladder","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cholangiocarcinoma and Gallbladder Cancer Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chenodeoxycholic acid; Cholic acid; Deoxycholic acid; Odds ratio; Bile acid; Internal medicine; Gastroenterology; Lithocholic acid; Gallbladder; Logistic regression; Medicine","score_opus":0.04748872547998754,"score_gpt":0.40638187086131505,"score_spread":0.3588931453813275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025372553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99447215,0.0007082221,0.0037601215,0.00013019795,0.000020202053,0.00004776254,0.00007683284,0.000009208222,0.0007752874],"genre_scores_gemma":[0.9981117,0.00016016264,0.0013558319,0.000020459058,0.000026824126,0.000027720469,0.000054976794,0.0000033335552,0.00023898121],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964309,0.002591375,0.00016325286,0.00029472882,0.00037495606,0.00014475323],"domain_scores_gemma":[0.97734374,0.01920879,0.0016377972,0.0006196448,0.000552405,0.0006376266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00738868,0.0003586638,0.0006137283,0.001177427,0.00045588854,0.0006507371,0.00026206713,0.00044098363,0.0021278851],"category_scores_gemma":[0.026591573,0.00023801737,0.0009795331,0.00054200826,0.00069811795,0.00044744235,0.0007548102,0.00035230792,0.00019718296],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014931101,0.00015218847,0.9776498,0.000117772746,0.00056148117,0.00056093873,0.0006331554,0.00061265606,0.0008819387,0.0011598954,0.00016459626,0.016012387],"study_design_scores_gemma":[0.00028205104,0.0038900913,0.93641526,0.0002308898,0.001559904,0.0057566436,0.0014125651,0.03908748,0.0011463491,0.007935706,0.0022054606,0.0000775818],"about_ca_topic_score_codex":0.001561593,"about_ca_topic_score_gemma":0.00079417013,"teacher_disagreement_score":0.00738868,"about_ca_system_score_codex":0.000258779,"about_ca_system_score_gemma":0.00089218025,"threshold_uncertainty_score":0.039075553},"labels":[],"label_agreement":null},{"id":"W2027885631","doi":"10.6000/1929-6029.2013.02.04.2","title":"Searching for Stability as we Age: The PCA-Biplot Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Kingston General Hospital; Queen's University","funders":"","keywords":"Biplot; Principal component analysis; Linear discriminant analysis; Gait; Varimax rotation; Stability (learning theory); Pattern recognition (psychology); Mathematics; Discriminant function analysis; Multivariate statistics; Statistics; Cadence; Artificial intelligence; Multivariate analysis; Computer science; Medicine; Physical medicine and rehabilitation; Machine learning; Biology","score_opus":0.289654553963346,"score_gpt":0.5321805273564234,"score_spread":0.2425259733930774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027885631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09557234,0.0034555942,0.89343274,0.0006123913,0.0001891045,0.00025396753,0.0012481349,0.0025719733,0.002663799],"genre_scores_gemma":[0.50291693,0.0027852843,0.48909336,0.00019392204,0.00017096155,0.0004226209,0.0021679376,0.0004114943,0.0018374525],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985083,0.00055330363,0.000105427265,0.0003619334,0.00037229396,0.000098716504],"domain_scores_gemma":[0.9975969,0.000907285,0.00041349864,0.00024777488,0.0007104942,0.00012408759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003167862,0.0016787002,0.0012024925,0.007319788,0.0008360831,0.0021737474,0.0006974808,0.00063661457,0.0021587445],"category_scores_gemma":[0.007825057,0.0005243117,0.0011769207,0.00615305,0.00065235444,0.0013891667,0.0009906272,0.000985541,0.0009531756],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061250536,0.0003081795,0.07527587,0.0008967949,0.0012164477,0.0006444211,0.0019637498,0.025693826,0.019810686,0.009486475,0.012587857,0.85150325],"study_design_scores_gemma":[0.000109228,0.0012722942,0.3873673,0.00057254743,0.00081340404,0.0021608293,0.0031563565,0.48747548,0.009203464,0.06711057,0.040118217,0.0006402368],"about_ca_topic_score_codex":0.004524253,"about_ca_topic_score_gemma":0.0032165179,"teacher_disagreement_score":0.007319788,"about_ca_system_score_codex":0.00043202878,"about_ca_system_score_gemma":0.000847064,"threshold_uncertainty_score":0.016753495},"labels":[],"label_agreement":null},{"id":"W2031037082","doi":"10.6000/1929-6029.2014.03.02.8","title":"Efficient Blockwise Permutation Tests Preserving Exchangeability","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of General Medical Sciences; National Institute on Aging; National Institutes of Health; U.S. Social Security Administration","keywords":"Permutation (music); Statistic; Test statistic; Resampling; Set (abstract data type); Algorithm; Mathematics; Voxel; Data set; Random permutation; Computer science; Pattern recognition (psychology); Artificial intelligence; Block (permutation group theory); Statistical hypothesis testing; Data mining; Statistics; Combinatorics","score_opus":0.056245631371795404,"score_gpt":0.4453815832593215,"score_spread":0.3891359518875261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031037082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031308685,0.00009609913,0.9959584,0.00007484922,0.000030269573,0.00006276076,0.00007160788,0.00018710512,0.0003881179],"genre_scores_gemma":[0.16873764,0.00041281144,0.8261574,0.000283986,0.00029678913,0.0010719565,0.0009902968,0.00040749687,0.0016416901],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98479,0.010109399,0.00063829264,0.0014956646,0.0025535405,0.0004130925],"domain_scores_gemma":[0.9104553,0.07585065,0.0030378476,0.00667734,0.003373891,0.0006049815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016088134,0.0011445644,0.0024375813,0.00241914,0.0008192214,0.001776734,0.0027537122,0.0015657174,0.0050437823],"category_scores_gemma":[0.101990096,0.000770382,0.0013059612,0.002687804,0.002350797,0.0037104138,0.0023860808,0.0030420145,0.0017077577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011322503,0.00026004194,0.004438275,0.0005496033,0.00059024675,0.00061400776,0.00031683114,0.14046223,0.013341382,0.2739309,0.0045053763,0.55985874],"study_design_scores_gemma":[0.0001487958,0.00046583446,0.00230865,0.000057286623,0.00010833563,0.00042972792,0.00004981949,0.74001473,0.008088361,0.24319217,0.005046519,0.00008973325],"about_ca_topic_score_codex":0.00094115763,"about_ca_topic_score_gemma":0.0009732299,"teacher_disagreement_score":0.016088134,"about_ca_system_score_codex":0.0007413081,"about_ca_system_score_gemma":0.002855805,"threshold_uncertainty_score":0.08508319},"labels":[],"label_agreement":null},{"id":"W2031632210","doi":"10.6000/1929-6029.2014.03.02.6","title":"Adoption of Six Sigma’s DMAIC to Reduce Complications in IntraLase Surgeries","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Corneal surgery and disorders","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Six Sigma; Medicine; LASIK; DMAIC; Lean Six Sigma; Ishikawa diagram; Surgery; Operations management; Lean manufacturing; Engineering","score_opus":0.07348602610557503,"score_gpt":0.45845577926077363,"score_spread":0.3849697531551986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031632210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93790966,0.004828879,0.040561978,0.0040882565,0.00020605781,0.0011986033,0.00040662327,0.0008192705,0.009980587],"genre_scores_gemma":[0.9662104,0.0011112011,0.031271484,0.0002091587,0.000029726238,0.00018230954,0.00023919632,0.000017098511,0.00072937174],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99289674,0.0020325775,0.0008739152,0.00038542753,0.003406871,0.00040453096],"domain_scores_gemma":[0.9828706,0.0035701548,0.005440945,0.0008248559,0.006189411,0.0011040807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065689622,0.00054760353,0.0003691562,0.0027680434,0.00087604683,0.0016279201,0.0007120567,0.00040575382,0.0010740171],"category_scores_gemma":[0.011375225,0.00019901588,0.0005794084,0.0016350112,0.00050694024,0.0006158861,0.0011117752,0.00065903366,0.00017736133],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057466386,0.001419243,0.28059494,0.0020402553,0.00021760752,0.00020552291,0.0030648797,0.0054087485,0.008789125,0.0021101404,0.003486086,0.6920888],"study_design_scores_gemma":[0.00028386863,0.011264025,0.904222,0.002089506,0.00041261897,0.0009892131,0.009490997,0.015449671,0.021268537,0.0040621087,0.030260947,0.00020644694],"about_ca_topic_score_codex":0.0028599969,"about_ca_topic_score_gemma":0.003310984,"teacher_disagreement_score":0.0065689622,"about_ca_system_score_codex":0.002193262,"about_ca_system_score_gemma":0.0071581816,"threshold_uncertainty_score":0.034740448},"labels":[],"label_agreement":null},{"id":"W2033253137","doi":"10.6000/1929-6029.2015.04.01.7","title":"An Exponential Melanoma Trend Model","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Melanoma; Population; Exponential function; Demography; Exponential growth; Lung cancer; Statistics; Mathematics; Medicine; Oncology; Cancer research","score_opus":0.18142898263057758,"score_gpt":0.5337042625947664,"score_spread":0.35227527996418884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033253137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4173184,0.0028079352,0.5291553,0.0033737284,0.00068103743,0.00041793485,0.0099743735,0.0015857937,0.03468545],"genre_scores_gemma":[0.9414527,0.0018656028,0.015048286,0.0003104816,0.00019431816,0.00032442572,0.0032869843,0.00019542543,0.037321776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999318,0.00016591084,0.000044027995,0.00023008422,0.00009488187,0.00014712938],"domain_scores_gemma":[0.9978537,0.0010196302,0.00035514575,0.00014532729,0.00054240326,0.00008368365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021240383,0.0009821104,0.0009770483,0.0017169138,0.0003993368,0.0014797978,0.002288634,0.001664097,0.009763801],"category_scores_gemma":[0.0077770986,0.000532761,0.0015260071,0.0016776779,0.0005226459,0.0023492577,0.00073282665,0.0014822539,0.0032931548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004530438,0.00024209802,0.054429755,0.0004796532,0.0003651977,0.001385468,0.0007014062,0.75354874,0.0043124696,0.11260472,0.011444838,0.060032666],"study_design_scores_gemma":[0.000060360602,0.00015720984,0.006940288,0.00006818443,0.00015256072,0.00066693255,0.00018802226,0.95451415,0.0004317068,0.028208049,0.008567309,0.000045315224],"about_ca_topic_score_codex":0.014031162,"about_ca_topic_score_gemma":0.008218446,"teacher_disagreement_score":0.014031162,"about_ca_system_score_codex":0.0010752344,"about_ca_system_score_gemma":0.0009437968,"threshold_uncertainty_score":0.032663167},"labels":[],"label_agreement":null},{"id":"W2037696238","doi":"10.6000/1929-6029.2013.02.04.5","title":"Snapshot of Statistical Methods Used in Geriatric Cohort Studies: How Do We Treat Missing Data in Publications?","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Longitudinal data; Computer science; Data set; Data mining; Cohort; Medicine; Cohort study; Snapshot (computer storage); Statistics; Data science; Artificial intelligence; Machine learning; Mathematics","score_opus":0.3901992935197678,"score_gpt":0.6153466726185004,"score_spread":0.22514737909873261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037696238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033984464,0.11742321,0.7690458,0.062434103,0.0075537814,0.0019477389,0.0032208862,0.00095074065,0.0034392804],"genre_scores_gemma":[0.44676122,0.04259454,0.47541773,0.016528217,0.0061682356,0.007977089,0.0027245511,0.0006914447,0.0011369702],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.48681787,0.41891578,0.050445177,0.0133529,0.028909499,0.0015587687],"domain_scores_gemma":[0.1470684,0.700998,0.060753945,0.05433403,0.034469463,0.0023761913],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.47494954,0.0012316075,0.004230625,0.0124185905,0.002227955,0.01033198,0.0053267577,0.0035023335,0.003483507],"category_scores_gemma":[0.78255343,0.0013601565,0.0042142128,0.018817408,0.004945756,0.013840095,0.006353764,0.0048842235,0.0009191464],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012812234,0.0001732249,0.17038551,0.03008343,0.014393334,0.0005698702,0.014801745,0.004388261,0.00056224136,0.06336842,0.04355888,0.6564339],"study_design_scores_gemma":[0.0012146591,0.0023348595,0.11397415,0.10021037,0.011284906,0.002844122,0.0135334935,0.037693333,0.0035335973,0.5579416,0.1545687,0.00086622284],"about_ca_topic_score_codex":0.0024014076,"about_ca_topic_score_gemma":0.0030352785,"teacher_disagreement_score":0.52505046,"about_ca_system_score_codex":0.002906102,"about_ca_system_score_gemma":0.008721277,"threshold_uncertainty_score":0.6474807},"labels":[],"label_agreement":null},{"id":"W2050734361","doi":"10.6000/1929-6029.2012.01.01.02","title":"Enriched-Data Problems and Essential Non-Identifiability","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Unobservable; Identifiability; Computer science; Latent variable; Censoring (clinical trials); Statistical model; Interpretation (philosophy); Verifiable secret sharing; Data mining; Econometrics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.3281842391572342,"score_gpt":0.596176048233318,"score_spread":0.26799180907608383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050734361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011225069,0.0033338312,0.9516814,0.022449955,0.00041776846,0.0002473115,0.001495488,0.0002925525,0.008856642],"genre_scores_gemma":[0.28916162,0.0044777216,0.6852424,0.0091458,0.0019498176,0.0024637524,0.0023383312,0.00036936605,0.0048512565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9322049,0.044402443,0.0041129254,0.009480449,0.00873984,0.0010594806],"domain_scores_gemma":[0.46706906,0.47602534,0.011703455,0.034404553,0.009226882,0.0015706625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.076118544,0.0017101055,0.004603601,0.0049465513,0.004025236,0.0066829803,0.007137286,0.0083010765,0.007143813],"category_scores_gemma":[0.30909014,0.002531192,0.004431553,0.006043551,0.019454595,0.016975535,0.010406159,0.014940865,0.00077148585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029806224,0.000020918644,0.0009209121,0.00026471892,0.00008274194,0.00029227504,0.00075283286,0.003218159,0.00006431351,0.9817702,0.002392566,0.010190565],"study_design_scores_gemma":[0.000013484557,0.000004734983,0.00009255105,0.00006621608,0.000007539132,0.000077462886,0.000047129284,0.005833559,0.000039343457,0.99223316,0.0015730077,0.000011775919],"about_ca_topic_score_codex":0.003895966,"about_ca_topic_score_gemma":0.0023130048,"teacher_disagreement_score":0.076118544,"about_ca_system_score_codex":0.0046908124,"about_ca_system_score_gemma":0.003955582,"threshold_uncertainty_score":0.40255815},"labels":[],"label_agreement":null},{"id":"W2051177053","doi":"10.6000/1929-6029.2013.02.03.7","title":"Observation-Driven Model for Zero-Inflated Daily Counts of Emergency Room Visit Data","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Mount Saint Vincent University","funders":"Natural Sciences and Engineering Research Council of Canada; Mount Saint Vincent University","keywords":"Zero (linguistics); Statistics; Environmental science; Mathematics","score_opus":0.3862239440114205,"score_gpt":0.593377101926606,"score_spread":0.20715315791518546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051177053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06604544,0.00024738946,0.93042535,0.00045551182,0.00006558944,0.00017125333,0.0013302823,0.00034038018,0.0009187943],"genre_scores_gemma":[0.8405876,0.00096281455,0.14138165,0.0002951802,0.00018098342,0.0011882248,0.0060530463,0.0001477617,0.009202688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963894,0.0017656722,0.00023717573,0.0007533534,0.0005497017,0.00030465587],"domain_scores_gemma":[0.9793706,0.014828655,0.0026367116,0.0012664625,0.0015953955,0.00030221528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008935772,0.0008556206,0.0015990639,0.0014153827,0.00051298225,0.0015236724,0.004367316,0.0017295667,0.003575366],"category_scores_gemma":[0.02505485,0.000899401,0.0016595679,0.0020844215,0.0010670734,0.0023393678,0.0016716532,0.0027001426,0.0009006848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004274906,0.00028417856,0.034217577,0.00039585555,0.0003448197,0.00081840996,0.0007787432,0.7249409,0.001955477,0.18751496,0.0040845275,0.04423707],"study_design_scores_gemma":[0.000022116115,0.000099023644,0.0033282777,0.000018107443,0.000035218043,0.000082316525,0.00005673438,0.9714357,0.00028902915,0.023247514,0.0013526424,0.00003334676],"about_ca_topic_score_codex":0.0055883164,"about_ca_topic_score_gemma":0.0039620916,"teacher_disagreement_score":0.008935772,"about_ca_system_score_codex":0.0010809704,"about_ca_system_score_gemma":0.0015388555,"threshold_uncertainty_score":0.047257423},"labels":[],"label_agreement":null},{"id":"W2052144032","doi":"10.6000/1929-6029.2013.02.01.06","title":"Cautions of Using Allele-Based Tests Under Heterosis","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Heart, Lung, and Blood Institute; Washington University in St. Louis","keywords":"Heterosis; Allele; Inheritance (genetic algorithm); Genetics; Biology; Statistical power; Statistics; Mathematics; Gene; Agronomy","score_opus":0.07974879356974414,"score_gpt":0.41849964433278275,"score_spread":0.3387508507630386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052144032","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045479115,0.008222521,0.89474976,0.0323551,0.005050097,0.0009356551,0.00087525725,0.0025845058,0.009747992],"genre_scores_gemma":[0.40175056,0.0020405576,0.548562,0.0376972,0.0025049131,0.0017195872,0.00038679887,0.0010926297,0.004245747],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5926358,0.32911962,0.02009608,0.016734505,0.040473476,0.0009404882],"domain_scores_gemma":[0.31432506,0.60965574,0.019850533,0.034616943,0.020002926,0.0015487212],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.33429065,0.002142308,0.0033640775,0.0035839668,0.002335006,0.004170575,0.006080974,0.0044687637,0.0022957495],"category_scores_gemma":[0.589208,0.0011239584,0.002284823,0.005366454,0.01601237,0.005781696,0.0039568185,0.014458489,0.0018691551],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040989,0.0005927837,0.08266591,0.009024536,0.0063056983,0.0071820547,0.024577059,0.020173695,0.014823,0.211149,0.12505367,0.49435365],"study_design_scores_gemma":[0.0007814519,0.0014568991,0.056588396,0.0060997717,0.0010661235,0.010009532,0.0040764795,0.07556101,0.025993451,0.71289325,0.104494415,0.0009791845],"about_ca_topic_score_codex":0.004318959,"about_ca_topic_score_gemma":0.0058946074,"teacher_disagreement_score":0.33429065,"about_ca_system_score_codex":0.0024469295,"about_ca_system_score_gemma":0.003237611,"threshold_uncertainty_score":0.82093817},"labels":[],"label_agreement":null},{"id":"W2052886909","doi":"10.6000/1929-6029.2014.03.03.7","title":"Forecasting Rate of Decline in Infant Mortality in South Asia Using Random Walk Approximation","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Care Issues","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Infant mortality; Estimation; Time series; South asia; Geography; Econometrics; Development economics; Developing country; Demography; Economics; Statistics; Economic growth; Mathematics; History","score_opus":0.2536209235992875,"score_gpt":0.5888792187578055,"score_spread":0.33525829515851796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052886909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9523433,0.0003951635,0.04361075,0.0008658391,0.000043430384,0.000029208291,0.0006613059,0.0001487653,0.0019022301],"genre_scores_gemma":[0.99431264,0.00019273264,0.004353848,0.000034791123,0.000014059351,0.000016657596,0.0005025414,0.000007849572,0.0005649325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998252,0.00006276251,0.000014222356,0.000048659964,0.000023251137,0.000026051019],"domain_scores_gemma":[0.99884343,0.0006487964,0.00022405492,0.000057716174,0.00017449964,0.00005143517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012201326,0.00032570586,0.00035029132,0.0005249615,0.00012758789,0.00047338815,0.0006307455,0.00058259536,0.00060133083],"category_scores_gemma":[0.0040606814,0.00016055346,0.00053595955,0.0004959024,0.00019453657,0.00060066435,0.00042674204,0.00081406836,0.0001446232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008665233,0.000047538917,0.10105856,0.000059558282,0.000078357945,0.00022010428,0.00017529142,0.87900156,0.0008920432,0.004572761,0.0012076781,0.012599841],"study_design_scores_gemma":[0.0000029689857,0.000015069891,0.0076656127,0.0000063965294,0.000007262049,0.000010876453,0.00003485717,0.9913488,0.00013773031,0.00060728675,0.0001576958,0.0000055425708],"about_ca_topic_score_codex":0.028665354,"about_ca_topic_score_gemma":0.012069524,"teacher_disagreement_score":0.028665354,"about_ca_system_score_codex":0.0004911471,"about_ca_system_score_gemma":0.0004231191,"threshold_uncertainty_score":0.056997},"labels":[],"label_agreement":null},{"id":"W2053678156","doi":"10.6000/1929-6029.2014.03.03.5","title":"Improved Ridge Regression Estimators for Binary Choice Models: An Empirical Study","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multicollinearity; Estimator; Ridge; Mean squared error; Statistics; Mathematics; Regression analysis; Regression; Econometrics; Linear regression; Variables; Geography","score_opus":0.35962704625503533,"score_gpt":0.6333091455539752,"score_spread":0.2736820992989399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053678156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079464905,0.002353439,0.9160222,0.0006675133,0.000047839625,0.000083228675,0.00013116655,0.00015592287,0.0010738187],"genre_scores_gemma":[0.54077315,0.0016648304,0.4549775,0.0002248949,0.0001880071,0.00021395413,0.00025550256,0.0001612393,0.0015409181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97927696,0.017680487,0.00047133802,0.0006659009,0.0015878057,0.0003174818],"domain_scores_gemma":[0.85710496,0.12556958,0.0050312695,0.0072993864,0.0044646817,0.00053015485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041716915,0.0005747593,0.0022035514,0.0016102568,0.00037546759,0.0012048836,0.0017467943,0.001221432,0.0018648923],"category_scores_gemma":[0.114884794,0.0005178425,0.0021195656,0.0026505913,0.0011310057,0.0028128403,0.0014493073,0.0037167368,0.00036424823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008959952,0.0011924412,0.055735733,0.0011743405,0.0014868674,0.0005594515,0.0012953749,0.40193772,0.002742276,0.19609214,0.0048785806,0.33200914],"study_design_scores_gemma":[0.00014077144,0.00035261046,0.011548308,0.00012539735,0.00017943371,0.00025679806,0.00015744875,0.9250527,0.00094430294,0.05805274,0.0031063564,0.00008317525],"about_ca_topic_score_codex":0.0012888501,"about_ca_topic_score_gemma":0.0011790494,"teacher_disagreement_score":0.041716915,"about_ca_system_score_codex":0.0005484463,"about_ca_system_score_gemma":0.00080788264,"threshold_uncertainty_score":0.22062278},"labels":[],"label_agreement":null},{"id":"W2059292772","doi":"10.6000/1929-6029.2014.03.01.2","title":"Modeling Survival After Diagnosis of a Specific Disease Based on Case Surveillance Data","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Life expectancy; Parametric statistics; Disease; Life table; Population; Medicine; Survival analysis; Demography; Parametric model; Statistics; Mathematics; Environmental health; Pathology","score_opus":0.12379950754696019,"score_gpt":0.47064472328699547,"score_spread":0.3468452157400353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059292772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83309853,0.0008643237,0.15972395,0.0008737222,0.00006504619,0.00030072322,0.0034450595,0.00024073185,0.0013879043],"genre_scores_gemma":[0.9805086,0.00039479064,0.015323923,0.000058634883,0.000053034273,0.00025270658,0.0023103165,0.000015378082,0.001082649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965473,0.0021069818,0.00019270745,0.0006769976,0.0002279925,0.00024811662],"domain_scores_gemma":[0.9695155,0.024789283,0.0031447383,0.001424763,0.00078145094,0.00034430748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011809654,0.0008523957,0.00095600466,0.001997996,0.00035004326,0.001039357,0.0017916935,0.0013096387,0.0011296157],"category_scores_gemma":[0.026506422,0.00062255113,0.0015563634,0.0015548638,0.00076068216,0.0011492568,0.0011883712,0.001080551,0.00023561115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040751262,0.00026245232,0.12078015,0.00013322322,0.0004967307,0.00043547177,0.000388995,0.8423948,0.0006106548,0.008480969,0.0008101899,0.024798797],"study_design_scores_gemma":[0.000022161032,0.00011601931,0.011428811,0.0000135233395,0.00006427624,0.00010332659,0.00004639673,0.9853902,0.00014243739,0.0023503697,0.00030647026,0.000015969164],"about_ca_topic_score_codex":0.018717524,"about_ca_topic_score_gemma":0.011703677,"teacher_disagreement_score":0.018717524,"about_ca_system_score_codex":0.0012769847,"about_ca_system_score_gemma":0.00080275995,"threshold_uncertainty_score":0.06245613},"labels":[],"label_agreement":null},{"id":"W2062137129","doi":"10.6000/1929-6029.2014.03.02.12","title":"Hardy-Weinberg Equilibrium as Foundational","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hardy–Weinberg principle; Basis (linear algebra); Population genetics; Population; Constant (computer programming); Mathematical economics; Mating; Statistical physics; Mathematics; Biology; Demography; Allele frequency; Physics; Genotype; Genetics; Computer science; Sociology","score_opus":0.039223394462926386,"score_gpt":0.44355896389259536,"score_spread":0.404335569429669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062137129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011537794,0.007309994,0.92904174,0.008526129,0.0012136294,0.00025453905,0.0007300651,0.0002572697,0.04112877],"genre_scores_gemma":[0.6528562,0.006312894,0.3228031,0.0045678606,0.0017849335,0.0014390761,0.00066409993,0.00023641092,0.009335465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97557765,0.01375466,0.0012366716,0.004051699,0.0046649985,0.00071429287],"domain_scores_gemma":[0.97762364,0.0152679905,0.0016300904,0.0031487138,0.0018830883,0.0004465549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021752711,0.00078236376,0.0019399223,0.0018037021,0.0014752671,0.0043694708,0.002431255,0.0024249996,0.004647386],"category_scores_gemma":[0.05687385,0.00064485194,0.00082527683,0.001770229,0.012226023,0.0043621855,0.0035293903,0.004287145,0.0009941241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002285875,0.000013221948,0.0013915586,0.00014554057,0.00006645202,0.00023326065,0.0006908787,0.0036362493,0.00035926368,0.96260613,0.0028467986,0.027987717],"study_design_scores_gemma":[0.000010623757,0.00003070397,0.00054944085,0.00009885064,0.000017323706,0.00028191583,0.000096993164,0.004285535,0.00022821595,0.9766278,0.01774626,0.000026347943],"about_ca_topic_score_codex":0.0024990933,"about_ca_topic_score_gemma":0.0011912918,"teacher_disagreement_score":0.021752711,"about_ca_system_score_codex":0.0022711665,"about_ca_system_score_gemma":0.0029049888,"threshold_uncertainty_score":0.11504072},"labels":[],"label_agreement":null},{"id":"W2064023586","doi":"10.6000/1929-6029.2012.01.01.07","title":"Modified McNemar Test","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"McNemar's test; Statistic; Statistics; Test statistic; Test (biology); Control (management); Mathematics; Econometrics; Statistical hypothesis testing; Computer science; Biology; Artificial intelligence","score_opus":0.7547346477049882,"score_gpt":0.7089670191742586,"score_spread":0.045767628530729576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064023586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035264034,0.009858758,0.9267354,0.002289464,0.002427096,0.0021475775,0.0032309627,0.0013698714,0.01667686],"genre_scores_gemma":[0.40890968,0.0028916015,0.5679159,0.0016657045,0.0022751985,0.0051710606,0.0029481875,0.00073050155,0.0074921725],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9245481,0.04542897,0.004692791,0.009378213,0.014691806,0.0012600597],"domain_scores_gemma":[0.79012346,0.16981208,0.010214902,0.015649173,0.012480025,0.0017204103],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03527549,0.0014831787,0.004950048,0.006996013,0.0019859201,0.004331977,0.007445409,0.0036582558,0.016097263],"category_scores_gemma":[0.25909463,0.0007376282,0.002534898,0.0063962406,0.0040198443,0.0040105563,0.0035173215,0.0050399853,0.0030995288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028584048,0.00048704547,0.040298097,0.0031814284,0.0037801159,0.0017532257,0.001484546,0.061652917,0.0023242678,0.16884314,0.04929118,0.6640457],"study_design_scores_gemma":[0.00095773215,0.0051867072,0.05098974,0.0026231916,0.0018217955,0.008325594,0.0024283866,0.39850295,0.010598819,0.3191076,0.19830215,0.001155369],"about_ca_topic_score_codex":0.0027074057,"about_ca_topic_score_gemma":0.0014052204,"teacher_disagreement_score":0.96472454,"about_ca_system_score_codex":0.002064022,"about_ca_system_score_gemma":0.004463313,"threshold_uncertainty_score":0.18655688},"labels":[],"label_agreement":null},{"id":"W2068588614","doi":"10.6000/1929-6029.2012.01.01.05","title":"Cost Assessment of Epidemiologic Surveys in Dentistry","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Sample size determination; Sample (material); Coefficient of variation; Margin (machine learning); Mathematics; Sampling error; Sampling (signal processing); Econometrics; Variable (mathematics); Observational error; Computer science","score_opus":0.23238788375326563,"score_gpt":0.5859553681676988,"score_spread":0.35356748441443314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068588614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4825384,0.036902986,0.4428331,0.011376604,0.00072491035,0.004694238,0.003226399,0.00021905798,0.017484292],"genre_scores_gemma":[0.9215608,0.004738902,0.07019819,0.00031821794,0.00021512738,0.0017432058,0.0006326124,0.000024026822,0.0005689549],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.80332494,0.17662422,0.005393191,0.0013516656,0.012669787,0.0006362569],"domain_scores_gemma":[0.62282544,0.33692122,0.022352565,0.0072332285,0.009895966,0.0007715127],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06378143,0.0008655442,0.0010817312,0.0029284146,0.0003501798,0.0016059146,0.0012519635,0.0013672567,0.0019567115],"category_scores_gemma":[0.3282911,0.00035428535,0.0014365729,0.003904675,0.0011653106,0.0018344457,0.002280093,0.00083370134,0.00013209778],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027861474,0.0004910342,0.3326437,0.0060201366,0.0031235826,0.0004063881,0.0008742018,0.18209632,0.0005881092,0.04885003,0.0041476535,0.41797262],"study_design_scores_gemma":[0.00059921783,0.007233947,0.43110558,0.005566168,0.003068863,0.0025531333,0.0026622775,0.43184856,0.0025631639,0.088885665,0.02361084,0.00030247972],"about_ca_topic_score_codex":0.0020828124,"about_ca_topic_score_gemma":0.0015284417,"teacher_disagreement_score":0.93621856,"about_ca_system_score_codex":0.00329799,"about_ca_system_score_gemma":0.0026116015,"threshold_uncertainty_score":0.33731246},"labels":[],"label_agreement":null},{"id":"W2070742341","doi":"10.6000/1929-6029.2025.14.78","title":"Autoencoder-Based Nonlinear Dimension Reduction for Single-Cell RNA-Seq Data: A Comparative Study of t-SNE and UMAP","year":2009,"lang":"en","type":"letter","venue":"International Journal of Statistics in Medical Research","topic":"Abdominal Trauma and Injuries","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Blunt; Abdominal trauma; Abdominal pain; Surgery; Blunt trauma; General surgery","score_opus":0.25915218236921356,"score_gpt":0.5106269590493152,"score_spread":0.25147477668010165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070742341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04160495,0.0008705419,0.955009,0.00025704852,0.000057464506,0.00006133556,0.00011583865,0.0010190618,0.0010046515],"genre_scores_gemma":[0.34171358,0.0014222754,0.6525385,0.00016996419,0.000047939506,0.00019359666,0.0008449065,0.00033156242,0.0027376553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942386,0.0001882984,0.000038554997,0.00014083572,0.00016694806,0.000041503034],"domain_scores_gemma":[0.99855,0.0008319114,0.00009645446,0.00016186407,0.00030562852,0.00005410703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021293447,0.0010551704,0.000640076,0.00070371956,0.00028365935,0.0007526073,0.0007688712,0.0006986171,0.0011288954],"category_scores_gemma":[0.0046624155,0.00034354097,0.0010671883,0.0006354895,0.00062080205,0.0013166061,0.001024082,0.0014162612,0.00044630902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038392874,0.00017442378,0.003430024,0.000572236,0.00031663536,0.00021806882,0.0003206557,0.37038785,0.05824913,0.0065173996,0.0014940451,0.5579356],"study_design_scores_gemma":[0.0000065936138,0.0000791588,0.0013999495,0.000019765444,0.000020653017,0.00006756904,0.000050850264,0.9829336,0.012833269,0.0015551494,0.0010128225,0.000020679465],"about_ca_topic_score_codex":0.0027933563,"about_ca_topic_score_gemma":0.003936974,"teacher_disagreement_score":0.0027933563,"about_ca_system_score_codex":0.00038756995,"about_ca_system_score_gemma":0.000807189,"threshold_uncertainty_score":0.011261165},"labels":[],"label_agreement":null},{"id":"W2077106477","doi":"10.6000/1929-6029.2015.04.01.12","title":"Time Profile of Time-Dependent Area Under the ROC Curve for Survival Data","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Biomarker; Statistics; Hazard ratio; Sensitivity (control systems); Area under the curve; Value (mathematics); Area under curve; Binary number; Hazard; Measure (data warehouse); Survival analysis; Constant (computer programming); Mathematics; Oncology; Medicine; Internal medicine; Computer science; Data mining; Biology; Confidence interval; Engineering","score_opus":0.1772351554068453,"score_gpt":0.4632251824560452,"score_spread":0.2859900270491999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077106477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2712755,0.008065776,0.7098344,0.001220318,0.00017749082,0.00026679513,0.00211386,0.0026822074,0.004363576],"genre_scores_gemma":[0.9025923,0.001635931,0.09215555,0.00021664411,0.00014558314,0.00028135307,0.0016325396,0.00041088153,0.0009291822],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99034506,0.0054161362,0.0007254243,0.0012027728,0.0018893989,0.0004211716],"domain_scores_gemma":[0.9211865,0.05831087,0.0079740165,0.005343907,0.0062693707,0.0009153291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015914686,0.00089468894,0.0010055859,0.0038024525,0.00033879123,0.0022550067,0.00085856754,0.0016458726,0.0013349806],"category_scores_gemma":[0.0890279,0.00024567335,0.0010904766,0.002802923,0.0011497117,0.0023004857,0.0011146612,0.0016511187,0.0008596854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035324134,0.00034617816,0.18942349,0.0023678415,0.0014959004,0.0030041335,0.003028195,0.22935839,0.05620421,0.048725307,0.0059476346,0.45656627],"study_design_scores_gemma":[0.00009257147,0.0033135395,0.16511862,0.00040691614,0.00052115327,0.0091006365,0.00094930065,0.7183348,0.03258187,0.05278747,0.016257884,0.00053529686],"about_ca_topic_score_codex":0.0007910497,"about_ca_topic_score_gemma":0.00034058533,"teacher_disagreement_score":0.015914686,"about_ca_system_score_codex":0.0007749146,"about_ca_system_score_gemma":0.00077023666,"threshold_uncertainty_score":0.08416593},"labels":[],"label_agreement":null},{"id":"W2081573765","doi":"10.6000/1929-6029.2014.03.02.5","title":"The Influence of Family Factors on Smoking Behavior in Turkey","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Kırıkkale Üniversitesi","keywords":"Sibling; Demography; Logistic regression; Turkish; Birth order; Medicine; Psychology; Developmental psychology; Population; Internal medicine","score_opus":0.09180255189262564,"score_gpt":0.4757805203398054,"score_spread":0.38397796844717974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081573765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983523,0.00080457475,0.000085277854,0.00008133856,0.0000065256686,0.0000027032427,0.000120488345,0.0000028319935,0.00054397696],"genre_scores_gemma":[0.99948573,0.00026735378,0.0000730148,0.000008851374,0.0000030685253,0.0000013821117,0.000071630835,9.3965883e-7,0.00008790122],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951696,0.00020512272,0.00003820551,0.00009335714,0.00008698167,0.00005929762],"domain_scores_gemma":[0.99941075,0.0001523951,0.00021569117,0.000029506986,0.000079449586,0.000112272915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003969686,0.0002112435,0.00019083208,0.0007139195,0.00039753833,0.00042850146,0.00020048831,0.00020019071,0.0011696579],"category_scores_gemma":[0.0014118057,0.00013262268,0.00034595683,0.00047188386,0.0001829977,0.00020792334,0.00025139164,0.00021616284,0.00007987171],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031666343,0.000023961897,0.99480754,0.000009869154,0.000048544247,0.00018973605,0.0001560512,0.00007745454,0.000114140865,0.000046327783,0.00008919548,0.0044055237],"study_design_scores_gemma":[9.615936e-7,0.00003547475,0.99880433,0.000013398893,0.000026879014,0.00027992873,0.00031264275,0.00028148544,0.00003073819,0.000045164332,0.00016573655,0.0000032051498],"about_ca_topic_score_codex":0.02470349,"about_ca_topic_score_gemma":0.025671424,"teacher_disagreement_score":0.02470349,"about_ca_system_score_codex":0.00048828585,"about_ca_system_score_gemma":0.00044311435,"threshold_uncertainty_score":0.049119413},"labels":[],"label_agreement":null},{"id":"W2084674170","doi":"10.6000/1929-6029.2012.01.01.06","title":"Nonparametric and Semiparametric Regression Analysis of Group Testing Samples","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Nonparametric statistics; Semiparametric regression; Nonparametric regression; Statistics; Econometrics; Semiparametric model; Regression analysis; Population; Mathematics; Regression; Medicine","score_opus":0.19108927469514805,"score_gpt":0.49680615338779355,"score_spread":0.3057168786926455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084674170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050547146,0.00008465816,0.9940017,0.0001018255,0.000013340636,0.000052812018,0.00008301614,0.00016144456,0.00044641618],"genre_scores_gemma":[0.4243768,0.00051455473,0.56748074,0.0003349822,0.00020774432,0.0013927822,0.0009615326,0.00037970388,0.00435121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.979963,0.015272412,0.0004407327,0.0016338244,0.0022033283,0.00048657422],"domain_scores_gemma":[0.92078394,0.06491141,0.0040628067,0.006657594,0.0031868631,0.0003974642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025788236,0.0010510575,0.0019147459,0.0021038235,0.00046690594,0.0014567726,0.0029654105,0.001351653,0.004264606],"category_scores_gemma":[0.105797365,0.0006703687,0.0019485608,0.0016817717,0.0020319568,0.0024538273,0.0024969701,0.0024832238,0.00085852016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021230866,0.0002343807,0.014082456,0.0004793122,0.000579299,0.00043762533,0.00072939816,0.42307898,0.0032368733,0.33208215,0.0035699906,0.22127709],"study_design_scores_gemma":[0.000026211768,0.000084454616,0.002976855,0.000049472717,0.000044466524,0.00012359212,0.00007146384,0.88700604,0.0011046501,0.10571736,0.0027618785,0.000033514523],"about_ca_topic_score_codex":0.0016810618,"about_ca_topic_score_gemma":0.0011396979,"teacher_disagreement_score":0.025788236,"about_ca_system_score_codex":0.0009852082,"about_ca_system_score_gemma":0.0013748863,"threshold_uncertainty_score":0.13638282},"labels":[],"label_agreement":null},{"id":"W2084710711","doi":"10.6000/1929-6029.2015.04.01.3","title":"Bayesian Inference Supports the Use of Bypass Surgery Over Percutaneous Coronary Intervention To Reduce Mortality in Diabetic Patients with Multivessel Coronary Disease","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Conventional PCI; Medicine; Percutaneous coronary intervention; Cardiology; Internal medicine; Coronary artery disease; Revascularization; Odds ratio; Confidence interval; Coronary artery bypass surgery; Randomized controlled trial; Hazard ratio; Diabetes mellitus; Myocardial infarction; Surgery; Artery","score_opus":0.11014959724984177,"score_gpt":0.44473030515606515,"score_spread":0.3345807079062234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084710711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7480027,0.0133287385,0.20992792,0.016670894,0.0004442159,0.0010671511,0.0015393877,0.00025786716,0.008761151],"genre_scores_gemma":[0.97430104,0.0009304352,0.021999402,0.0015476663,0.00023277901,0.00017445935,0.00041822292,0.000023487502,0.0003726029],"study_design_codex":"observational","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9100215,0.07764794,0.0025535082,0.0060646227,0.002771256,0.0009411891],"domain_scores_gemma":[0.47922567,0.4933307,0.01677953,0.0058312113,0.003177512,0.0016553937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.100212224,0.0015295219,0.0036565233,0.0022595592,0.001391778,0.0037080739,0.0024507025,0.0023986162,0.0041529],"category_scores_gemma":[0.33186963,0.0013654272,0.005893356,0.001506708,0.0032582006,0.0026089747,0.0023645307,0.0040157326,0.00030663982],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.023509802,0.0011735962,0.34871078,0.0035677715,0.037004054,0.0017945057,0.0029579755,0.34665796,0.0017636763,0.06317594,0.005567696,0.16411631],"study_design_scores_gemma":[0.007314419,0.0032909722,0.090808325,0.0015905831,0.018178986,0.0010454116,0.00057011057,0.68111897,0.0015089739,0.18839523,0.005878957,0.00029907987],"about_ca_topic_score_codex":0.012403109,"about_ca_topic_score_gemma":0.009962172,"teacher_disagreement_score":0.100212224,"about_ca_system_score_codex":0.0022870547,"about_ca_system_score_gemma":0.0034941516,"threshold_uncertainty_score":0.5299792},"labels":[],"label_agreement":null},{"id":"W2085276374","doi":"10.6000/1929-6029.2014.03.03.4","title":"Predicting Risks of Increased Morbidity among Atrial Fibrillation Patients using Consumption Classes","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Bivariate analysis; Atrial fibrillation; Regression analysis; Framingham Risk Score; Multivariate statistics; Regression; Risk assessment; Internal medicine; Statistics; Disease; Computer science","score_opus":0.5926253083857957,"score_gpt":0.5837067368994308,"score_spread":0.00891857148636499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085276374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99534935,0.00010714374,0.003113826,0.00012594517,0.0000067051424,0.000019229188,0.0007583303,0.000029820603,0.00048953894],"genre_scores_gemma":[0.99784017,0.000052446998,0.0011912528,0.000008141969,0.0000073425217,0.0000104674045,0.0007708037,0.0000039832785,0.00011521116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99958783,0.00018767077,0.000054619522,0.00006212568,0.00006703283,0.0000406736],"domain_scores_gemma":[0.99541605,0.0023936413,0.0012965905,0.00025141105,0.00034632653,0.00029584498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016383686,0.00040230033,0.00036475484,0.0016296812,0.00016594098,0.0007220667,0.00023991802,0.00037882078,0.001934738],"category_scores_gemma":[0.008508627,0.0001119151,0.00078943407,0.0009765727,0.00016553972,0.00036870546,0.00048226875,0.0006485679,0.0003373591],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018905508,0.00005228201,0.991446,0.0000070619826,0.000051894098,0.000015954787,0.000021828933,0.002595122,0.00007828059,0.00008225907,0.000165268,0.0052950257],"study_design_scores_gemma":[0.000022143437,0.00029154881,0.90552646,0.000021048687,0.00009306009,0.00015698507,0.00011897334,0.092230506,0.00036745847,0.0009039684,0.00024807433,0.000019848869],"about_ca_topic_score_codex":0.0039812583,"about_ca_topic_score_gemma":0.0030136225,"teacher_disagreement_score":0.0039812583,"about_ca_system_score_codex":0.0002941865,"about_ca_system_score_gemma":0.00035374332,"threshold_uncertainty_score":0.008664608},"labels":[],"label_agreement":null},{"id":"W2088609660","doi":"10.6000/1929-6029.2015.04.01.11","title":"The Hybrid ROC (HROC) Curve and its Divergence Measures for Binary Classification","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Divergence (linguistics); Mathematics; Measure (data warehouse); Binary classification; Statistics; Binary number; Pattern recognition (psychology); Kullback–Leibler divergence; Function (biology); Artificial intelligence; Computer science; Biology; Data mining","score_opus":0.19146622763271332,"score_gpt":0.4669100906900752,"score_spread":0.2754438630573619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088609660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060448885,0.015737988,0.9152052,0.0013042423,0.0002739125,0.00024304894,0.0011350824,0.0011461752,0.004505521],"genre_scores_gemma":[0.7524498,0.0040485957,0.23820092,0.00065319927,0.00046481582,0.00064746337,0.0015325851,0.00038599875,0.0016165904],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9841353,0.0077603254,0.0010948766,0.0023357745,0.0041796155,0.0004941023],"domain_scores_gemma":[0.94227797,0.039819695,0.005779166,0.005449288,0.0056608343,0.0010130402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01914553,0.0015502815,0.0022682608,0.009097732,0.0007613716,0.0032650367,0.0018003345,0.002844208,0.0012914336],"category_scores_gemma":[0.067179434,0.00035097907,0.0020181397,0.006140457,0.0031969657,0.003760248,0.0024650276,0.002358202,0.0007676277],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010401993,0.0003550025,0.0880109,0.002772012,0.001927824,0.001163083,0.0010881573,0.24514757,0.00844712,0.13676627,0.01267077,0.5006112],"study_design_scores_gemma":[0.00006446129,0.0012741943,0.041778658,0.0005505722,0.0003551559,0.005263654,0.0005198596,0.7497872,0.004828681,0.17682423,0.018341007,0.00041233233],"about_ca_topic_score_codex":0.0018114211,"about_ca_topic_score_gemma":0.00076862867,"teacher_disagreement_score":0.01914553,"about_ca_system_score_codex":0.0018678468,"about_ca_system_score_gemma":0.0015860731,"threshold_uncertainty_score":0.101252496},"labels":[],"label_agreement":null},{"id":"W2088732975","doi":"10.6000/1929-6029.2015.04.01.16","title":"A Study of Risk Factors for Breast Cancer in a Primary Oncology Clinic in Benghazi-Libya","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Breast cancer; Cancer; Medical record; Malignancy; Odds ratio; Stage (stratigraphy); Internal medicine; Population; Gynecology; Risk factor; Family history; Risk factors for breast cancer","score_opus":0.3177808614801817,"score_gpt":0.5886975077509701,"score_spread":0.2709166462707884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088732975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994012,0.00017865829,0.000012936831,0.00006458153,0.0000034957382,0.00001772454,0.00007216974,9.144103e-7,0.0002484376],"genre_scores_gemma":[0.99937505,0.00022522827,0.000040738847,0.000055993376,0.000012277541,0.000019970614,0.00007968182,5.330898e-7,0.00019054141],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99966395,0.00009187053,0.000029968156,0.000046362056,0.00007064139,0.00009724621],"domain_scores_gemma":[0.9993444,0.00014629558,0.00023728075,0.00003061937,0.00007755205,0.00016384172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003517391,0.00022788049,0.0002486906,0.0008656923,0.0012880113,0.00073261786,0.0003726787,0.0003773452,0.0027832123],"category_scores_gemma":[0.0012708592,0.0003422915,0.00019959892,0.001372172,0.00037463132,0.0003645428,0.00054203306,0.00044219507,0.00030038916],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025169355,0.00009685469,0.99711406,0.000023884426,0.000010801671,0.0005082951,0.0009404653,0.0000054694756,0.0001414878,0.000009577209,0.00011304924,0.0010109344],"study_design_scores_gemma":[0.000009842139,0.00015021938,0.9946544,0.00001765178,0.000012565882,0.0010444586,0.0036223785,0.000034220073,0.000052883857,0.000008578295,0.00038995204,0.00000282585],"about_ca_topic_score_codex":0.014566704,"about_ca_topic_score_gemma":0.014754897,"teacher_disagreement_score":0.014566704,"about_ca_system_score_codex":0.00069875823,"about_ca_system_score_gemma":0.0010191828,"threshold_uncertainty_score":0.028963804},"labels":[],"label_agreement":null},{"id":"W2094794062","doi":"10.6000/1929-6029.2012.01.01.08","title":"Performance Measures in Binary Classification","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Predictive value; Receiver operating characteristic; Binary classification; Statistics; Sensitivity (control systems); Mathematics; Binary number; Value (mathematics); Likelihood ratios in diagnostic testing; Artificial intelligence; Pattern recognition (psychology); Computer science; Medicine; Support vector machine; Internal medicine; Engineering; Arithmetic","score_opus":0.14221368785995453,"score_gpt":0.46019623576992613,"score_spread":0.3179825479099716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094794062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025585331,0.07561777,0.85223633,0.004257107,0.0025488378,0.0005729678,0.005585529,0.003616474,0.029979581],"genre_scores_gemma":[0.37376404,0.030833332,0.5628028,0.002117322,0.0066963416,0.0021891475,0.011910439,0.0017679665,0.007918713],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9597126,0.017172387,0.004710673,0.004570254,0.01267047,0.0011635784],"domain_scores_gemma":[0.90406907,0.074061975,0.006590873,0.006136799,0.0082753105,0.000866014],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02762837,0.0028641257,0.002300443,0.011262303,0.0011353212,0.0055138404,0.0025553592,0.0027018108,0.0050825058],"category_scores_gemma":[0.12129305,0.00051317667,0.0016783823,0.011667266,0.0022482954,0.0070037656,0.002579806,0.003774343,0.004156074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005777982,0.00037356297,0.031775236,0.003991186,0.00085542805,0.00026487204,0.0005622361,0.03636348,0.0026828465,0.11438322,0.064377725,0.7437925],"study_design_scores_gemma":[0.00012897649,0.0017102595,0.03644972,0.003366689,0.00077755563,0.0041937204,0.00074957643,0.24996465,0.01586409,0.5284576,0.15777822,0.00055894884],"about_ca_topic_score_codex":0.0010343988,"about_ca_topic_score_gemma":0.00048404277,"teacher_disagreement_score":0.97237164,"about_ca_system_score_codex":0.0018061936,"about_ca_system_score_gemma":0.0013772231,"threshold_uncertainty_score":0.14611459},"labels":[],"label_agreement":null},{"id":"W2095889036","doi":"10.6000/1929-6029.2013.02.04.4","title":"Long-Run Macroeconomic Determinants of Cancer Incidence","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Income elasticity of demand; Economics; Per capita income; Per capita; Incidence (geometry); Econometrics; Cancer incidence; Demographic economics; Cancer; Demography; Mathematics; Medicine; Population; Environmental health","score_opus":0.12265451229714609,"score_gpt":0.6204643917360818,"score_spread":0.49780987943893573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095889036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98274475,0.0019268319,0.0035245481,0.0027831392,0.000041425566,0.000024859333,0.0032748452,0.000049349943,0.005630246],"genre_scores_gemma":[0.9984383,0.00040867075,0.00014726662,0.000039835064,0.00002069677,0.0000053854424,0.0006770201,0.0000037502805,0.00025911297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979407,0.000053883625,0.000019063938,0.00004304261,0.00003114105,0.000058835023],"domain_scores_gemma":[0.99739456,0.0008217239,0.0011722085,0.00014425679,0.00020875591,0.00025842528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000785946,0.00013478441,0.00021034948,0.0005954065,0.00019204392,0.00086046965,0.00017118035,0.00031224737,0.002433751],"category_scores_gemma":[0.0037579408,0.000105311614,0.00034786295,0.0010299481,0.0004064727,0.0004867783,0.00050074264,0.000652619,0.00029153124],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046946145,0.000032395328,0.9798426,0.000043522556,0.00009563679,0.00008765276,0.00008018303,0.011525964,0.0002557775,0.0032682058,0.0007915057,0.0039297463],"study_design_scores_gemma":[0.0000041951585,0.000037728387,0.982852,0.000031640953,0.00005372451,0.00009516537,0.00015804102,0.011330846,0.00019641998,0.003584643,0.0016452459,0.000010423358],"about_ca_topic_score_codex":0.007708311,"about_ca_topic_score_gemma":0.005321213,"teacher_disagreement_score":0.007708311,"about_ca_system_score_codex":0.0007282746,"about_ca_system_score_gemma":0.00052027195,"threshold_uncertainty_score":0.015326917},"labels":[],"label_agreement":null},{"id":"W2100297422","doi":"10.6000/1929-6029.2012.01.02.09","title":"Validation of Gene Expression Profiles in Genomic Data through Complementary Use of Cluster Analysis and PCA-Related Biplots","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Data mining; Computer science; Normalization (sociology); Hierarchical clustering; Computational biology; Expression (computer science); Biplot; Principal component analysis; Stability (learning theory); Pattern recognition (psychology); Artificial intelligence; Biology; Gene; Machine learning; Genetics","score_opus":0.1376354957064008,"score_gpt":0.45836002833195755,"score_spread":0.3207245326255568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100297422","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27276343,0.0014128027,0.71655846,0.0002775575,0.00013539799,0.00045379967,0.003358275,0.0030749182,0.0019653444],"genre_scores_gemma":[0.6412434,0.00078483234,0.35107902,0.000071876,0.000049987342,0.0009303863,0.0048116874,0.0004795809,0.00054933643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99310464,0.0033736422,0.0004939927,0.0012069874,0.0015711157,0.00024952838],"domain_scores_gemma":[0.9885188,0.006401335,0.0011028232,0.001853331,0.00196679,0.00015694807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006768616,0.001199032,0.0011945872,0.004999971,0.00077119365,0.001867453,0.0006960532,0.00042540437,0.0010941196],"category_scores_gemma":[0.019503085,0.00019309788,0.0012456398,0.0057875267,0.0011059237,0.0007952785,0.001025714,0.0012961259,0.000581408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031830878,0.00083875423,0.06333366,0.0031088889,0.001841211,0.0006550534,0.002224333,0.06666535,0.29295588,0.008692548,0.0033963763,0.5531048],"study_design_scores_gemma":[0.00019643235,0.0020506538,0.25155452,0.00031951113,0.00074418075,0.0014639401,0.0018017542,0.47452074,0.23520572,0.01918481,0.012534331,0.00042332988],"about_ca_topic_score_codex":0.001905012,"about_ca_topic_score_gemma":0.0011926066,"teacher_disagreement_score":0.006768616,"about_ca_system_score_codex":0.00046014556,"about_ca_system_score_gemma":0.0010360181,"threshold_uncertainty_score":0.035796285},"labels":[],"label_agreement":null},{"id":"W2102099704","doi":"10.6000/1929-6029.2012.01.02.10","title":"Analysis of RT-qPCR Data","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Real-time polymerase chain reaction; Reverse transcription polymerase chain reaction; Statistical analysis; Reverse transcriptase; Computational biology; Database normalization; Statistics; Cover (algebra); Computer science; Polymerase chain reaction; Biology; Mathematics; Messenger RNA; Genetics; Engineering; Gene","score_opus":0.09306684180203897,"score_gpt":0.5164411277763602,"score_spread":0.42337428597432125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102099704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017994668,0.0078202775,0.85288376,0.0007145698,0.0028688451,0.00679385,0.08324418,0.015882056,0.011797838],"genre_scores_gemma":[0.022700705,0.0091505125,0.83588964,0.0012367445,0.000691189,0.029223656,0.07614288,0.0063971137,0.018567564],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9922484,0.0013544555,0.0010979819,0.0017899253,0.0028517074,0.00065751694],"domain_scores_gemma":[0.99484813,0.0020206245,0.00040774528,0.0011058358,0.0014815044,0.0001361871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047910954,0.0037675125,0.0042966716,0.0042137667,0.001788478,0.0020098477,0.0024984968,0.0016717638,0.03741746],"category_scores_gemma":[0.008732542,0.0017663537,0.0030592545,0.005083878,0.0012646707,0.0011760208,0.0013241274,0.0063866707,0.035230774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070544775,0.00045946514,0.0010637001,0.0046231784,0.00020783934,0.0004271783,0.00035455285,0.0025529533,0.91802484,0.0032283221,0.020007566,0.048345048],"study_design_scores_gemma":[0.00012535256,0.0008025061,0.0030890505,0.0005097684,0.0002648955,0.0007522624,0.00015831215,0.005989268,0.77257025,0.004790439,0.21071713,0.0002308462],"about_ca_topic_score_codex":0.0008660924,"about_ca_topic_score_gemma":0.0017853472,"teacher_disagreement_score":0.03741746,"about_ca_system_score_codex":0.00083769567,"about_ca_system_score_gemma":0.0020957333,"threshold_uncertainty_score":0.12517393},"labels":[],"label_agreement":null},{"id":"W2105264183","doi":"10.6000/1929-6029.2013.02.01.08","title":"Heterogeneity in Preferences for Primary Care Consultations: Results from a Discrete Choice Experiment","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pairwise comparison; Primary care; Flexibility (engineering); Scale (ratio); Family medicine; Sample (material); Psychology; Medicine; Nursing; Geography; Statistics","score_opus":0.3510320277761778,"score_gpt":0.5755432794372984,"score_spread":0.22451125166112063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105264183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976901,0.000026224521,0.0011735414,0.00006764679,0.000011699084,0.0003883484,0.000060540307,0.0000039229803,0.0005779756],"genre_scores_gemma":[0.9956052,0.000031572785,0.0025693034,0.00012310494,0.000018662959,0.0011140781,0.00009541978,0.00000501536,0.00043775485],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9716432,0.023727208,0.0010549756,0.001156551,0.0018694567,0.0005485201],"domain_scores_gemma":[0.75525945,0.23134768,0.0065600984,0.0041722255,0.0010157863,0.0016448083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037854306,0.00042230837,0.0010220556,0.00031282782,0.0006846736,0.0018092901,0.0006088417,0.0011248427,0.0049797506],"category_scores_gemma":[0.055633143,0.00035457476,0.00093106757,0.0004410162,0.0016572997,0.00089147413,0.000853819,0.0016187053,0.00035058663],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.23968048,0.17106654,0.32164106,0.0025331578,0.003268607,0.0013798297,0.04989006,0.021676593,0.04578675,0.00914364,0.0023347663,0.13159847],"study_design_scores_gemma":[0.039646856,0.27502573,0.5180432,0.0003547323,0.0014577344,0.00078994373,0.013786116,0.10453932,0.01661756,0.022366118,0.0068118903,0.0005607349],"about_ca_topic_score_codex":0.00080629485,"about_ca_topic_score_gemma":0.0005233697,"teacher_disagreement_score":0.037854306,"about_ca_system_score_codex":0.0007370878,"about_ca_system_score_gemma":0.0009801857,"threshold_uncertainty_score":0.20019507},"labels":[],"label_agreement":null},{"id":"W2106365739","doi":"10.6000/1929-6029.2012.01.01.01","title":"The Health Status of Dentists Exposed to Mercury from Silver Amalgam Tooth Restorations","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Amalgam (chemistry); Medicine; Dentistry; Medical prescription; Mercury (programming language); Family medicine; Pharmacy; Environmental health; Nursing","score_opus":0.0924619803716777,"score_gpt":0.46334069126274624,"score_spread":0.37087871089106855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106365739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99977356,0.000027067827,0.000013971938,0.000009744101,9.429812e-7,0.000003722048,0.000047622045,4.847704e-7,0.00012289191],"genre_scores_gemma":[0.9995283,0.00006609729,0.000048001533,0.000025375077,0.0000052929113,0.0000048881266,0.00011869984,4.400623e-7,0.00020306965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995858,0.000094608906,0.000043846936,0.0000543486,0.00014738341,0.00007396408],"domain_scores_gemma":[0.9990645,0.00025682783,0.00029694178,0.000067457615,0.00018217656,0.0001321338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061694183,0.00016266025,0.00023926492,0.0011247938,0.0005808918,0.00043375613,0.00017958602,0.00039626795,0.0011216609],"category_scores_gemma":[0.0024045303,0.00029234617,0.0002690163,0.00071009016,0.00024455646,0.0002511456,0.00041763214,0.0002705377,0.00030025718],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008680379,0.00013370784,0.99637103,0.0000065918384,0.000020465552,0.00007742543,0.00046219057,0.000019176787,0.0007680634,0.000008200438,0.000050142928,0.0019962692],"study_design_scores_gemma":[0.0000054429115,0.00023460809,0.9986652,0.0000019001114,0.000012685224,0.00020307569,0.00049039494,0.00006658973,0.00014802163,0.00000841109,0.00016098273,0.0000026718064],"about_ca_topic_score_codex":0.008499106,"about_ca_topic_score_gemma":0.01040175,"teacher_disagreement_score":0.008499106,"about_ca_system_score_codex":0.00025740813,"about_ca_system_score_gemma":0.00023703405,"threshold_uncertainty_score":0.016899288},"labels":[],"label_agreement":null},{"id":"W2108852559","doi":"10.6000/1929-6029.2015.04.01.15","title":"Determinants of Immunization Among Children Aged 12-23 Months in Ethiopia: A Proportional Odds Model Approach","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ordered logit; Logistic regression; Odds; Immunization; Demography; Public health interventions; Psychological intervention; Medicine; Ordinal regression; Descriptive statistics; Public health; Regression analysis; Environmental health; Statistics; Immunology; Sociology","score_opus":0.09346369690431838,"score_gpt":0.45096931613751956,"score_spread":0.3575056192332012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108852559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9661809,0.0010960657,0.027766284,0.0019110494,0.0001569909,0.0002055632,0.001062853,0.00009136638,0.0015290276],"genre_scores_gemma":[0.9901148,0.0006374698,0.006898782,0.00009188371,0.000099331126,0.00016653801,0.00049207406,0.000016488508,0.0014826028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973028,0.0018928989,0.00008348181,0.0002768241,0.00011523369,0.00032884616],"domain_scores_gemma":[0.9946089,0.004482803,0.0003621632,0.0001125132,0.00018684374,0.0002466916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064182,0.0009366812,0.0010389176,0.0015444454,0.0006181538,0.0018058625,0.0019866026,0.0009901272,0.00424389],"category_scores_gemma":[0.008196185,0.0007267136,0.002597179,0.0009143989,0.00036958556,0.0010093498,0.0010859924,0.0020128377,0.00035999567],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001561143,0.0020900415,0.8710908,0.0002976922,0.003113489,0.0013794694,0.0007908983,0.07477246,0.00036142702,0.00714593,0.003423073,0.033973698],"study_design_scores_gemma":[0.00018261684,0.0008830423,0.08784601,0.00016245218,0.0010495172,0.00048676183,0.0016120352,0.9010805,0.0001545999,0.004806167,0.0016844843,0.00005184303],"about_ca_topic_score_codex":0.020037355,"about_ca_topic_score_gemma":0.007668537,"teacher_disagreement_score":0.020037355,"about_ca_system_score_codex":0.00071703,"about_ca_system_score_gemma":0.001639955,"threshold_uncertainty_score":0.039841473},"labels":[],"label_agreement":null},{"id":"W2109261812","doi":"10.6000/1929-6029.2014.03.01.4","title":"Quantifying Maternal and Paternal Disease History Using Log-Rank Score with an Application to a National Cohort Study","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; Northwestern University","keywords":"Rank (graph theory); Cohort; Demography; Statistics; Medicine; Psychology; Mathematics; Sociology; Combinatorics","score_opus":0.1379066941544187,"score_gpt":0.47148318914592036,"score_spread":0.33357649499150166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109261812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8341466,0.0006027906,0.16121188,0.0007742462,0.00007868161,0.00023064888,0.0018218312,0.00031741304,0.00081593165],"genre_scores_gemma":[0.9495481,0.00011138308,0.049162578,0.00005721067,0.00002144034,0.00013593062,0.00053751166,0.000020319952,0.00040555967],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98858786,0.009429949,0.0003629405,0.0007699719,0.0006134547,0.00023582329],"domain_scores_gemma":[0.9482636,0.040953897,0.0034122283,0.004511271,0.002042668,0.0008163421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026786542,0.0006272933,0.00089682994,0.001477186,0.0005809039,0.0011852182,0.001244915,0.00088012894,0.0016287891],"category_scores_gemma":[0.06103008,0.0003041136,0.0017108008,0.0022280093,0.0008299098,0.0007963033,0.0011297636,0.0012993162,0.0002064459],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011298227,0.00023337457,0.8797329,0.00014842892,0.0015624434,0.0005243254,0.00047852987,0.05352469,0.0007224827,0.004601788,0.0012552419,0.05608601],"study_design_scores_gemma":[0.00017885408,0.0007178747,0.21894597,0.000046449633,0.00061814656,0.00064040674,0.00034243346,0.76673424,0.00069721614,0.008959218,0.0020181884,0.00010088261],"about_ca_topic_score_codex":0.02051119,"about_ca_topic_score_gemma":0.017214343,"teacher_disagreement_score":0.026786542,"about_ca_system_score_codex":0.0007915546,"about_ca_system_score_gemma":0.0012472103,"threshold_uncertainty_score":0.14166242},"labels":[],"label_agreement":null},{"id":"W2115740690","doi":"10.6000/1929-6029.2015.04.01.9","title":"Some Useful Properties of Log-Logistic Random Variables for Health Care Simulations","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Random variable; Mathematics; Sum of normally distributed random variables; Statistics; Logarithm; Logistic regression; Variable (mathematics); Normal distribution; Multivariate random variable; Mathematical analysis","score_opus":0.3867722813776298,"score_gpt":0.5983361301163364,"score_spread":0.21156384873870654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115740690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038987321,0.0013291196,0.9868348,0.0011044653,0.00013140307,0.00009582643,0.00046916882,0.00025325804,0.005883304],"genre_scores_gemma":[0.3383914,0.0075030443,0.6354309,0.0017347953,0.001551307,0.0016536681,0.0022685998,0.0008446658,0.01062159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964491,0.0022838796,0.00023141058,0.0002761802,0.0006032669,0.0001560851],"domain_scores_gemma":[0.9651101,0.028471215,0.0019860314,0.0015021189,0.0025248807,0.00040567887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009142387,0.0013325531,0.0009770539,0.0023805287,0.0007769209,0.0017994744,0.0017689394,0.001548944,0.007992598],"category_scores_gemma":[0.06626511,0.0005270266,0.0017362342,0.0035506007,0.0014888211,0.0038869756,0.0014044906,0.0038875476,0.0017711172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051241626,0.000074723415,0.003893478,0.00026919405,0.00006164755,0.00038272634,0.00025415543,0.2848316,0.00087787665,0.65153676,0.009677724,0.048088815],"study_design_scores_gemma":[0.000024697742,0.000058249385,0.0009018043,0.00015294741,0.000022975382,0.00025840552,0.000069362904,0.5256852,0.0006471179,0.45926192,0.01286496,0.00005237345],"about_ca_topic_score_codex":0.0029804616,"about_ca_topic_score_gemma":0.0020010455,"teacher_disagreement_score":0.009142387,"about_ca_system_score_codex":0.001339209,"about_ca_system_score_gemma":0.0015494033,"threshold_uncertainty_score":0.048350096},"labels":[],"label_agreement":null},{"id":"W2116181250","doi":"10.6000/1929-6029.2015.04.01.13","title":"Use of Geometric Mean in Bioequivalence Trials","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bioequivalence; Confusion; Logarithm; Normality; Geometric mean; Mathematics; Transformation (genetics); Statistics; Logarithmic scale; Econometrics; Scale (ratio); Medicine; Psychology; Pharmacology; Geography","score_opus":0.9369601174424793,"score_gpt":0.7400837680119565,"score_spread":0.1968763494305228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116181250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063125873,0.02377385,0.9512168,0.0047046198,0.0023082711,0.0006686058,0.00064253586,0.0005747454,0.009798058],"genre_scores_gemma":[0.25697,0.012787229,0.71359414,0.0055979155,0.0024968951,0.0045283027,0.00072775234,0.00062538806,0.002672374],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.691419,0.2618561,0.012664214,0.0120394,0.021127576,0.0008937191],"domain_scores_gemma":[0.7385153,0.2165211,0.015773937,0.019312367,0.009033147,0.000844158],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1651478,0.0016201764,0.004226132,0.0048839203,0.0011259114,0.0042790254,0.0035212962,0.0046534687,0.004907282],"category_scores_gemma":[0.34524113,0.000715376,0.003573374,0.0064724064,0.005929734,0.0048022717,0.0041088094,0.0073345522,0.0016915279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028334612,0.00023794259,0.008806107,0.0067852223,0.0025350354,0.00078406907,0.002317146,0.013502825,0.0027839607,0.40701795,0.02282719,0.5295691],"study_design_scores_gemma":[0.0007020938,0.0043139826,0.008907137,0.0046464885,0.001845974,0.003050764,0.0006518885,0.05503618,0.009831135,0.763739,0.14685594,0.0004194619],"about_ca_topic_score_codex":0.0005881119,"about_ca_topic_score_gemma":0.0004058683,"teacher_disagreement_score":0.8348522,"about_ca_system_score_codex":0.002218788,"about_ca_system_score_gemma":0.002863746,"threshold_uncertainty_score":0.87339544},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W2116793483","doi":"10.6000/1929-6029.2013.02.03.8","title":"Efficiency of Co-Expression of Transcription Factors Pdx1, Ngn3, NeuroD and Pax6 with Insulin: A Statistical Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pancreatic function and diabetes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"PDX1; NeuroD; Insulin; Islet; Biology; Internal medicine; Endocrinology; Transcription factor; Medicine; Gene; Genetics","score_opus":0.05095332832371692,"score_gpt":0.3901186892803647,"score_spread":0.3391653609566478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116793483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6095879,0.0025863366,0.37349868,0.00032182306,0.00042585988,0.0017502349,0.0033354592,0.0018332152,0.006660477],"genre_scores_gemma":[0.8709061,0.00038351998,0.119361304,0.000114459646,0.00008802228,0.0044297776,0.0018370543,0.0004199789,0.0024596937],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97540057,0.007342514,0.0027912313,0.005147696,0.008184193,0.0011338455],"domain_scores_gemma":[0.9614946,0.02735595,0.0049022143,0.0036353949,0.0021875405,0.00042426158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018590717,0.0009248265,0.0022092306,0.0044044266,0.0007772723,0.0018934988,0.0014670227,0.0012462201,0.0041931844],"category_scores_gemma":[0.026448531,0.00049029413,0.0020434035,0.0041762725,0.0021997343,0.0013122094,0.0019957998,0.0022184183,0.00092210236],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019636944,0.0021572674,0.4335509,0.004187433,0.008762148,0.00078860996,0.0039466615,0.0132728815,0.2391209,0.008663665,0.003135782,0.26277685],"study_design_scores_gemma":[0.00044465758,0.015789773,0.5424416,0.00037013122,0.0035581768,0.002335501,0.0030157117,0.1634666,0.23922215,0.0076892357,0.021257829,0.0004086576],"about_ca_topic_score_codex":0.0005652498,"about_ca_topic_score_gemma":0.0004879318,"teacher_disagreement_score":0.018590717,"about_ca_system_score_codex":0.001022884,"about_ca_system_score_gemma":0.0007216762,"threshold_uncertainty_score":0.09831828},"labels":[],"label_agreement":null},{"id":"W2118470117","doi":"10.6000/1929-6029.2013.02.03.3","title":"Analysis of Risk and Protective Factors for Arthritis Status and Severity Using Survey Data","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Rheumatoid Arthritis Research and Therapies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Behavioral Risk Factor Surveillance System; Overweight; Obesity; Arthritis; Logistic regression; Odds ratio; Internal medicine; Alcohol consumption; Risk factor; Demography; Environmental health; Physical therapy; Alcohol; Population","score_opus":0.1186641200878771,"score_gpt":0.46250749794086243,"score_spread":0.34384337785298535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118470117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941988,0.00035422933,0.0008826679,0.000052305666,0.0000075161324,0.000028629995,0.003919815,0.00001348465,0.00054255244],"genre_scores_gemma":[0.9962167,0.00015326646,0.0005788774,0.000020195483,0.0000096343165,0.00003630333,0.0028104698,0.0000034953132,0.0001710103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99796474,0.0009774803,0.00024099977,0.00028554272,0.00032072482,0.00021043941],"domain_scores_gemma":[0.9959484,0.0016306126,0.0014400984,0.00029938217,0.0004363985,0.00024506738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022356152,0.0002790119,0.000352228,0.0021710072,0.00021318208,0.0005268262,0.0003076492,0.00022285577,0.00117408],"category_scores_gemma":[0.0072508715,0.00026209585,0.0011906147,0.0026146104,0.0001225024,0.0003664407,0.0004627476,0.0004049207,0.0001732918],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002431934,0.0000084949925,0.9985567,0.000011262842,0.00013890884,0.00000909401,0.000018509492,0.00007400612,0.000040273633,0.000011973533,0.00005216788,0.0010543686],"study_design_scores_gemma":[0.0000031899592,0.00004999691,0.998401,0.000009791604,0.00007795957,0.000040335075,0.00010299599,0.0009923999,0.000038655184,0.00001771173,0.00026223203,0.0000037659347],"about_ca_topic_score_codex":0.03613863,"about_ca_topic_score_gemma":0.04106394,"teacher_disagreement_score":0.03613863,"about_ca_system_score_codex":0.00028123846,"about_ca_system_score_gemma":0.0005867654,"threshold_uncertainty_score":0.07185656},"labels":[],"label_agreement":null},{"id":"W2119025233","doi":"10.6000/1929-6029.2013.02.02.03","title":"Accounting for the Hierarchical Structure in Veterans Health Administration Data: Differences in Healthcare Utilization between Men and Women Veterans","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; U.S. Department of Veterans Affairs","keywords":"Health care; Logistic regression; Multilevel model; Medicine; Military service; Demography; Computer science; Political science","score_opus":0.288708469300767,"score_gpt":0.4794168716715215,"score_spread":0.19070840237075454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119025233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88519305,0.001084607,0.10326202,0.0020968427,0.00024087177,0.0009916337,0.003609266,0.0003073868,0.0032142128],"genre_scores_gemma":[0.9796229,0.000117639276,0.016894842,0.00021856514,0.000055221408,0.0002797328,0.0021671385,0.00004556994,0.00059825426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9441867,0.041546468,0.0036380915,0.004579511,0.0042089694,0.0018400959],"domain_scores_gemma":[0.81996036,0.12977353,0.018755764,0.023014037,0.006937181,0.0015590985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037331566,0.0006454484,0.0008400284,0.0019310742,0.0013455311,0.0018190147,0.0017778297,0.0006543314,0.0015949925],"category_scores_gemma":[0.15017204,0.0008679558,0.0027476782,0.0043654735,0.0013714051,0.0020182934,0.001802105,0.0015367914,0.00029303823],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015209138,0.00006001123,0.9795394,0.00006076456,0.00081662845,0.000068483234,0.00096579693,0.0038108374,0.00022096834,0.001482682,0.0009276731,0.0118947355],"study_design_scores_gemma":[0.00005706371,0.00043254104,0.9330534,0.00017303432,0.0005317431,0.00016640929,0.0016886261,0.05413233,0.0007692193,0.005786579,0.0031422374,0.000066873225],"about_ca_topic_score_codex":0.097602606,"about_ca_topic_score_gemma":0.09962923,"teacher_disagreement_score":0.097602606,"about_ca_system_score_codex":0.0025184578,"about_ca_system_score_gemma":0.0040186075,"threshold_uncertainty_score":0.19743055},"labels":[],"label_agreement":null},{"id":"W2119902643","doi":"10.6000/1929-6029.2013.02.04.3","title":"Factors Affecting Self-Image in Patients with a Diagnosis of Eating Disorders on the Basis of a Cluster Analysis","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Eating Disorders and Behaviors","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eating disorders; Bulimia nervosa; Anorexia nervosa; Psychology; Self-image; Eating Disorder Inventory; Comorbidity; Depression (economics); Beck Depression Inventory; Anorexia; Cluster (spacecraft); Psychiatry; Clinical psychology; Medicine; Anxiety; Internal medicine","score_opus":0.037683884771847,"score_gpt":0.4167932814381025,"score_spread":0.37910939666625554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119902643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994773,0.000043066044,0.00019992563,0.00002029825,0.0000035722876,0.000015606684,0.000030027866,0.0000028771312,0.00020732451],"genre_scores_gemma":[0.99964,0.000013804037,0.00019891532,0.000003393397,0.0000023195355,0.000007424566,0.00007785745,0.0000012820813,0.000055047425],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99958545,0.00012069701,0.000045968707,0.000072169554,0.00010998375,0.000065699474],"domain_scores_gemma":[0.99909186,0.0002272659,0.0002365866,0.00009354393,0.00019601359,0.00015472168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000702064,0.000225194,0.00035907672,0.0011914235,0.0005114197,0.0006257151,0.00021254073,0.00019133605,0.0011265965],"category_scores_gemma":[0.0024761693,0.000110396846,0.00042050844,0.00067234994,0.00030989884,0.00022159991,0.0006090852,0.00029741327,0.00016745766],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003288041,0.0000515181,0.9943188,0.000011929404,0.00006029536,0.00009633655,0.0005574104,0.00013513849,0.0006044816,0.000053180636,0.00010937838,0.0036727008],"study_design_scores_gemma":[0.000012027501,0.0001944876,0.9960872,0.000008937082,0.00003777004,0.0003743493,0.0016094836,0.0012030548,0.00018726285,0.00010543495,0.00017091731,0.000009201301],"about_ca_topic_score_codex":0.0025529175,"about_ca_topic_score_gemma":0.0026614063,"teacher_disagreement_score":0.0025529175,"about_ca_system_score_codex":0.00028574097,"about_ca_system_score_gemma":0.00039272834,"threshold_uncertainty_score":0.0050761104},"labels":[],"label_agreement":null},{"id":"W2121202398","doi":"10.6000/1929-6029.2014.03.04.2","title":"Application of Survival Tree Based on Texture Features Obtained through MRI of Patients with Brain Metastases from Breast Cancer","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Proportional hazards model; Medicine; Breast cancer; Magnetic resonance imaging; Survival analysis; Cancer; Wavelet; Radiology; Statistics; Artificial intelligence; Internal medicine; Mathematics; Computer science","score_opus":0.015311792806623049,"score_gpt":0.3985411081200439,"score_spread":0.38322931531342086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121202398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9784006,0.00026525185,0.020023683,0.00011228825,0.000016218055,0.000022375578,0.00080078345,0.00007277951,0.00028595547],"genre_scores_gemma":[0.9949698,0.000087956956,0.004016527,0.0000073722044,0.000011445745,0.000014728689,0.00080341985,0.0000062971085,0.00008251302],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99968624,0.00013403136,0.000025292997,0.000049160655,0.00006381713,0.00004142973],"domain_scores_gemma":[0.99843234,0.0009153482,0.00026926014,0.000082253886,0.0002010432,0.00009977107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007895537,0.00027235292,0.00044135723,0.001132944,0.00020336917,0.00040728645,0.00014716786,0.00023837347,0.0008330569],"category_scores_gemma":[0.004534265,0.0000950639,0.00053919636,0.00086328364,0.00012136052,0.00040951473,0.00027286206,0.00026619906,0.00015799435],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017182966,0.00012304666,0.85452265,0.00009283346,0.00020960446,0.000407981,0.0002517058,0.03357221,0.005536704,0.00036216833,0.00081807753,0.10238467],"study_design_scores_gemma":[0.00004592399,0.0011118694,0.6574276,0.00003421583,0.00025559415,0.00087330496,0.00032709978,0.3336128,0.0026837268,0.0019322821,0.00164736,0.000048233836],"about_ca_topic_score_codex":0.0019098796,"about_ca_topic_score_gemma":0.0019370458,"teacher_disagreement_score":0.0019098796,"about_ca_system_score_codex":0.0001991592,"about_ca_system_score_gemma":0.00034564294,"threshold_uncertainty_score":0.0041756034},"labels":[],"label_agreement":null},{"id":"W2121475256","doi":"10.6000/1929-6029.2012.01.02.05","title":"Two-Part Pattern-Mixture Model for Longitudinal Incomplete Semi-Continuous Toenail Data","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Longitudinal data; Mixture model; Binary data; Logistic regression; Statistics; Mathematics; Computer science; Longitudinal study; Continuous modelling; Dropout (neural networks); Mixed model; Binary number; Econometrics; Data mining; Machine learning","score_opus":0.18374716977075786,"score_gpt":0.48186322428504275,"score_spread":0.2981160545142849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121475256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011659573,0.00027841688,0.98657924,0.00026301097,0.000058812864,0.0001061444,0.00046374218,0.00024438708,0.00034669208],"genre_scores_gemma":[0.44895405,0.0012744693,0.53046083,0.00054973783,0.00028590573,0.0021040833,0.0039312523,0.00029654676,0.012143103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938883,0.0032600942,0.00032996983,0.0014467422,0.0007102006,0.00036471282],"domain_scores_gemma":[0.98140234,0.01295039,0.0016789827,0.0021727588,0.0014331453,0.00036244382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012675818,0.0017432194,0.0028353366,0.0022673751,0.00085383415,0.0021172985,0.0052501676,0.003132393,0.005141684],"category_scores_gemma":[0.031199243,0.0014134565,0.0035108577,0.0031231453,0.0021152203,0.0036409723,0.002369174,0.0037470383,0.0016031021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011421604,0.00037778736,0.020702008,0.00079759024,0.0012363556,0.0011883205,0.0015996945,0.4620966,0.0047159023,0.3496244,0.0054306756,0.15108846],"study_design_scores_gemma":[0.000053672447,0.00010999279,0.0024755201,0.000051698313,0.000110915425,0.0001779505,0.00007037846,0.9105481,0.00048543548,0.0835497,0.002298927,0.00006775846],"about_ca_topic_score_codex":0.007384606,"about_ca_topic_score_gemma":0.007052897,"teacher_disagreement_score":0.012675818,"about_ca_system_score_codex":0.0014405396,"about_ca_system_score_gemma":0.0014687455,"threshold_uncertainty_score":0.06703693},"labels":[],"label_agreement":null},{"id":"W2121734380","doi":"10.6000/1929-6029.2015.04.01.8","title":"On the Translation of a Treatment's Effect on Disease Progression Into an Effect on Overall Survival","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tumor progression; Medicine; Hazard ratio; Oncology; Progression-free survival; Bevacizumab; Disease; Internal medicine; Proportional hazards model; Cancer; Overall survival; Chemotherapy; Confidence interval","score_opus":0.58865762749821,"score_gpt":0.6797552771109773,"score_spread":0.09109764961276723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121734380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17722231,0.018568553,0.6668479,0.028835468,0.001292876,0.0012051314,0.0026653223,0.0012542707,0.102108136],"genre_scores_gemma":[0.9256451,0.0060613644,0.052218705,0.0059412452,0.0006097901,0.0012155966,0.00036772783,0.00034681224,0.007593629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9801035,0.015778404,0.00054588565,0.0009926609,0.0019424757,0.00063707575],"domain_scores_gemma":[0.7416783,0.2463705,0.0049761734,0.0047478173,0.0016087315,0.00061848905],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.040892676,0.0015949566,0.0021810348,0.0012688907,0.00040974928,0.0025430275,0.0009967757,0.001861278,0.019459363],"category_scores_gemma":[0.15734254,0.00062949426,0.0027478952,0.0013469908,0.0050047846,0.004366371,0.0021652882,0.004075867,0.0012430163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029488252,0.00039458228,0.007440111,0.0032138797,0.0012967638,0.0005325279,0.0008456518,0.24024671,0.005992042,0.5728782,0.005851997,0.15835868],"study_design_scores_gemma":[0.0008353588,0.0042813607,0.01821052,0.0012264925,0.0013648346,0.00078951893,0.00053405075,0.2522053,0.0069304886,0.698413,0.01505629,0.00015278049],"about_ca_topic_score_codex":0.0016952245,"about_ca_topic_score_gemma":0.00054275116,"teacher_disagreement_score":0.95910734,"about_ca_system_score_codex":0.0022224688,"about_ca_system_score_gemma":0.0022905446,"threshold_uncertainty_score":0.21626371},"labels":[],"label_agreement":null},{"id":"W2122051317","doi":"10.6000/1929-6029.2013.02.03.5","title":"Analysis of Genetic Relationship Among 11 Iranian Ethnic Groups with Bayesian Multidimensional Scaling Using HLA Class II Data","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shiraz University; Shiraz University of Medical Sciences","keywords":"Bayesian probability; Human leukocyte antigen; Class (philosophy); Ethnic group; Multidimensional scaling; Statistics; Mathematics; Computer science; Artificial intelligence; Genetics; Biology; Sociology; Anthropology","score_opus":0.10153332705668816,"score_gpt":0.44065502617221514,"score_spread":0.339121699115527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122051317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8532139,0.00015993272,0.1445222,0.00021165833,0.000017860946,0.00011506369,0.00050618564,0.00013894573,0.0011141868],"genre_scores_gemma":[0.9120378,0.000060563434,0.08715428,0.00001935301,0.000013097461,0.00009834167,0.00047768513,0.00001613414,0.00012280745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99718875,0.001548339,0.00019790784,0.00045273206,0.0005203105,0.00009189248],"domain_scores_gemma":[0.99380034,0.0039143455,0.0008865897,0.0005570236,0.0006534874,0.00018824298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043096924,0.0004328256,0.00044971824,0.0027853914,0.00058024155,0.00081034366,0.00042211355,0.00029685715,0.0010887054],"category_scores_gemma":[0.013549734,0.00017710093,0.00086680613,0.0018903598,0.0004278537,0.0005641752,0.0007005833,0.0005178279,0.00018279905],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004334182,0.00019724166,0.8089798,0.00013172488,0.00066624576,0.00020352434,0.0014185543,0.027151626,0.0063129403,0.0041885027,0.001034276,0.149282],"study_design_scores_gemma":[0.00006791333,0.00032654966,0.4927301,0.00008654248,0.00037130748,0.00057776424,0.0017450689,0.47360677,0.0048050466,0.021837318,0.0037455657,0.0001000962],"about_ca_topic_score_codex":0.0043306034,"about_ca_topic_score_gemma":0.0035739972,"teacher_disagreement_score":0.0043306034,"about_ca_system_score_codex":0.0005583009,"about_ca_system_score_gemma":0.00088000274,"threshold_uncertainty_score":0.02279216},"labels":[],"label_agreement":null},{"id":"W2122276533","doi":"10.6000/1929-6029.2013.02.02.09","title":"SFA vs. DEA for Measuring Healthcare Efficiency: A Systematic Review","year":2013,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; National and Kapodistrian University of Athens; European Commission","keywords":"Data envelopment analysis; Stochastic frontier analysis; Computer science; Process (computing); Measure (data warehouse); Frontier; Health care; Order (exchange); Econometrics; Efficient frontier; Estimation; Risk analysis (engineering); Operations research; Management science; Data mining; Economics; Statistics; Business; Mathematics; Microeconomics","score_opus":0.4591177729516017,"score_gpt":0.6166483589404933,"score_spread":0.15753058598889158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122276533","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007054296,0.99630404,0.0016811282,0.00038566842,0.00008413885,0.0002897183,0.00022721759,0.000008597284,0.00031401482],"genre_scores_gemma":[0.023411116,0.9657552,0.008783741,0.00040364958,0.00009883235,0.0011785344,0.00024424365,0.000009888114,0.000114769835],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9673674,0.016730875,0.008639178,0.00155665,0.0053243246,0.0003816369],"domain_scores_gemma":[0.88269824,0.103597224,0.0070147943,0.0013919759,0.005003077,0.00029472442],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037742984,0.00254823,0.011346305,0.023114245,0.00077674736,0.0047187926,0.0021100773,0.002504816,0.0040867403],"category_scores_gemma":[0.110756375,0.0010482541,0.017658768,0.023832012,0.0012147306,0.004434388,0.0019078754,0.0020491828,0.00036048813],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027476094,0.000050196893,0.0020969678,0.74213105,0.022634286,0.000088883215,0.00024535778,0.0016999765,0.00014828067,0.00471766,0.0020328173,0.22387971],"study_design_scores_gemma":[0.00043022784,0.00054016564,0.0065141413,0.83715385,0.10389807,0.0005011157,0.00074918836,0.0023148493,0.00045655874,0.008645846,0.038665343,0.00013061878],"about_ca_topic_score_codex":0.006908332,"about_ca_topic_score_gemma":0.015114485,"teacher_disagreement_score":0.962257,"about_ca_system_score_codex":0.0062882896,"about_ca_system_score_gemma":0.012933402,"threshold_uncertainty_score":0.19960636},"labels":[],"label_agreement":null},{"id":"W2124079768","doi":"10.6000/1929-6029.2014.03.02.9","title":"An Independent and External Validation of the ACC NCDR Bleeding Risk Score among a National Multi-Site Community Hospital Registry of Cardiac Interventions","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Acute Myocardial Infarction Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bivalirudin; Medicine; Conventional PCI; Receiver operating characteristic; Framingham Risk Score; Incidence (geometry); Psychological intervention; Risk assessment; Retrospective cohort study; Internal medicine; Emergency medicine; Cardiology; Myocardial infarction","score_opus":0.0900324590758376,"score_gpt":0.4647093723979626,"score_spread":0.37467691332212505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124079768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98686755,0.00017912276,0.004427933,0.000115116774,0.00004011195,0.00061379594,0.005981895,0.00010469263,0.0016697949],"genre_scores_gemma":[0.98170125,0.000063066225,0.005347461,0.00008752397,0.000061864936,0.00056466117,0.011853416,0.000038488026,0.0002821377],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97644573,0.012547875,0.0025547876,0.0035009102,0.0042118635,0.00073886075],"domain_scores_gemma":[0.91716486,0.024486082,0.017388938,0.018894203,0.019794704,0.0022712527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027032832,0.000677868,0.00066394854,0.00309757,0.00067397754,0.0014190638,0.001516506,0.0009427116,0.0013289086],"category_scores_gemma":[0.06456441,0.0003495912,0.0008548944,0.002784634,0.0008142693,0.0008834485,0.002055748,0.0008118365,0.0010065957],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035549238,0.00017568661,0.990448,0.000033394936,0.00015140395,0.00004032819,0.00018506471,0.00036732663,0.00035723782,0.000079234385,0.0012009224,0.0066059553],"study_design_scores_gemma":[0.00014278808,0.00026059314,0.99383545,0.000030807812,0.00008803046,0.00030186062,0.00010063043,0.0036207251,0.0003937024,0.00005970229,0.0011497393,0.000016024596],"about_ca_topic_score_codex":0.0051795165,"about_ca_topic_score_gemma":0.0070438203,"teacher_disagreement_score":0.027032832,"about_ca_system_score_codex":0.00096390635,"about_ca_system_score_gemma":0.0017237788,"threshold_uncertainty_score":0.14296502},"labels":[],"label_agreement":null},{"id":"W2124721134","doi":"10.6000/1929-6029.2013.02.04.7","title":"Longitudinal Data Analysis of Symptom Score Trajectories Using Linear Mixed Models in a Clinical Trial","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Longitudinal data; Clinical trial; Mixed model; Generalized linear mixed model; Longitudinal study; Linear model; Flow chart; Repeated measures design; Statistics; Chart; Computer science; Sample size determination; Mathematics; Data mining; Medicine","score_opus":0.9085991657182378,"score_gpt":0.7010326407567958,"score_spread":0.207566524961442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124721134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03582904,0.0037413705,0.94283473,0.0015029471,0.0007974834,0.011275689,0.0017509615,0.0010538728,0.0012138535],"genre_scores_gemma":[0.2046456,0.0018632334,0.7168436,0.00086222315,0.00035985664,0.071764685,0.0014830879,0.00020638251,0.0019712981],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.79798764,0.18780105,0.0048106564,0.004728136,0.0038317887,0.00084062683],"domain_scores_gemma":[0.83410233,0.1452352,0.007685832,0.009658707,0.0027553344,0.00056257134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17233671,0.0016538571,0.004779997,0.0023096083,0.0009015765,0.00259428,0.0023257302,0.0025972137,0.0065591265],"category_scores_gemma":[0.18028118,0.00091352983,0.005681517,0.0036407958,0.0014639671,0.0022413244,0.002266565,0.004184429,0.0010037443],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.04982247,0.004152853,0.064720005,0.016513806,0.030363452,0.0014238023,0.0031635119,0.13848503,0.0055227824,0.1122798,0.019482575,0.5540699],"study_design_scores_gemma":[0.01033599,0.056300063,0.029542413,0.0027109843,0.013930609,0.00076991925,0.0009981042,0.6744826,0.008236001,0.1657365,0.03631012,0.0006467391],"about_ca_topic_score_codex":0.0011077789,"about_ca_topic_score_gemma":0.0010727696,"teacher_disagreement_score":0.17233671,"about_ca_system_score_codex":0.0013720845,"about_ca_system_score_gemma":0.0031110041,"threshold_uncertainty_score":0.91141456},"labels":[],"label_agreement":null},{"id":"W2124887300","doi":"10.6000/1929-6029.2014.03.02.1","title":"Biologic Therapy for Psoriatic Arthritis or Moderate to Severe Plaque Psoriasis: Systematic Review with Pairwise and Network Meta-Analysis","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Psoriasis: Treatment and Pathogenesis","field":"Immunology and Microbiology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Psoriatic arthritis; Golimumab; Psoriasis; Placebo; Internal medicine; Adverse effect; Meta-analysis; Randomized controlled trial; Infliximab; Ustekinumab; Etanercept; Psoriasis Area and Severity Index; Arthritis; Physical therapy; Rheumatoid arthritis; Dermatology; Alternative medicine; Pathology","score_opus":0.10216470467691911,"score_gpt":0.3950385845504263,"score_spread":0.29287387987350716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124887300","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070354845,0.9865551,0.003285335,0.00027346268,0.00022764641,0.00064027414,0.001421244,0.00008670504,0.000474861],"genre_scores_gemma":[0.39226124,0.58814734,0.011219466,0.0010248391,0.00042472142,0.0037187387,0.0024831628,0.000108937864,0.0006114956],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.977088,0.015956165,0.0032759162,0.0018129688,0.001541324,0.00032558537],"domain_scores_gemma":[0.9722242,0.02228971,0.0032125125,0.0009207839,0.001118008,0.00023490458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023663424,0.0034266198,0.01833561,0.008019649,0.00053272664,0.0024048977,0.002424423,0.001532458,0.004634685],"category_scores_gemma":[0.04609205,0.0014931927,0.04079251,0.008291061,0.00062154356,0.0020272986,0.0019379513,0.0020226354,0.00042022616],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016039232,0.000019748959,0.002261162,0.13691863,0.8504351,0.00007164003,0.00003484723,0.0014632312,0.00009017684,0.00015942127,0.0005324564,0.006409705],"study_design_scores_gemma":[0.0008675376,0.0001420891,0.001791651,0.007549794,0.98754907,0.00006559049,0.000016476926,0.0006603952,0.00005159925,0.00040973397,0.00087738514,0.000018782368],"about_ca_topic_score_codex":0.0063798516,"about_ca_topic_score_gemma":0.011724604,"teacher_disagreement_score":0.023663424,"about_ca_system_score_codex":0.0025326961,"about_ca_system_score_gemma":0.002185254,"threshold_uncertainty_score":0.12514561},"labels":[],"label_agreement":null},{"id":"W2125715396","doi":"10.6000/1929-6029.2013.02.03.2","title":"Prediction of Childhood Asthma Using Conditional Probability and Discrete Event Simulation","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Asthma and respiratory diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Asthma; Conditional probability; Medicine; Environmental health; Population; Tobacco smoke; Incidence (geometry); Demography; Pediatrics; Statistics; Mathematics; Immunology","score_opus":0.06320896893309028,"score_gpt":0.43407296859876593,"score_spread":0.3708639996656756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125715396","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6983437,0.00052404474,0.2927201,0.00074790575,0.00010938531,0.00020981248,0.0019905297,0.0007859386,0.004568651],"genre_scores_gemma":[0.9793032,0.00018564597,0.018612267,0.000035031127,0.000019795061,0.000116152674,0.0009228729,0.000020455547,0.0007846275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989666,0.0005757861,0.000061143735,0.00016139138,0.00009939109,0.00013577612],"domain_scores_gemma":[0.983353,0.014567586,0.00073410536,0.00033706648,0.00064739824,0.00036088057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002676783,0.0006053388,0.0009206736,0.0010999427,0.0003069923,0.0010114431,0.0009865331,0.001093333,0.0020423809],"category_scores_gemma":[0.011000661,0.00040836123,0.00096811977,0.00080166716,0.00052219944,0.0005517448,0.0007551602,0.0009182517,0.00017462409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038038906,0.000018166436,0.003079571,0.000009477685,0.000012980912,0.000018694082,0.000009599122,0.9946261,0.000037096128,0.0009213496,0.00008446104,0.0011445705],"study_design_scores_gemma":[0.000006433764,0.000009068503,0.0004289692,0.0000023797415,0.0000032328978,0.0000042036413,0.0000044309663,0.9989403,0.00003706311,0.0005136915,0.000047441983,0.000002821995],"about_ca_topic_score_codex":0.036018614,"about_ca_topic_score_gemma":0.011724239,"teacher_disagreement_score":0.036018614,"about_ca_system_score_codex":0.0012223258,"about_ca_system_score_gemma":0.0013723575,"threshold_uncertainty_score":0.0716179},"labels":[],"label_agreement":null},{"id":"W2126861252","doi":"10.6000/1929-6029.2012.01.01.04","title":"Relationship Between Education and Hospital Visit","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Negative binomial distribution; Health education; Psychology; Medicine; Actuarial science; Nursing; Public health; Statistics; Economics; Mathematics","score_opus":0.24767369128243033,"score_gpt":0.593731524742841,"score_spread":0.34605783346041064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126861252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952931,0.00047013053,0.00014548367,0.0005486469,0.000019775563,0.000006693238,0.00030331817,0.000009792825,0.0032030777],"genre_scores_gemma":[0.9989519,0.000080531245,0.00005428146,0.000045275076,0.0000116319925,0.00000211519,0.00012829535,0.0000017460948,0.0007241501],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999408,0.000192105,0.000062912106,0.00007957328,0.00012024399,0.0001371812],"domain_scores_gemma":[0.9896263,0.004652534,0.0033627301,0.00023091622,0.00046165366,0.0016659459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049199216,0.00009587102,0.00019282373,0.00062346406,0.00031000195,0.00061305193,0.0003654874,0.00054333836,0.011167063],"category_scores_gemma":[0.0070346813,0.000120789846,0.00046241094,0.0005792988,0.00026001383,0.00037543537,0.00030860835,0.0015145304,0.00054129376],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005591224,0.000097816686,0.9979069,0.000010086557,0.000040087536,0.00005657186,0.000039919105,0.00006568605,0.000045045716,0.00006729739,0.00013966781,0.001474909],"study_design_scores_gemma":[0.0000023066025,0.000060476752,0.9990903,0.000008855596,0.000017322964,0.00014647083,0.00015458606,0.00022529255,0.000026142458,0.00004513907,0.00021911702,0.0000039550673],"about_ca_topic_score_codex":0.0067802854,"about_ca_topic_score_gemma":0.009184355,"teacher_disagreement_score":0.011167063,"about_ca_system_score_codex":0.00042929358,"about_ca_system_score_gemma":0.00039432631,"threshold_uncertainty_score":0.03735751},"labels":[],"label_agreement":null},{"id":"W2127310938","doi":"10.6000/1929-6029.2014.03.02.10","title":"Testing the Equivalence of Survival Distributions using PP- and PPP-Plots","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Wilcoxon signed-rank test; Equivalence (formal languages); Null hypothesis; Hazard ratio; Log-rank test; Plot (graphics); Survival analysis; Population; Hazard; Mann–Whitney U test; Demography; Discrete mathematics; Confidence interval; Biology","score_opus":0.17419176540506606,"score_gpt":0.4780635370323231,"score_spread":0.3038717716272571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127310938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06709975,0.0005659215,0.9260363,0.0004299865,0.00009684601,0.00024486377,0.00076534756,0.00085198367,0.0039091264],"genre_scores_gemma":[0.74017334,0.0005199868,0.25459132,0.0002627001,0.00014321154,0.00092587195,0.0020315337,0.0003641947,0.0009878817],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97965723,0.013003455,0.0012848858,0.0022812744,0.0032733916,0.00049982715],"domain_scores_gemma":[0.8042856,0.17147669,0.007886825,0.008196435,0.00700868,0.0011457121],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03302148,0.00085572863,0.0013931426,0.0043933373,0.0007325051,0.0035058784,0.0012680546,0.0017426726,0.0056081796],"category_scores_gemma":[0.21148309,0.0003783065,0.0012896936,0.0027507453,0.0034594745,0.005132185,0.0038237732,0.0029446923,0.00094380305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002548583,0.00031146142,0.12468918,0.001129863,0.00074128754,0.0013219139,0.003796165,0.11527435,0.006444338,0.34526098,0.0072931577,0.39118868],"study_design_scores_gemma":[0.00021333968,0.0013789571,0.046194136,0.00041590055,0.00018743407,0.0018552206,0.0018167973,0.41200393,0.005647556,0.51690584,0.01314327,0.00023763903],"about_ca_topic_score_codex":0.00097522035,"about_ca_topic_score_gemma":0.00038181897,"teacher_disagreement_score":0.96697855,"about_ca_system_score_codex":0.00081249746,"about_ca_system_score_gemma":0.0008980916,"threshold_uncertainty_score":0.17463636},"labels":[],"label_agreement":null},{"id":"W2128379663","doi":"10.6000/1929-6029.2014.03.03.1","title":"Comparison of Methods for Clustered Data Analysis in a Non-Ideal Situation: Results from an Evaluation of Predictors of Yellow Fever Vaccine Refusal in the Global TravEpiNet (GTEN) Consortium","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; National Institutes of Health; Georgetown University; Johns Hopkins University; Kaiser Permanente; Northwestern University; Emory University; Tulane University; University of Southern California","keywords":"Cluster analysis; Statistics; Logistic regression; Random effects model; Generalized estimating equation; Standard error; Odds ratio; Econometrics; Cluster (spacecraft); Sample size determination; Mathematics; Population; Sample (material); Medicine; Computer science; Environmental health; Meta-analysis; Internal medicine","score_opus":0.20683399517557813,"score_gpt":0.5949867239918685,"score_spread":0.38815272881629037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128379663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32192668,0.003138588,0.66165817,0.0021212043,0.000559187,0.0056359456,0.0016166717,0.0008119357,0.0025316544],"genre_scores_gemma":[0.50553274,0.00044204097,0.4868624,0.0003843675,0.00006760833,0.0050230706,0.0009390754,0.00042048306,0.00032819255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.41784957,0.54583406,0.013309388,0.010268498,0.011729498,0.0010089477],"domain_scores_gemma":[0.25627977,0.6763219,0.01525553,0.032405272,0.01818295,0.0015545775],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.36655727,0.0015292467,0.0022606656,0.0035110281,0.0017423648,0.0028855542,0.0041003264,0.0019777128,0.0020866443],"category_scores_gemma":[0.60395944,0.0012343792,0.006922888,0.0043820515,0.0028829954,0.0033131533,0.005164719,0.00249645,0.00034682595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.022888714,0.002542826,0.3672021,0.008429353,0.052912455,0.0008534756,0.019938001,0.13669366,0.0014335667,0.055815157,0.015209039,0.31608164],"study_design_scores_gemma":[0.00852746,0.0109479185,0.20716034,0.0039498243,0.0074681714,0.0011929445,0.008496871,0.6560908,0.0037272354,0.0770663,0.014445616,0.0009265593],"about_ca_topic_score_codex":0.0084108375,"about_ca_topic_score_gemma":0.0070737954,"teacher_disagreement_score":0.63344276,"about_ca_system_score_codex":0.003751509,"about_ca_system_score_gemma":0.006472179,"threshold_uncertainty_score":0.78114766},"labels":[],"label_agreement":null},{"id":"W2128847823","doi":"10.6000/1929-6029.2015.04.01.1","title":"Prediction and Identification of Covariates of Intra-cerebral Hemorrhage","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Covariate; Internal medicine; Univariate analysis; Stroke (engine); Statistical significance; Cardiology; Multivariate analysis; Statistics","score_opus":0.0716946575571897,"score_gpt":0.4178897695267511,"score_spread":0.34619511196956143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128847823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99416286,0.000341453,0.004251798,0.0003731589,0.000045575674,0.000020611446,0.00038367976,0.000034386972,0.0003863773],"genre_scores_gemma":[0.9984261,0.000078193625,0.00094435224,0.000025171392,0.000023208904,0.000011973589,0.00029127387,0.000005065988,0.00019459135],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99723154,0.0018546326,0.00013711474,0.00034193808,0.0002318168,0.00020299778],"domain_scores_gemma":[0.9902592,0.006080514,0.0017582247,0.00081795454,0.00039592362,0.0006881991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004056258,0.000674825,0.0005424607,0.0007477244,0.00028398298,0.0010143478,0.00058092416,0.00087180425,0.0015036487],"category_scores_gemma":[0.015734222,0.0002913445,0.0018162505,0.0007290186,0.00029983837,0.00062033214,0.00078642357,0.0019051641,0.00041013065],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035650402,0.000092130984,0.9941598,0.000012790969,0.0002156577,0.000069295726,0.000038856404,0.001060578,0.00013308629,0.00012822413,0.00015646413,0.003576559],"study_design_scores_gemma":[0.000048857015,0.0009180285,0.94326645,0.000032492073,0.00028419867,0.00037864727,0.00014627104,0.052785564,0.00043935978,0.00092399376,0.00075156527,0.000024577957],"about_ca_topic_score_codex":0.0017659602,"about_ca_topic_score_gemma":0.0012453207,"teacher_disagreement_score":0.004056258,"about_ca_system_score_codex":0.00023392831,"about_ca_system_score_gemma":0.00082669983,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2132259143","doi":"10.6000/1929-6029.2014.03.04.8","title":"LQAS in Health Monitoring – Insights from a Bayesian Perspective","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lot quality assurance sampling; Bayesian probability; Bayesian hierarchical modeling; Public health; Perspective (graphical); Computer science; Bayesian inference; Data mining; Environmental health; Geography; Medicine; Population; Artificial intelligence; Sampling design","score_opus":0.08280691942839997,"score_gpt":0.4416605731710354,"score_spread":0.35885365374263545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132259143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011575444,0.0030506467,0.96817744,0.009610932,0.000119483484,0.00008827536,0.00024508283,0.0001493303,0.006983419],"genre_scores_gemma":[0.6109697,0.0072682872,0.37313727,0.002196001,0.0012315959,0.00060196966,0.00046825435,0.00014764618,0.0039792815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9759779,0.019655049,0.00067857496,0.0015305054,0.0017557666,0.00040222067],"domain_scores_gemma":[0.84443796,0.1373706,0.006423838,0.0046397354,0.0060594752,0.0010683534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049452227,0.00096785097,0.00223432,0.0036371963,0.001088808,0.0040950547,0.0031846974,0.002869803,0.0028445958],"category_scores_gemma":[0.14167468,0.0010715231,0.0016071016,0.002915879,0.0053578606,0.0077517363,0.002833595,0.0046569975,0.00040394915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043507403,0.00004533236,0.0058482243,0.00018766441,0.00016512262,0.00014180453,0.00069423986,0.10602146,0.00014797489,0.8426405,0.001832047,0.042231977],"study_design_scores_gemma":[0.000015710662,0.000035270867,0.0011761144,0.000118839656,0.000031047213,0.00007063999,0.000151118,0.21446067,0.00007711466,0.7806521,0.0031797306,0.000031708816],"about_ca_topic_score_codex":0.019949721,"about_ca_topic_score_gemma":0.009926174,"teacher_disagreement_score":0.049452227,"about_ca_system_score_codex":0.003607428,"about_ca_system_score_gemma":0.0026003923,"threshold_uncertainty_score":0.26153153},"labels":[],"label_agreement":null},{"id":"W2133303102","doi":"10.6000/1929-6029.2012.01.02.06","title":"Ontology Based Statistical Automated Inference - New Approach to Artificial Intelligence","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Ontology; Bridging (networking); Inference; Semantic Web; Natural language processing; Statistical inference; Knowledge representation and reasoning; Artificial intelligence; Ontology Inference Layer; Information retrieval; OWL-S; Social Semantic Web; Mathematics; Statistics","score_opus":0.20805800181012954,"score_gpt":0.5030199476678145,"score_spread":0.294961945857685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133303102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057065126,0.0009981396,0.9934196,0.0011015342,0.00011113044,0.00004407326,0.00015554216,0.00036702066,0.0032323739],"genre_scores_gemma":[0.083401345,0.004485233,0.90530705,0.00095255807,0.0008843174,0.00036633684,0.00082163187,0.00027977367,0.00350168],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923649,0.0031168126,0.0007057138,0.0011906333,0.00242481,0.00019716696],"domain_scores_gemma":[0.9845977,0.010150869,0.00068862387,0.0029743826,0.0013375279,0.0002508548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009028104,0.0012398822,0.0025220395,0.0065182275,0.0012964618,0.008186016,0.0045336173,0.0021884474,0.003965169],"category_scores_gemma":[0.018460264,0.0011201553,0.004044811,0.0051071714,0.00645011,0.011049556,0.005100636,0.004442787,0.0012711146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023121873,0.00006430409,0.0007192455,0.0004957272,0.00025233906,0.00023867094,0.00037838263,0.020989958,0.00051851175,0.88639104,0.004397201,0.085531406],"study_design_scores_gemma":[0.000007052409,0.0000090266485,0.00012967983,0.00006879387,0.000028418526,0.00008383218,0.000054194043,0.06280578,0.0002517479,0.9224779,0.014062646,0.000020993291],"about_ca_topic_score_codex":0.003394855,"about_ca_topic_score_gemma":0.0027967773,"teacher_disagreement_score":0.009028104,"about_ca_system_score_codex":0.002428279,"about_ca_system_score_gemma":0.0034598163,"threshold_uncertainty_score":0.047745764},"labels":[],"label_agreement":null},{"id":"W2136099024","doi":"10.6000/1929-6029.2015.04.01.5","title":"Age Scale for Assessing Activities of Daily Living","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Activities of daily living; Spearman's rank correlation coefficient; Gerontology; Medicine; Rank correlation; Grip strength; Psychology; Correlation; Physical therapy; Statistics; Mathematics","score_opus":0.174522116532733,"score_gpt":0.5656896710771893,"score_spread":0.3911675545444563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136099024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.611932,0.040129658,0.05181476,0.003845839,0.0029394797,0.014853178,0.06334075,0.0013136917,0.20983067],"genre_scores_gemma":[0.78679657,0.015001405,0.0916605,0.002004668,0.00052754674,0.0107359355,0.03322308,0.00012263986,0.059927642],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99897146,0.00022706631,0.00023286951,0.0000865904,0.00041082414,0.000071265364],"domain_scores_gemma":[0.9988819,0.00013308563,0.00025599552,0.00006076444,0.000573028,0.000095251984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014470683,0.0006188036,0.0005147981,0.0016421339,0.00041385525,0.0004256362,0.00068554556,0.0003801992,0.0047801584],"category_scores_gemma":[0.003088918,0.00010826584,0.00067657913,0.00087531254,0.00017924164,0.0007248748,0.00096886023,0.0008094596,0.0023809026],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009768168,0.0007052589,0.41550267,0.0014746272,0.0005474243,0.0004895007,0.0019524518,0.0010273128,0.005159637,0.0060394625,0.09049372,0.47563124],"study_design_scores_gemma":[0.00016572395,0.0014210157,0.7189216,0.00057858595,0.00018116686,0.0031102519,0.0012235905,0.0013025785,0.0009937825,0.0032244893,0.26878378,0.00009352488],"about_ca_topic_score_codex":0.0018514919,"about_ca_topic_score_gemma":0.005063274,"teacher_disagreement_score":0.0047801584,"about_ca_system_score_codex":0.00050237216,"about_ca_system_score_gemma":0.000579825,"threshold_uncertainty_score":0.015991151},"labels":[],"label_agreement":null},{"id":"W2136124010","doi":"10.6000/1929-6029.2015.04.01.6","title":"Estimating Mean and Standard Deviation from the Sample Size, Three Quartiles, Minimum, and Maximum","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Standard deviation; Quartile; Statistics; Mathematics; Log-normal distribution; Sample size determination; Sample mean and sample covariance; Standard error; Sample (material); Absolute deviation; Confidence interval","score_opus":0.14119885989226724,"score_gpt":0.5093025280025928,"score_spread":0.3681036681103256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136124010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011954674,0.02965857,0.94679946,0.0036103446,0.0011491189,0.0007313434,0.0022462648,0.0005904961,0.0032597077],"genre_scores_gemma":[0.20385063,0.013233905,0.7667361,0.0030673472,0.0017323748,0.0065522604,0.002870602,0.00048563667,0.0014711955],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.86358774,0.10068749,0.0130050415,0.009958534,0.0121121565,0.00064894144],"domain_scores_gemma":[0.5340053,0.41541195,0.018922847,0.023195678,0.0077942284,0.0006700919],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.10140572,0.0017689815,0.004147998,0.005799848,0.00076400983,0.00372925,0.0036844062,0.002898105,0.004471011],"category_scores_gemma":[0.4006602,0.0009178796,0.007744019,0.0066486793,0.003880204,0.0042089717,0.003140393,0.0049395273,0.0009927966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002524886,0.00024328462,0.06383356,0.028596688,0.027815798,0.000688781,0.0025872146,0.04529316,0.003391049,0.1492111,0.042214222,0.63360035],"study_design_scores_gemma":[0.0010899475,0.0018916338,0.042956803,0.010370435,0.014069947,0.002031279,0.000750979,0.064043276,0.00925056,0.7530801,0.09969568,0.00076922943],"about_ca_topic_score_codex":0.0018064375,"about_ca_topic_score_gemma":0.0012234173,"teacher_disagreement_score":0.89859426,"about_ca_system_score_codex":0.0017914949,"about_ca_system_score_gemma":0.003657752,"threshold_uncertainty_score":0.5362911},"labels":[],"label_agreement":null},{"id":"W2137459451","doi":"10.6000/1929-6029.2014.03.04.7","title":"Avoiding Inferential Errors in Public Health Research: The Statistical Modelling of Physical Activity Behavior","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Negative binomial distribution; Ordinary least squares; Statistics; Mathematics; Regression analysis; Econometrics; Linear regression; Count data; Binomial regression; Variables; Regression; Statistical model","score_opus":0.36569130093483876,"score_gpt":0.5285389190900147,"score_spread":0.16284761815517595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137459451","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011288758,0.011876832,0.95318526,0.016566047,0.00162436,0.0011294934,0.0005529203,0.00038497636,0.0033913753],"genre_scores_gemma":[0.3410454,0.0140347835,0.628127,0.0059397393,0.0031035403,0.0060409023,0.000633226,0.00029549652,0.0007799416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.3942001,0.5607876,0.013216769,0.010558431,0.020111304,0.0011256974],"domain_scores_gemma":[0.088373885,0.8589719,0.018566588,0.025141742,0.008233862,0.00071193575],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4045574,0.0029884954,0.0047963685,0.009495341,0.0022935998,0.008712575,0.006604753,0.0061235647,0.0022861417],"category_scores_gemma":[0.74203306,0.002214801,0.0039782943,0.01359932,0.022417467,0.0086039,0.0075419284,0.0089702485,0.000746663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056573376,0.000539673,0.08502796,0.014827598,0.0074151596,0.0014417353,0.014415092,0.07362068,0.00089209934,0.46038797,0.013680865,0.32718542],"study_design_scores_gemma":[0.00012047615,0.00046505203,0.012044019,0.00511966,0.0006759846,0.0005066342,0.0015038787,0.109106846,0.0008345485,0.8558109,0.0136198625,0.00019212451],"about_ca_topic_score_codex":0.008497151,"about_ca_topic_score_gemma":0.0052097635,"teacher_disagreement_score":0.5954426,"about_ca_system_score_codex":0.0045990865,"about_ca_system_score_gemma":0.020943513,"threshold_uncertainty_score":0.7342867},"labels":[],"label_agreement":null},{"id":"W2140739283","doi":"10.6000/1929-6029.2013.02.01.03","title":"A Dynamical Study of Risk Factors in Intracerebral Hemorrhage using Multivariate Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Intracerebral hemorrhage; Covariate; Logistic regression; Internal medicine; Univariate analysis; Multivariate statistics; Multivariate analysis; Antihypertensive drug; Univariate; Blood pressure; Statistics","score_opus":0.06296483867815934,"score_gpt":0.4252318630209623,"score_spread":0.36226702434280295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140739283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93590766,0.0008778795,0.060287178,0.0010834056,0.00009689502,0.00004703683,0.0003884904,0.00016167916,0.0011498149],"genre_scores_gemma":[0.9950604,0.00021469189,0.0036095774,0.000026042942,0.00006697583,0.000027968606,0.00022191713,0.000020910133,0.0007514172],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977325,0.0014928617,0.000053365966,0.00028551687,0.00016583143,0.00026984388],"domain_scores_gemma":[0.9930443,0.0049991813,0.00070762285,0.00039649458,0.00033598734,0.0005163751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037462264,0.0008201427,0.0007502854,0.0014355294,0.0005890909,0.0013526923,0.0007663505,0.0004894392,0.0021441681],"category_scores_gemma":[0.01167418,0.00034704772,0.0021435176,0.0006945096,0.00051725743,0.00075614924,0.0009426681,0.0012771958,0.00022489125],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005515325,0.00021579834,0.90815747,0.00005453287,0.0018726897,0.00085844425,0.0003860703,0.0505844,0.0011284042,0.008753797,0.001242752,0.026194207],"study_design_scores_gemma":[0.00003164429,0.0003618397,0.20404994,0.000035203953,0.00027656803,0.00044507848,0.00030294995,0.787708,0.00025032443,0.0055370606,0.00095755124,0.0000438346],"about_ca_topic_score_codex":0.011162884,"about_ca_topic_score_gemma":0.005004148,"teacher_disagreement_score":0.011162884,"about_ca_system_score_codex":0.00044023493,"about_ca_system_score_gemma":0.0008674291,"threshold_uncertainty_score":0.022195816},"labels":[],"label_agreement":null},{"id":"W2141410053","doi":"10.6000/1929-6029.2013.02.04.6","title":"Modified Kaplan-Meier Estimator Based on Competing Risks for Heavy Censoring Data","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Kaplan–Meier estimator; Estimator; Survival analysis; Statistics; Survival function; Proportional hazards model; Econometrics; Mathematics; Medicine","score_opus":0.465653726966659,"score_gpt":0.5860725092309494,"score_spread":0.12041878226429042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141410053","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008416375,0.0017602968,0.9885278,0.00019074148,0.00007286909,0.00016866847,0.00019174513,0.0002617683,0.00040980638],"genre_scores_gemma":[0.2614358,0.0027799117,0.7300919,0.00023843275,0.00036800563,0.0013513084,0.001346144,0.00018359546,0.0022048366],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916642,0.005832033,0.00042455716,0.00080219447,0.0010358664,0.0002411669],"domain_scores_gemma":[0.9782541,0.01713316,0.0016392374,0.0013429477,0.0013997334,0.00023072617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013981364,0.00085984886,0.0013893639,0.0025323238,0.00040366678,0.0010484367,0.002169379,0.0011980252,0.00236465],"category_scores_gemma":[0.04366107,0.00038914505,0.0014881933,0.0017190679,0.00050375436,0.0021416345,0.0012719975,0.002025157,0.00046591536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011209894,0.00027163763,0.045974102,0.0020549006,0.0018339126,0.0009778036,0.0013847727,0.1678463,0.0055757454,0.19068274,0.013999183,0.56827796],"study_design_scores_gemma":[0.0003316207,0.00084629585,0.01641802,0.00031130813,0.0006201159,0.0026452802,0.0002731267,0.8087779,0.003631027,0.13992888,0.025942575,0.00027394775],"about_ca_topic_score_codex":0.002532077,"about_ca_topic_score_gemma":0.0015489329,"teacher_disagreement_score":0.013981364,"about_ca_system_score_codex":0.00066005887,"about_ca_system_score_gemma":0.0017654729,"threshold_uncertainty_score":0.07394135},"labels":[],"label_agreement":null},{"id":"W2141493353","doi":"10.6000/1929-6029.2014.03.03.9","title":"Conditional Two Level Mixture with Known Mixing Proportions: Applications to School and Student Level Overweight and Obesity Data from Birmingham, England","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Oxford","keywords":"Overweight; Statistics; Mixing (physics); Bayesian probability; Mathematics; Parametric statistics; Outcome (game theory); Variance (accounting); Maximization; Random effects model; Econometrics; Computer science; Body mass index; Mathematical optimization; Medicine; Meta-analysis","score_opus":0.25475424502420435,"score_gpt":0.5347593854659777,"score_spread":0.2800051404417733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141493353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6360621,0.0009209783,0.35364035,0.0010699646,0.00005805799,0.00034431962,0.005512993,0.00078146544,0.0016098293],"genre_scores_gemma":[0.8383215,0.00021734241,0.15042506,0.00011887482,0.000034518023,0.0003635256,0.009034907,0.00011230203,0.0013719248],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99425215,0.0043014647,0.00028768263,0.0006100949,0.00035930227,0.00018934297],"domain_scores_gemma":[0.9557038,0.03650177,0.0018434279,0.003704268,0.0018161017,0.0004305632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014960508,0.00042368763,0.0010746757,0.0017630048,0.00087723666,0.0011850472,0.0019081552,0.0014621364,0.0034178866],"category_scores_gemma":[0.06373505,0.0005515542,0.0013455452,0.0033028822,0.0009520507,0.0011177761,0.0023824286,0.0019030826,0.0004483126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001965703,0.00042924765,0.44429258,0.0007458082,0.0009448669,0.0011887803,0.004819816,0.33374837,0.002473459,0.06460096,0.008351149,0.13643928],"study_design_scores_gemma":[0.00021168479,0.00014677136,0.1625401,0.00010302004,0.000140836,0.0003322716,0.00091720774,0.78592235,0.0009047069,0.042885516,0.0057565304,0.00013903111],"about_ca_topic_score_codex":0.060879733,"about_ca_topic_score_gemma":0.06320789,"teacher_disagreement_score":0.060879733,"about_ca_system_score_codex":0.0017254287,"about_ca_system_score_gemma":0.0009470395,"threshold_uncertainty_score":0.121050775},"labels":[],"label_agreement":null},{"id":"W2143330673","doi":"10.6000/1929-6029.2013.02.02.07","title":"Predictive Models for the Management of Vesicoureteral Reflux from the View of Statisticians","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pediatric Urology and Nephrology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vesicoureteral reflux; Outcome (game theory); Medicine; Intensive care medicine; Disease; Internal medicine; Reflux","score_opus":0.0811378152809248,"score_gpt":0.4428944430911506,"score_spread":0.36175662781022583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143330673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13259134,0.010122102,0.83888984,0.009229107,0.0005207459,0.00020664778,0.002217729,0.0010575252,0.0051649334],"genre_scores_gemma":[0.90611845,0.0057096872,0.08112133,0.0006475658,0.0007612902,0.00036511745,0.002221947,0.00008395358,0.002970726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822646,0.0011121581,0.00010609163,0.00020524704,0.00022882446,0.00012117674],"domain_scores_gemma":[0.98416406,0.013431684,0.0010009955,0.00029076144,0.0008841031,0.00022841005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005970726,0.0010642349,0.0012362265,0.0024850967,0.0004346044,0.0017769776,0.0011799043,0.0011242523,0.0020717818],"category_scores_gemma":[0.020977918,0.00042918778,0.0011479941,0.0016433485,0.00065869774,0.0011923289,0.00075231586,0.002175348,0.00046764687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016633865,0.00011043061,0.016914682,0.00017539899,0.00023607379,0.00013773725,0.00009495993,0.9131443,0.0002697495,0.016504513,0.0038243025,0.04842155],"study_design_scores_gemma":[0.000007950598,0.000041770418,0.0012680696,0.000041614036,0.000034421166,0.000027382921,0.00002651893,0.98513454,0.00006943519,0.012689696,0.0006476674,0.000010935117],"about_ca_topic_score_codex":0.008965443,"about_ca_topic_score_gemma":0.0061473725,"teacher_disagreement_score":0.008965443,"about_ca_system_score_codex":0.0013972609,"about_ca_system_score_gemma":0.001698183,"threshold_uncertainty_score":0.031576633},"labels":[],"label_agreement":null},{"id":"W2143924797","doi":"10.6000/1929-6029.2015.04.01.14","title":"Survival Functions in the Presence of Several Events and Competing Risks: Estimation and Interpretation Beyond Kaplan-Meier","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic factors in colorectal cancer","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Kaplan–Meier estimator; Survival analysis; Econometrics; Survival function; Statistics; Multivariate statistics; Event (particle physics); Estimation; Mathematics; Economics","score_opus":0.10626574655959373,"score_gpt":0.4722946423755655,"score_spread":0.36602889581597176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143924797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012229483,0.005637525,0.978958,0.0007680653,0.000090487694,0.000048816033,0.00027582888,0.00027997117,0.0017117974],"genre_scores_gemma":[0.65730697,0.010469801,0.324152,0.00040947183,0.0005378539,0.0004355221,0.0011245377,0.00027031882,0.0052935807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99576145,0.0028808129,0.00024415672,0.00039638043,0.0005393106,0.0001779199],"domain_scores_gemma":[0.97546333,0.020782834,0.0018842637,0.0009079176,0.00074269925,0.0002189219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014003871,0.0012773138,0.0016065968,0.0030491115,0.0003819646,0.0024372032,0.0013558646,0.0015335119,0.002762644],"category_scores_gemma":[0.029895348,0.00049548235,0.0017664109,0.0021052002,0.0013297094,0.0031555165,0.0018370077,0.0033110268,0.0005109619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002610933,0.00011727638,0.029848022,0.0012734857,0.00072235795,0.0014956503,0.0014293852,0.2629823,0.0016791881,0.46811548,0.0056229006,0.2264528],"study_design_scores_gemma":[0.000029183217,0.00028222415,0.008814536,0.00037414394,0.000243749,0.001956132,0.00043586973,0.5284505,0.00093200384,0.4413018,0.017031794,0.00014802889],"about_ca_topic_score_codex":0.002295331,"about_ca_topic_score_gemma":0.000784709,"teacher_disagreement_score":0.014003871,"about_ca_system_score_codex":0.0010738043,"about_ca_system_score_gemma":0.0013103299,"threshold_uncertainty_score":0.07406044},"labels":[],"label_agreement":null},{"id":"W2144252306","doi":"10.6000/1929-6029.2013.02.02.06","title":"A Comparison of Error Correcting Output Coding Methods for Multiclass Classification by Using Support Vector Machine: The Prediction of Self-Monitoring of Blood Sugar","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multiclass classification; Support vector machine; Artificial intelligence; Binary number; Computer science; Machine learning; Structured support vector machine; Binary classification; Pattern recognition (psychology); Coding (social sciences); Class (philosophy); AdaBoost; Data mining; Mathematics; Statistics","score_opus":0.21699383592999893,"score_gpt":0.5356901599460219,"score_spread":0.31869632401602294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144252306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36802402,0.0065486026,0.6182645,0.0006311742,0.00049272145,0.00018495796,0.00020646346,0.0017406397,0.0039069233],"genre_scores_gemma":[0.81274503,0.0013823047,0.18362296,0.00009566324,0.00008939137,0.00013108477,0.00035541496,0.000092750946,0.001485419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974323,0.0008493978,0.0001631984,0.00034396266,0.0010668832,0.00014419456],"domain_scores_gemma":[0.9898705,0.0060130325,0.00047783784,0.00073403766,0.002734701,0.00016993484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00452832,0.0007083019,0.0009038639,0.0018810254,0.00039806278,0.0010174133,0.0009877064,0.0010499902,0.00060491374],"category_scores_gemma":[0.012258168,0.00024247062,0.0006347616,0.0013134736,0.00047595636,0.0012305281,0.00067645084,0.0011034659,0.00023323791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020398765,0.00044338848,0.010375587,0.00030723665,0.00028630203,0.00009861485,0.0002877742,0.12709726,0.0087385,0.0033518432,0.002010639,0.844963],"study_design_scores_gemma":[0.000036590693,0.0003640762,0.004994906,0.000042322135,0.00006451374,0.000077907,0.00007998821,0.9845047,0.008034239,0.00079438864,0.0009631868,0.000043141805],"about_ca_topic_score_codex":0.0057293936,"about_ca_topic_score_gemma":0.0026255345,"teacher_disagreement_score":0.0057293936,"about_ca_system_score_codex":0.0007447291,"about_ca_system_score_gemma":0.0009888017,"threshold_uncertainty_score":0.023948312},"labels":[],"label_agreement":null},{"id":"W2144729853","doi":"10.6000/1929-6029.2015.04.03.2","title":"Supplementing Missing Self-Reported Race Data with a Probability Distribution in Logistic Regression Models","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pneumonia and Respiratory Infections","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Research Resources; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Categorical variable; Statistics; Missing data; Logistic regression; Econometrics; Mathematics; Race (biology); Estimator","score_opus":0.3094708227240504,"score_gpt":0.5223042274735936,"score_spread":0.2128334047495432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144729853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06536789,0.00079805416,0.93027633,0.0011556349,0.00015397131,0.00023012437,0.0008380042,0.0004446424,0.0007352205],"genre_scores_gemma":[0.720821,0.0012948157,0.27201793,0.00048049842,0.00031344494,0.0011316277,0.0015598242,0.00014766761,0.0022331844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96541613,0.02990126,0.0008530941,0.002068831,0.0011456286,0.0006151179],"domain_scores_gemma":[0.8988362,0.08725265,0.0059645595,0.0053806687,0.0021824352,0.00038344483],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.036913235,0.0013552106,0.0025994403,0.0018528248,0.0009286794,0.0018620446,0.0041718665,0.0024416244,0.0032172718],"category_scores_gemma":[0.11502013,0.0011712075,0.0031211195,0.0038102998,0.0014714516,0.0038807623,0.002323023,0.0040364517,0.0009512251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001213288,0.00054413255,0.16558218,0.0011999593,0.0027460793,0.0025730543,0.0017038814,0.5193254,0.00093062577,0.087162316,0.006449992,0.21056916],"study_design_scores_gemma":[0.0000913803,0.00022993433,0.008195857,0.0001854144,0.0003631273,0.00028715903,0.0001807745,0.9418471,0.00042135981,0.04505767,0.0030555956,0.0000846283],"about_ca_topic_score_codex":0.009870251,"about_ca_topic_score_gemma":0.00794803,"teacher_disagreement_score":0.9630868,"about_ca_system_score_codex":0.000976389,"about_ca_system_score_gemma":0.002158309,"threshold_uncertainty_score":0.19521815},"labels":[],"label_agreement":null},{"id":"W2146492981","doi":"10.6000/1929-6029.2012.01.02.11","title":"Feature Selection in Statistical Classification","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Feature selection; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); Feature (linguistics); Computer science; Statistics; Mathematics","score_opus":0.2702167256840201,"score_gpt":0.5786589139916529,"score_spread":0.3084421883076328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146492981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026398494,0.007654033,0.9874098,0.0004346803,0.00021077174,0.000046670488,0.00026081724,0.0005020863,0.000841255],"genre_scores_gemma":[0.14711091,0.01857765,0.8236025,0.0007327601,0.0023682024,0.0007485277,0.0018683583,0.000400731,0.004590361],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966846,0.0014477263,0.00027335354,0.0006081739,0.0008614803,0.00012463308],"domain_scores_gemma":[0.9950008,0.0037124087,0.00026182117,0.0004724779,0.0004886244,0.000064000706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043256963,0.0010745693,0.0018419685,0.0025216376,0.0005165558,0.0015908926,0.0010976917,0.0012595339,0.0026531352],"category_scores_gemma":[0.010532614,0.00043006832,0.0013519195,0.0043984,0.00085723185,0.0016726396,0.00085981976,0.0015342217,0.0019829168],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014266657,0.0001002692,0.002756445,0.0012852696,0.00033746363,0.00026798036,0.00014465532,0.045374304,0.0064117513,0.05903625,0.019404596,0.86473835],"study_design_scores_gemma":[0.00007446093,0.00038207343,0.0067029768,0.00047240936,0.000259393,0.00086112623,0.000086638625,0.5526195,0.0137354005,0.35812387,0.06652805,0.00015403904],"about_ca_topic_score_codex":0.0012270918,"about_ca_topic_score_gemma":0.0007615243,"teacher_disagreement_score":0.0043256963,"about_ca_system_score_codex":0.0005226912,"about_ca_system_score_gemma":0.00062983483,"threshold_uncertainty_score":0.0228768},"labels":[],"label_agreement":null},{"id":"W2155276878","doi":"10.6000/1929-6029.2014.03.04.11","title":"Development of Predictive Models for Continuous Flow Left Ventricular Assist Device Patients using Bayesian Networks","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Mechanical Circulatory Support Devices","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian network; Medicine; Predictive power; Bayesian probability; Bayes' theorem; Ventricular assist device; Machine learning; Internal medicine; Cardiology; Artificial intelligence; Computer science; Heart failure","score_opus":0.04170688185284299,"score_gpt":0.35358751188370396,"score_spread":0.311880630030861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155276878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058433205,0.00037985877,0.9373056,0.0006476645,0.000040262363,0.00019711316,0.0006871902,0.00056176033,0.0017472511],"genre_scores_gemma":[0.71249354,0.0007000191,0.28171661,0.00019308856,0.00016427068,0.0008560442,0.00189276,0.00008019317,0.0019035201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989747,0.0005902838,0.00006717571,0.0001603733,0.00013865605,0.00006894867],"domain_scores_gemma":[0.98903507,0.00936327,0.00056537957,0.00011635278,0.00077831093,0.00014168216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043354235,0.0011386762,0.0009901022,0.0020983927,0.00048897753,0.0012001222,0.0012733848,0.0007825609,0.0024883198],"category_scores_gemma":[0.014392446,0.0009634914,0.0011826983,0.00075333414,0.0004148437,0.001033434,0.0009007714,0.0015849846,0.0005578809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008809755,0.00006563345,0.007406827,0.000043577445,0.00010273269,0.000057493708,0.000057724897,0.96049905,0.00016163939,0.0032886767,0.0006664227,0.027562145],"study_design_scores_gemma":[0.0000057495135,0.000008074016,0.00028041666,0.000010305524,0.00000883503,0.0000073067067,0.0000044972876,0.9971807,0.000035135,0.002365261,0.00009036973,0.0000033477245],"about_ca_topic_score_codex":0.01963028,"about_ca_topic_score_gemma":0.016931899,"teacher_disagreement_score":0.01963028,"about_ca_system_score_codex":0.0014693325,"about_ca_system_score_gemma":0.0019356986,"threshold_uncertainty_score":0.039032042},"labels":[],"label_agreement":null},{"id":"W2156438778","doi":"10.6000/1929-6029.2014.03.04.3","title":"Graphical Investigation of Threshold Choice Effect on Odds Ratio Related to Prognostic Factors in Stroke Recovery","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Neurorehabilitation; Stroke (engine); Logistic regression; Univariate; Multivariate statistics; Medicine; Odds ratio; Multivariate analysis; Statistics; Ordered logit; Mathematics; Physical medicine and rehabilitation; Physical therapy; Rehabilitation","score_opus":0.03501373794371558,"score_gpt":0.3988753572696653,"score_spread":0.3638616193259497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156438778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7119851,0.00278707,0.2725295,0.0010837829,0.00030989837,0.00028562496,0.002818955,0.004454855,0.0037451228],"genre_scores_gemma":[0.97262776,0.0002006385,0.025776368,0.0000904893,0.000070569375,0.0001339248,0.0005335517,0.00015944756,0.0004071535],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97796565,0.016185358,0.00074770564,0.0028533484,0.0016816567,0.0005661612],"domain_scores_gemma":[0.7278884,0.25434154,0.007703224,0.00643399,0.0028817835,0.0007511702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017780147,0.00095146935,0.0013008335,0.0029193289,0.00029569704,0.0021776054,0.0010355889,0.0012434734,0.008790851],"category_scores_gemma":[0.12258769,0.00035771346,0.0016467277,0.0013938413,0.0014128052,0.0016422715,0.00088725233,0.0012156571,0.00063275406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02371287,0.0007547629,0.58647263,0.002712296,0.0040745167,0.0032107523,0.0021511512,0.068768986,0.027404234,0.0127580175,0.0048993016,0.26308045],"study_design_scores_gemma":[0.00067579653,0.00512012,0.46153322,0.000531656,0.002553653,0.0035877987,0.0008990955,0.4811785,0.016849233,0.02145168,0.005290974,0.00032820323],"about_ca_topic_score_codex":0.0007830022,"about_ca_topic_score_gemma":0.00034206396,"teacher_disagreement_score":0.017780147,"about_ca_system_score_codex":0.0005041269,"about_ca_system_score_gemma":0.00033166827,"threshold_uncertainty_score":0.09403151},"labels":[],"label_agreement":null},{"id":"W2156868155","doi":"10.6000/1929-6029.2012.01.02.02","title":"Using Prior Information on Parameters to Eliminate Dependence on Initial Values in Fitting Coxian Phase Type Distributions to Length of Stay Data in Healthcare Settings","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Range (aeronautics); Statistics; Sample size determination; Sample (material); Phase (matter); Prior information; Mathematics; Distribution (mathematics); Computer science; Materials science; Physics; Thermodynamics; Mathematical analysis","score_opus":0.23452184924021688,"score_gpt":0.5734995952563481,"score_spread":0.3389777460161312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156868155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16303964,0.0007675955,0.83047074,0.0009861593,0.000085309905,0.0007377942,0.0006338134,0.0005467699,0.0027321842],"genre_scores_gemma":[0.785996,0.00049154944,0.20979759,0.0004701094,0.000071174436,0.0008417223,0.001288593,0.00011471191,0.00092848623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98427725,0.011500289,0.0008123729,0.0015962428,0.0013903128,0.00042353984],"domain_scores_gemma":[0.7677098,0.21052554,0.007122779,0.009787985,0.0042241085,0.0006298557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046426438,0.0013269109,0.0012573908,0.0018521704,0.0009609446,0.0019149283,0.0017401252,0.002330537,0.003736817],"category_scores_gemma":[0.20428765,0.00089582853,0.0022642326,0.001764653,0.0019457411,0.0035961836,0.0018184355,0.0041202493,0.0004972983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017140872,0.0006498293,0.14641343,0.0010344995,0.0014014823,0.00079009583,0.0019028865,0.5819114,0.0051581534,0.037736587,0.0030965554,0.21819106],"study_design_scores_gemma":[0.00034698047,0.0011920739,0.055067413,0.0008807805,0.00070628844,0.0006347622,0.00046816128,0.86451536,0.008647081,0.059707243,0.007576791,0.00025711066],"about_ca_topic_score_codex":0.0058088847,"about_ca_topic_score_gemma":0.006157391,"teacher_disagreement_score":0.046426438,"about_ca_system_score_codex":0.0016370728,"about_ca_system_score_gemma":0.002658851,"threshold_uncertainty_score":0.24552935},"labels":[],"label_agreement":null},{"id":"W2157316029","doi":"10.6000/1929-6029.2013.02.02.08","title":"A Multistate Markov Model Based on CD4 Cell Count for HIV/AIDS Patients on Antiretroviral Therapy (ART)","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Antiretroviral therapy; Human immunodeficiency virus (HIV); Medicine; Proportional hazards model; Markov model; Transmission (telecommunications); Antiretroviral treatment; Demography; Markov chain; Statistics; Internal medicine; Immunology; Viral load; Mathematics; Computer science","score_opus":0.04326440135255493,"score_gpt":0.42627338233736234,"score_spread":0.3830089809848074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157316029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6614398,0.0012301116,0.32010293,0.0030638827,0.00025439207,0.00044925677,0.005079871,0.00048120023,0.007898637],"genre_scores_gemma":[0.97329265,0.0007502585,0.015563838,0.000117998075,0.00009008818,0.00042625237,0.002266798,0.000023602337,0.007468416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999213,0.00033273947,0.000036793495,0.00017640054,0.000066136716,0.00017494468],"domain_scores_gemma":[0.9976942,0.001742717,0.00023958052,0.0000724041,0.00014012642,0.0001110024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021011222,0.0006454939,0.001076516,0.0009653447,0.00072039367,0.0014062847,0.0013795104,0.0011960992,0.0063091335],"category_scores_gemma":[0.0028590534,0.0005654134,0.0014036596,0.000735969,0.0005712495,0.0013370406,0.0007653167,0.0018941496,0.00060752186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008958686,0.00024812616,0.04018928,0.00013518796,0.00020333753,0.0006051301,0.00036625934,0.91063684,0.0010143131,0.034856036,0.0017407191,0.009108973],"study_design_scores_gemma":[0.000058319496,0.000110830544,0.003202503,0.00002078617,0.000069767484,0.00008237311,0.0000519657,0.98946124,0.00010238046,0.0062402,0.00057733053,0.000022313578],"about_ca_topic_score_codex":0.022602297,"about_ca_topic_score_gemma":0.022067312,"teacher_disagreement_score":0.022602297,"about_ca_system_score_codex":0.00142574,"about_ca_system_score_gemma":0.0019730106,"threshold_uncertainty_score":0.044941485},"labels":[],"label_agreement":null},{"id":"W2158925993","doi":"10.6000/1929-6029.2012.01.01.09","title":"Analysis of Microarray Data","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Microarray analysis techniques; Microarray databases; Computer science; Chemistry","score_opus":0.11054092308991348,"score_gpt":0.4972434100025373,"score_spread":0.3867024869126238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158925993","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014546199,0.004029757,0.88039565,0.00068529346,0.0005466226,0.002374373,0.0750974,0.016218185,0.006106457],"genre_scores_gemma":[0.023249244,0.0043931208,0.89675456,0.000495761,0.00033505476,0.00574749,0.06361362,0.0016990739,0.0037119945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961647,0.0007093158,0.00041763257,0.0008158112,0.0016473664,0.00024518184],"domain_scores_gemma":[0.9963696,0.0015948403,0.00024270624,0.0008919592,0.0008017395,0.00009921859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034129655,0.0016333059,0.0023122488,0.0035898807,0.0010610537,0.0028711273,0.0016605573,0.0005963518,0.012558246],"category_scores_gemma":[0.008987824,0.0006317475,0.0019077328,0.0049400753,0.00051768555,0.0008436964,0.0010665441,0.0018409895,0.012281798],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057450606,0.00023356221,0.005913804,0.005420372,0.00043291552,0.00068316376,0.0003531201,0.0067073046,0.46224785,0.008461364,0.042796806,0.4661753],"study_design_scores_gemma":[0.00014676533,0.0008814949,0.053357486,0.0006855102,0.00045897052,0.0032614472,0.00052515644,0.04359436,0.34075055,0.044808526,0.5111817,0.00034806124],"about_ca_topic_score_codex":0.00089710084,"about_ca_topic_score_gemma":0.0010386212,"teacher_disagreement_score":0.012558246,"about_ca_system_score_codex":0.00068491994,"about_ca_system_score_gemma":0.0014229849,"threshold_uncertainty_score":0.04201156},"labels":[],"label_agreement":null},{"id":"W2160608989","doi":"10.6000/1929-6029.2014.03.03.2","title":"Association between Obesity, Race and Knee Osteoarthritis: The Multicenter Osteoarthritis Study","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Osteoarthritis; Obesity; Medicine; Odds ratio; Ordinal regression; Odds; Longitudinal study; Physical therapy; Association (psychology); Multicenter study; Internal medicine; Psychology; Logistic regression; Randomized controlled trial; Pathology; Mathematics; Alternative medicine","score_opus":0.028654548660221758,"score_gpt":0.3839617104832148,"score_spread":0.35530716182299305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160608989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973015,0.001032874,0.0001926851,0.00015137315,0.000019031868,0.000017292692,0.0009811432,0.0000039947668,0.00030023896],"genre_scores_gemma":[0.998716,0.00024014151,0.00021273877,0.000053418415,0.00003231033,0.00003093532,0.00059247535,0.000004001543,0.0001181287],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99867827,0.0006800408,0.00015525229,0.00020244757,0.00017038303,0.000113565395],"domain_scores_gemma":[0.9972989,0.00027097834,0.0014683764,0.0002102938,0.00027539374,0.0004760769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002083393,0.00034136354,0.0003710558,0.00093338854,0.00060612743,0.0006620797,0.00042944256,0.00064187445,0.0011709249],"category_scores_gemma":[0.0043567196,0.0003029529,0.0005525018,0.0015709403,0.00024621972,0.0005266078,0.00067031506,0.00072522165,0.00016895696],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001884501,0.000036289508,0.9987496,0.000010311092,0.00019525792,0.000017656972,0.00004085115,0.000016498116,0.00011250884,0.000017721448,0.000100202655,0.0005145283],"study_design_scores_gemma":[0.0000123606715,0.000065217864,0.99944454,0.000007030141,0.00007139397,0.00007555981,0.000066604276,0.00010324975,0.000018174625,0.000018699959,0.000113623166,0.0000034012019],"about_ca_topic_score_codex":0.0055972137,"about_ca_topic_score_gemma":0.007890611,"teacher_disagreement_score":0.0055972137,"about_ca_system_score_codex":0.00016160497,"about_ca_system_score_gemma":0.0002949481,"threshold_uncertainty_score":0.01112926},"labels":[],"label_agreement":null},{"id":"W2163168290","doi":"","title":"An Exponential Model for Melanoma Mortality Trends","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Demography; Incidence (geometry); Population; Mortality rate; Cancer; Melanoma; Exponential growth; Age groups; Medicine; Mathematics; Internal medicine; Cancer research","score_opus":0.10326703143025967,"score_gpt":0.47943999710007135,"score_spread":0.3761729656698117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163168290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38417554,0.0030834367,0.55918986,0.0040203477,0.00070203585,0.00065788033,0.013163805,0.0013371988,0.03366985],"genre_scores_gemma":[0.91349804,0.0021406095,0.02525881,0.00037876796,0.00020803977,0.00079974707,0.0056992574,0.0001825302,0.051834162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990759,0.00029472986,0.000048927195,0.00024147618,0.000110082525,0.00022891305],"domain_scores_gemma":[0.9976204,0.0012861666,0.00035118542,0.00013433464,0.0005288217,0.00007907427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032239978,0.0010650718,0.0009731668,0.0016124884,0.00045912844,0.0013103737,0.0021236208,0.0018767302,0.009353447],"category_scores_gemma":[0.007433564,0.0005794079,0.0018965191,0.0012813385,0.0006256624,0.0017251183,0.000897179,0.0020737536,0.0027250694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039553334,0.0001971179,0.029347705,0.00029995883,0.0001921556,0.00067731243,0.00055933435,0.87719077,0.001869737,0.05458787,0.006296146,0.028386327],"study_design_scores_gemma":[0.0000576563,0.00016084303,0.006607794,0.00006479585,0.00009293552,0.0002679443,0.00020001935,0.96793,0.00031208943,0.016881341,0.007372282,0.000052341235],"about_ca_topic_score_codex":0.02047543,"about_ca_topic_score_gemma":0.010683204,"teacher_disagreement_score":0.02047543,"about_ca_system_score_codex":0.0013388251,"about_ca_system_score_gemma":0.0010879143,"threshold_uncertainty_score":0.040712476},"labels":[],"label_agreement":null},{"id":"W2163437315","doi":"10.6000/1929-6029.2014.03.01.7","title":"Interpreting Long-Term Trends in Time Series Intervention Studies of Smoke-Free Legislation and Health","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Medical Research Council; Public Health England; University of Bath; British Heart Foundation; Cancer Research UK; United Kingdom Clinical Research Collaboration","keywords":"Legislation; Parametric statistics; Term (time); Econometrics; Demography; Poisson regression; Poisson distribution; Parametric model; Spline (mechanical); Smoke; Statistics; Mathematics; Medicine; Geography; Environmental health; Population; Political science; Law","score_opus":0.16375135498064075,"score_gpt":0.5284321878095404,"score_spread":0.3646808328288996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163437315","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70378464,0.021008395,0.24401298,0.014885832,0.0019923262,0.001242212,0.0048077884,0.00042024854,0.007845618],"genre_scores_gemma":[0.9654686,0.0020735972,0.026451994,0.0016974267,0.0004362979,0.0012143119,0.001430213,0.000087579625,0.0011400093],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90149903,0.08794041,0.0039056183,0.0029010586,0.002832267,0.00092161115],"domain_scores_gemma":[0.59410673,0.36306953,0.027316462,0.009688147,0.0050406056,0.0007784909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.14605291,0.0009414151,0.0015119186,0.0033935679,0.0008527966,0.00294009,0.0023880133,0.0030919826,0.004669289],"category_scores_gemma":[0.27335528,0.00066408236,0.0046417536,0.005162838,0.002135392,0.0026893893,0.0028962535,0.0032449146,0.0003569123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029753712,0.0006258822,0.7432185,0.008317692,0.023283664,0.0010636582,0.010373192,0.055440437,0.001598083,0.032478146,0.0059872987,0.11463802],"study_design_scores_gemma":[0.00036101823,0.0044705546,0.7302009,0.0069728163,0.009055127,0.00055029657,0.010591187,0.13113649,0.0025439875,0.078186445,0.025578303,0.00035290557],"about_ca_topic_score_codex":0.010793641,"about_ca_topic_score_gemma":0.008944677,"teacher_disagreement_score":0.14605291,"about_ca_system_score_codex":0.0017202704,"about_ca_system_score_gemma":0.0020781434,"threshold_uncertainty_score":0.77241087},"labels":[],"label_agreement":null},{"id":"W2164484465","doi":"10.6000/1929-6029.2014.03.04.10","title":"Predictive Modelling of Patient Reported Radiotherapy-Related Toxicity by the Application of Symptom Clustering and Autoregression","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cancer survivorship and care","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Universitas Andalas","keywords":"Radiation therapy; Medicine; Prostate cancer; Triage; Quality of life (healthcare); Cancer; Internal medicine; Emergency medicine","score_opus":0.03295447579746805,"score_gpt":0.3892625673734372,"score_spread":0.3563080915759691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164484465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69461155,0.0010456281,0.29790092,0.0014596384,0.00014752906,0.00041304235,0.0022379444,0.0007776308,0.0014061108],"genre_scores_gemma":[0.9600873,0.0003080088,0.036619376,0.000080091544,0.00006170331,0.00022402797,0.00172825,0.000035275865,0.0008559734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728656,0.0019650285,0.00011995179,0.00031857818,0.00016001069,0.00014991658],"domain_scores_gemma":[0.9885252,0.0092927655,0.0008914263,0.0005436432,0.0005829237,0.00016398693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073391744,0.00092393655,0.0011784593,0.0018294045,0.00033543998,0.0012721071,0.001058268,0.00067098136,0.0014112514],"category_scores_gemma":[0.016539628,0.00039757264,0.0018879548,0.0017640666,0.00035731707,0.000614058,0.0008447007,0.0015155006,0.00037644006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065883464,0.0004438531,0.14981522,0.00017656038,0.0008078006,0.00020701459,0.00035491266,0.7366945,0.00095647835,0.0030181848,0.0019701282,0.10489648],"study_design_scores_gemma":[0.000011487545,0.00010206983,0.014101405,0.000015912521,0.000050809853,0.000029116545,0.00004609093,0.9837864,0.00014966025,0.0014018887,0.00028578486,0.000019379051],"about_ca_topic_score_codex":0.014483814,"about_ca_topic_score_gemma":0.010051519,"teacher_disagreement_score":0.014483814,"about_ca_system_score_codex":0.00084270333,"about_ca_system_score_gemma":0.0013733552,"threshold_uncertainty_score":0.03881377},"labels":[],"label_agreement":null},{"id":"W2164584594","doi":"10.6000/1929-6029.2013.02.01.02","title":"Time-Dependent Relationships Between Human Brain and Body Temperature After Severe Traumatic Brain Injury","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Thermal Regulation in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Traumatic brain injury; Neurointensive care; Thermistor; Medicine; Intracranial pressure; Anesthesia; Nuclear medicine","score_opus":0.05533140277770641,"score_gpt":0.4368684721309871,"score_spread":0.3815370693532807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164584594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975459,0.0001952338,0.0015950605,0.000030821113,0.000006632193,0.000010589849,0.00019883474,0.000017570555,0.00039929294],"genre_scores_gemma":[0.9991217,0.0000985004,0.00036984496,0.000008428283,0.000004590986,0.000010221124,0.00023082049,0.000004145827,0.00015170427],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997776,0.000062460145,0.000019854817,0.0000527012,0.00006232995,0.000024959707],"domain_scores_gemma":[0.9984856,0.00070608046,0.00048583673,0.00007575444,0.00018325313,0.00006335257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049422693,0.00014909075,0.00020907179,0.00029885146,0.00010829733,0.00035207003,0.00013682757,0.00017521667,0.0010965384],"category_scores_gemma":[0.004581845,0.000103512815,0.00012480108,0.00034335515,0.00022749626,0.00024850975,0.00023341052,0.00033637558,0.0002720384],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026983419,0.00011843573,0.91953206,0.00015737722,0.00018617476,0.00034717898,0.00084453484,0.0056313304,0.025544478,0.0001605144,0.00071732316,0.044062227],"study_design_scores_gemma":[0.000003871108,0.00027502576,0.9933615,0.000007695477,0.00001714322,0.000277578,0.00009204418,0.003566673,0.0021019082,0.0000908482,0.00019817127,0.000007635183],"about_ca_topic_score_codex":0.001883599,"about_ca_topic_score_gemma":0.0019915465,"teacher_disagreement_score":0.001883599,"about_ca_system_score_codex":0.00022726861,"about_ca_system_score_gemma":0.00015940209,"threshold_uncertainty_score":0.0037453175},"labels":[],"label_agreement":null},{"id":"W2166374017","doi":"10.6000/1929-6029.2014.03.04.1","title":"A Robust Parameterization for Unbounded Covariates Within the Cox Proportional Hazards Model","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Proportional hazards model; Hazard; Statistics; Mathematics; Function (biology); Bounded function; Transformation (genetics); Econometrics; Applied mathematics","score_opus":0.2088160599941217,"score_gpt":0.5008657764484716,"score_spread":0.29204971645434996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166374017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010481161,0.0002878269,0.9978321,0.00020694692,0.00004044313,0.00003789532,0.00007995112,0.00011345965,0.00035319274],"genre_scores_gemma":[0.29213127,0.0033309252,0.6905413,0.000900313,0.0008947728,0.001597812,0.0018260076,0.00078290555,0.0079947235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9915731,0.005227859,0.000289849,0.0011520018,0.0014025576,0.00035471178],"domain_scores_gemma":[0.9864124,0.009847392,0.0010135183,0.0015647578,0.0009669205,0.00019498654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016378932,0.0016231502,0.0021239067,0.0015548585,0.0006648473,0.001970313,0.0034894017,0.0019772537,0.0030693072],"category_scores_gemma":[0.047276627,0.0008750666,0.0024747422,0.002003626,0.0018100757,0.0027485366,0.0028982924,0.0062036877,0.0013628561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021484852,0.000081644415,0.0025194075,0.00037104692,0.00024359461,0.00060771336,0.00033701022,0.41303086,0.003386461,0.4816429,0.0068289074,0.090735555],"study_design_scores_gemma":[0.000040633156,0.00012044122,0.0007024785,0.000072446936,0.000080799124,0.00029520836,0.000042060514,0.7874639,0.0006919237,0.20210662,0.008306974,0.00007652031],"about_ca_topic_score_codex":0.0037177766,"about_ca_topic_score_gemma":0.00184801,"teacher_disagreement_score":0.016378932,"about_ca_system_score_codex":0.0012894965,"about_ca_system_score_gemma":0.0028569605,"threshold_uncertainty_score":0.086621046},"labels":[],"label_agreement":null},{"id":"W2167095458","doi":"10.6000/1929-6029.2013.02.03.6","title":"An Application of Gamma Generalized Linear Model for Estimation of Survival Function of Diabetic Nephropathy Patients","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Akaike information criterion; Diabetic nephropathy; Mathematics; Covariate; Diabetes mellitus; Linear regression; Statistics; Gamma distribution; Nephropathy; Renal function; Regression analysis; Survival analysis; Regression; Internal medicine; Medicine; Endocrinology","score_opus":0.04796155289504596,"score_gpt":0.4250175883832371,"score_spread":0.37705603548819117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167095458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113671415,0.0017564886,0.87831396,0.0010514295,0.00022623608,0.0003469946,0.001915855,0.0010715497,0.0016461177],"genre_scores_gemma":[0.7984609,0.0021547987,0.18392111,0.00045195047,0.00028600183,0.0016754087,0.0055419854,0.00036462012,0.007143149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99627614,0.00234264,0.00014328734,0.00070139527,0.00024755753,0.0002891267],"domain_scores_gemma":[0.9938334,0.004877692,0.0004520199,0.00029142378,0.00043006556,0.00011537443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075581535,0.001446429,0.0016531739,0.0021177076,0.0005745008,0.0014383901,0.0020289691,0.0014752347,0.0033286186],"category_scores_gemma":[0.014015561,0.00056387496,0.0025962736,0.0020315805,0.00062154833,0.0010545036,0.001461592,0.002411083,0.0007892927],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072862324,0.00029913033,0.103270635,0.00061717647,0.0015574358,0.0010897194,0.0012018564,0.6686715,0.0017943857,0.034177586,0.008338289,0.17825368],"study_design_scores_gemma":[0.000051524166,0.00033944062,0.012025279,0.00011599738,0.00022927631,0.00022934629,0.00021203986,0.96282876,0.00039445146,0.019783469,0.0037209913,0.00006948181],"about_ca_topic_score_codex":0.015801208,"about_ca_topic_score_gemma":0.008144677,"teacher_disagreement_score":0.015801208,"about_ca_system_score_codex":0.0011880078,"about_ca_system_score_gemma":0.0019400886,"threshold_uncertainty_score":0.03997183},"labels":[],"label_agreement":null},{"id":"W2168190598","doi":"10.6000/1929-6029.2012.01.02.07","title":"On the Measurement of Change in Medical Research","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Baseline (sea); Ordinal Scale; Interpretation (philosophy); Standard deviation; Ordinal data; Test (biology); Psychology; Level of measurement; Statistics; Econometrics; Computer science; Mathematics; Political science","score_opus":0.8472474708371073,"score_gpt":0.6427348919138398,"score_spread":0.20451257892326746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168190598","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014278675,0.33594963,0.34929758,0.18736583,0.0189697,0.003633975,0.0019878482,0.0005191834,0.08799753],"genre_scores_gemma":[0.30113474,0.21267928,0.39466557,0.054894794,0.018236713,0.010994393,0.0013002071,0.0004412271,0.0056530447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.41393718,0.4843321,0.027066246,0.010567148,0.06238643,0.0017108367],"domain_scores_gemma":[0.31499127,0.60565567,0.026942836,0.024932057,0.025495805,0.0019824167],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4149696,0.0019593579,0.0043952484,0.011035396,0.0024540152,0.01109781,0.0036746103,0.0061927843,0.0040522977],"category_scores_gemma":[0.5310267,0.0011424392,0.003662391,0.017067933,0.030406058,0.013126212,0.010268219,0.011372481,0.001852785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003659413,0.0001499706,0.009559939,0.007739944,0.00084748917,0.00015584633,0.004776444,0.0033763316,0.00033790368,0.596385,0.026695175,0.34961006],"study_design_scores_gemma":[0.00021008488,0.0012546339,0.021015141,0.023986569,0.00049828016,0.0005467223,0.002275502,0.003985097,0.0006528273,0.7962275,0.14899789,0.00034978616],"about_ca_topic_score_codex":0.0038566424,"about_ca_topic_score_gemma":0.0021171214,"teacher_disagreement_score":0.58503044,"about_ca_system_score_codex":0.0095214695,"about_ca_system_score_gemma":0.0092695495,"threshold_uncertainty_score":0.72144663},"labels":[],"label_agreement":null},{"id":"W2168426365","doi":"10.6000/1929-6029.2014.03.01.1","title":"The Suppression Variables in Clinical Research","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confounding; Variable (mathematics); Multivariate statistics; Variables; Statistics; Regression analysis; Mathematics; Multivariate analysis; Econometrics; Psychology","score_opus":0.20808358031188728,"score_gpt":0.5305601905755258,"score_spread":0.32247661026363855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168426365","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001059512,0.005599751,0.00019585429,0.071757756,0.9214266,0.000021079422,0.00007298703,0.00002298727,0.00079694646],"genre_scores_gemma":[0.0011835577,0.0019632059,0.00014699736,0.02503377,0.97038406,0.00003939907,0.000013906597,0.000023611472,0.0012115556],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98352814,0.0070196865,0.002436272,0.0018775614,0.0045797876,0.00055856985],"domain_scores_gemma":[0.66658396,0.2849545,0.0087520955,0.0063455524,0.0261762,0.0071876147],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031878542,0.0025807763,0.0039317077,0.0047257375,0.002999066,0.009418554,0.004360715,0.01616928,0.00774628],"category_scores_gemma":[0.18793853,0.0015335295,0.0021085085,0.0023497462,0.008090347,0.004573725,0.0021025455,0.022040218,0.003074351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054940167,0.00001722765,0.00012269107,0.00057201786,0.000057562087,0.00013918667,0.000059953763,0.000025699645,0.000031559986,0.001892988,0.99106735,0.0059589916],"study_design_scores_gemma":[0.00039659545,0.000110212844,0.0022739673,0.004572075,0.00054015324,0.00092722574,0.00036667296,0.0008679765,0.00041227604,0.01301338,0.9764263,0.000093192204],"about_ca_topic_score_codex":0.0009262273,"about_ca_topic_score_gemma":0.001514128,"teacher_disagreement_score":0.96812147,"about_ca_system_score_codex":0.0028010334,"about_ca_system_score_gemma":0.0041473443,"threshold_uncertainty_score":0.16859186},"labels":[],"label_agreement":null},{"id":"W2169943518","doi":"10.6000/1929-6029.2012.01.02.08","title":"Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Ordinal data; Ordinal regression; Random effects model; Statistics; Econometrics; Hyperparameter; Variable-order Bayesian network; Longitudinal data; Computer science; Mathematics; Logistic regression; Bayesian hierarchical modeling; Bayes' theorem; Bayesian inference; Data mining; Machine learning","score_opus":0.3674978022759443,"score_gpt":0.5906975653593451,"score_spread":0.22319976308340084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169943518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006453795,0.00025758013,0.99241626,0.00026505545,0.000017532928,0.00005953585,0.00016511843,0.0001390121,0.00022605354],"genre_scores_gemma":[0.27018335,0.0015316495,0.7217033,0.00042796438,0.00023119157,0.0013023039,0.0016825328,0.0002686573,0.002669011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98947585,0.007745145,0.00035465063,0.0010390419,0.0010799681,0.00030530474],"domain_scores_gemma":[0.92525554,0.06751563,0.0022723821,0.0022209974,0.0022229478,0.0005124811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024882885,0.0008532863,0.0023605095,0.002494545,0.001077009,0.0016742375,0.0024332637,0.0018062012,0.0038616408],"category_scores_gemma":[0.078983344,0.00077198667,0.0022033846,0.0027499397,0.0014825813,0.0028461274,0.0022762995,0.004072158,0.0005959007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052262103,0.00024593782,0.012951988,0.00068894366,0.0006905905,0.00068830437,0.0012451363,0.42972574,0.002285813,0.38388193,0.005004797,0.16206817],"study_design_scores_gemma":[0.000042654752,0.00006953512,0.0019216381,0.00005754612,0.000067069515,0.0001230439,0.00007352908,0.8476604,0.00028533416,0.1478706,0.0017831929,0.000045442568],"about_ca_topic_score_codex":0.008454608,"about_ca_topic_score_gemma":0.0063199494,"teacher_disagreement_score":0.024882885,"about_ca_system_score_codex":0.0013110584,"about_ca_system_score_gemma":0.0025184148,"threshold_uncertainty_score":0.13159484},"labels":[],"label_agreement":null},{"id":"W2171087224","doi":"10.6000/1929-6029.2013.02.02.02","title":"A Generalization of the «Lady-Tasting-Tea» Procedure to Link Qualitative and Quantitative Approaches in Psychiatric Research","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diverse Scientific and Engineering Research","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalization; Sibling; Sample size determination; Wine tasting; Set (abstract data type); Psychology; Sample (material); Test (biology); Point (geometry); Power (physics); Computer science; Developmental psychology; Mathematics; Statistics","score_opus":0.16590333025809123,"score_gpt":0.4564188040789886,"score_spread":0.2905154738208974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171087224","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009828974,0.00045353867,0.94436973,0.0017847978,0.0008574471,0.030996433,0.0008437733,0.0006309595,0.010234282],"genre_scores_gemma":[0.049056973,0.00024353148,0.83020526,0.0018895165,0.00014734997,0.116793595,0.0001656558,0.00012727304,0.0013708598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.75693834,0.22251397,0.005760297,0.0070026442,0.0068589863,0.0009258343],"domain_scores_gemma":[0.78488755,0.16738917,0.008773705,0.031270713,0.0069159474,0.00076304807],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.16610739,0.0014302656,0.0016242686,0.005267984,0.0032059131,0.0026195229,0.0025298162,0.0020137804,0.007456315],"category_scores_gemma":[0.18176733,0.0010360774,0.0021345264,0.0054351157,0.014482814,0.0029577706,0.0055612954,0.004661749,0.0013935388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013321477,0.0007673752,0.007688748,0.0071271043,0.0006763304,0.00043944444,0.07004041,0.002608965,0.008891815,0.46952778,0.02114595,0.40975392],"study_design_scores_gemma":[0.0019585565,0.0038905807,0.030548794,0.002640926,0.0005220173,0.001344588,0.017901398,0.026042907,0.0078585455,0.706274,0.20030697,0.0007108177],"about_ca_topic_score_codex":0.0024370572,"about_ca_topic_score_gemma":0.0035083864,"teacher_disagreement_score":0.8338926,"about_ca_system_score_codex":0.0030021942,"about_ca_system_score_gemma":0.005032347,"threshold_uncertainty_score":0.8784703},"labels":[],"label_agreement":null},{"id":"W2171301535","doi":"10.6000/1929-6029.2013.02.01.05","title":"Impact of Various Effects of Smoking in the Mouth on Motivating Dental Patients to Quit Smoking","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Fukuoka Dental College","keywords":"Medicine; Smoking cessation; Quit smoking; Presentation (obstetrics); Dentistry; Oral health; Family medicine; Surgery","score_opus":0.04615727759105364,"score_gpt":0.4556844388237714,"score_spread":0.40952716123271776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171301535","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977749,0.0003879677,0.000051876497,0.00024374819,0.0000102783715,0.00004039722,0.000018425688,0.0000045421307,0.0014678449],"genre_scores_gemma":[0.9990503,0.00027528655,0.0002987646,0.00008023093,0.000014746617,0.000022790617,0.00001816524,0.0000011609859,0.00023850116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983,0.0009494277,0.000101288606,0.00007282872,0.0003098143,0.00026673474],"domain_scores_gemma":[0.9956033,0.0023239965,0.00063351623,0.00011346476,0.00024076198,0.0010848729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018523363,0.0002258085,0.000314393,0.0003360238,0.0005606095,0.00074244203,0.00021352587,0.0005847823,0.0032900556],"category_scores_gemma":[0.008887037,0.00014621837,0.00089209055,0.00016940171,0.00027850745,0.00033429038,0.0005988985,0.00069381204,0.000172172],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028266478,0.009954912,0.76186824,0.0006008798,0.0005826365,0.00049521856,0.0046530375,0.00048832357,0.00433649,0.00029980333,0.000495314,0.21339847],"study_design_scores_gemma":[0.00015316151,0.0075178384,0.9836435,0.000110064946,0.0005105987,0.00031527027,0.003319296,0.00064435427,0.0010161754,0.00021641608,0.0025186995,0.00003457275],"about_ca_topic_score_codex":0.0011524584,"about_ca_topic_score_gemma":0.003323502,"teacher_disagreement_score":0.0032900556,"about_ca_system_score_codex":0.0004667646,"about_ca_system_score_gemma":0.0010115226,"threshold_uncertainty_score":0.011006355},"labels":[],"label_agreement":null},{"id":"W2173601296","doi":"10.6000/1929-6029.2015.04.04.5","title":"Recalibration in Validation Studies of Diabetes Risk Prediction Models: A Systematic Review","year":2015,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Diabetes, Cardiovascular Risks, and Lipoproteins","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation; Carl and Emily Fuchs Foundation","keywords":"Predictive modelling; Population; Systematic review; Risk assessment; Computer science; Medicine; MEDLINE; Machine learning; Environmental health","score_opus":0.23609020868346783,"score_gpt":0.5096498530748312,"score_spread":0.2735596443913634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2173601296","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007386684,0.9973604,0.0006201048,0.00034349944,0.00012486147,0.00028340434,0.00030467135,0.00001875216,0.00020559652],"genre_scores_gemma":[0.021580944,0.97313076,0.0031715531,0.00062652625,0.000116432275,0.0007817068,0.0004746259,0.000025779,0.00009173618],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9437556,0.027978176,0.016904926,0.002799445,0.00802287,0.0005388977],"domain_scores_gemma":[0.6388893,0.3122065,0.027377408,0.0059956447,0.0149013065,0.0006298512],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06380813,0.0019929032,0.00915179,0.012661914,0.0007382252,0.0042552943,0.0039734077,0.0023548712,0.003263594],"category_scores_gemma":[0.26462412,0.0017051267,0.010840183,0.011480529,0.0015580775,0.0045408686,0.0025372792,0.002004802,0.00043142666],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019986054,0.000016058977,0.0016863444,0.90891516,0.016706126,0.0000812169,0.00030917465,0.0002648159,0.00008889207,0.0004979921,0.0017452291,0.06948912],"study_design_scores_gemma":[0.00017800729,0.00012875875,0.0032855598,0.9029003,0.07514452,0.0002397871,0.00024459997,0.0003099649,0.0002519222,0.00072815013,0.01653322,0.000055182703],"about_ca_topic_score_codex":0.008088507,"about_ca_topic_score_gemma":0.018983403,"teacher_disagreement_score":0.93619186,"about_ca_system_score_codex":0.0046717743,"about_ca_system_score_gemma":0.01691718,"threshold_uncertainty_score":0.33745366},"labels":[],"label_agreement":null},{"id":"W2174498577","doi":"10.6000/1929-6029.2015.04.04.2","title":"Predicting Breast Cancer Mortality in the Presence of Competing Risks Using Smartphone Application Development Software","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Breast cancer; Android (operating system); Computer science; Context (archaeology); Breast cancer screening; Medicine; Smartphone application; Software; Health care; Cancer; Multimedia; Mammography","score_opus":0.3489147642688512,"score_gpt":0.5479369148488675,"score_spread":0.19902215058001632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174498577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5797394,0.001389131,0.3753504,0.0031287924,0.00032200845,0.0015308284,0.0060007847,0.02358483,0.008953832],"genre_scores_gemma":[0.79570156,0.0005560011,0.19848754,0.00041949737,0.00007411393,0.00047401054,0.0016935589,0.00025018986,0.002343568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994541,0.00020203006,0.00008330848,0.00009904681,0.00012474018,0.000036758105],"domain_scores_gemma":[0.9936168,0.0051295278,0.0003754314,0.00023560131,0.00049983076,0.00014269215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018169017,0.0005682146,0.00046039827,0.0009544907,0.00016967425,0.00076996186,0.0005118342,0.00050242845,0.002619051],"category_scores_gemma":[0.010876242,0.00031509186,0.000742029,0.0004411011,0.00011914133,0.00045167925,0.00060234807,0.00058885536,0.00058948295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014743385,0.0006830733,0.2718575,0.0009051961,0.00062553794,0.0012250562,0.00085285085,0.072619155,0.0081080925,0.0047261007,0.032673948,0.6042491],"study_design_scores_gemma":[0.0002081825,0.0005466888,0.06387384,0.0002580388,0.00027620286,0.0008635914,0.00017718316,0.9078651,0.0059581897,0.008259585,0.011577961,0.0001354796],"about_ca_topic_score_codex":0.005131377,"about_ca_topic_score_gemma":0.008093611,"teacher_disagreement_score":0.005131377,"about_ca_system_score_codex":0.0003176623,"about_ca_system_score_gemma":0.00066302373,"threshold_uncertainty_score":0.010203004},"labels":[],"label_agreement":null},{"id":"W2187042752","doi":"10.6000/1929-6029.2015.04.04.3","title":"A Natural Experiment for Inferring Causal Association between Smoking and Tooth Loss: A Study of a Workplace Contemporary Cohort","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidence interval; Odds ratio; Medicine; Incidence (geometry); Demography; Cohort study; Tooth loss; Observational study; Cohort; Odds; Dentistry; Relative risk; Causality (physics); Oral health; Logistic regression; Internal medicine","score_opus":0.1595404800536326,"score_gpt":0.49922019085162106,"score_spread":0.3396797107979884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187042752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936553,0.00019754974,0.005335868,0.00010431347,0.000053729473,0.00027801888,0.00009082396,0.000015422347,0.0002689219],"genre_scores_gemma":[0.9936074,0.0000913966,0.0050797253,0.00022464905,0.000057901238,0.00049190683,0.00016750333,0.000009510166,0.00027005438],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9760074,0.020323677,0.0004778,0.0017127066,0.0011562358,0.0003220736],"domain_scores_gemma":[0.92070955,0.05090662,0.0055437097,0.01929965,0.002086971,0.0014534718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043288194,0.000491716,0.00063548435,0.00058894884,0.0019804377,0.0009389946,0.00108605,0.0012741874,0.0013126207],"category_scores_gemma":[0.050399486,0.0007577448,0.0014750347,0.00032870835,0.0024319827,0.00071045564,0.0009649276,0.0013984259,0.00023782431],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006627742,0.007013121,0.96627384,0.0001295619,0.0021703052,0.0005974051,0.0022878402,0.0006395319,0.004702176,0.0010913213,0.00047005212,0.007996996],"study_design_scores_gemma":[0.002267837,0.033771742,0.936916,0.00009317424,0.0017128568,0.001979423,0.0018991465,0.012350818,0.0030058231,0.0035362155,0.0023366362,0.00013029491],"about_ca_topic_score_codex":0.0047703525,"about_ca_topic_score_gemma":0.0044758506,"teacher_disagreement_score":0.043288194,"about_ca_system_score_codex":0.00064237165,"about_ca_system_score_gemma":0.0011216743,"threshold_uncertainty_score":0.22893256},"labels":[],"label_agreement":null},{"id":"W2187734372","doi":"10.6000/1929-6029.2015.04.04.1","title":"Modeling of the Deaths Due to Ebola Virus Disease Outbreak in Western Africa","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ebola virus; Covariate; Negative binomial distribution; Sierra leone; Poisson distribution; Econometrics; Outbreak; Count data; Generalized linear model; Bayesian probability; Geography; Statistics; Random effects model; Medicine; Mathematics; Virology; Economics","score_opus":0.48208132060239023,"score_gpt":0.5631658237118166,"score_spread":0.08108450310942633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187734372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70961666,0.0020310797,0.26431778,0.0047833747,0.00016207446,0.00040359437,0.005704319,0.00038880718,0.012592212],"genre_scores_gemma":[0.96467686,0.00081813446,0.025051303,0.00015932946,0.000074081014,0.0003785157,0.001367131,0.00003615772,0.007438455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935955,0.00038560882,0.00002708969,0.00010525986,0.000059793987,0.00006279757],"domain_scores_gemma":[0.9984901,0.0010600504,0.00025619936,0.00003389825,0.00010846942,0.00005134994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015470934,0.00065556733,0.0005287583,0.0006875798,0.0003183596,0.0008121441,0.0012115343,0.0011997232,0.0018968153],"category_scores_gemma":[0.005976717,0.00036410795,0.0006446184,0.0006644084,0.0004936805,0.0007715722,0.0009363752,0.00084039033,0.00021982171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014335041,0.000094508694,0.024615444,0.0001494034,0.00010862666,0.00032254917,0.0004275524,0.9407438,0.0005105857,0.019469174,0.0022055653,0.011209488],"study_design_scores_gemma":[0.000023179764,0.000072844836,0.0042035305,0.000025399377,0.000038600112,0.000063936466,0.00014743478,0.9867466,0.00012385866,0.0069329683,0.0016082781,0.000013399499],"about_ca_topic_score_codex":0.025000477,"about_ca_topic_score_gemma":0.01593363,"teacher_disagreement_score":0.025000477,"about_ca_system_score_codex":0.0012301262,"about_ca_system_score_gemma":0.000974734,"threshold_uncertainty_score":0.049709916},"labels":[],"label_agreement":null},{"id":"W2189049422","doi":"10.6000/1929-6029.2015.04.04.4","title":"Non-Homogeneous Poisson Process to Model Seasonal Events: Application to the Health Diseases","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Homogeneous; Poisson distribution; Seasonality; Dengue fever; Poisson regression; Statistics; Poisson process; Environmental science; Econometrics; Geography; Environmental health; Mathematics; Medicine; Immunology","score_opus":0.13182093696444294,"score_gpt":0.47984745569990106,"score_spread":0.3480265187354581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189049422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062253516,0.00061608263,0.9345668,0.0007389235,0.00016475115,0.00009564619,0.0001723598,0.00014296312,0.0012490419],"genre_scores_gemma":[0.8270553,0.0016967456,0.16561541,0.00031479084,0.0005127352,0.00031869777,0.00042847864,0.00010134131,0.003956566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985562,0.00093922607,0.000076291246,0.00016263813,0.0001510548,0.00011454923],"domain_scores_gemma":[0.9926138,0.0057144617,0.0005822048,0.00036772582,0.00053566665,0.0001861406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006761516,0.0006522807,0.00097528956,0.0009887316,0.0004846984,0.0007963569,0.0018633556,0.001219752,0.0012762077],"category_scores_gemma":[0.013223221,0.00040428794,0.0017187585,0.0012590351,0.00070441776,0.00092288555,0.0011257335,0.0018685814,0.000212042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007960345,0.00015408843,0.01205453,0.00009683153,0.00018016875,0.0006029682,0.00031939175,0.8526859,0.0009432436,0.102296144,0.0012121358,0.029375007],"study_design_scores_gemma":[0.000009792874,0.000021354404,0.0008635855,0.00000510985,0.0000151587665,0.000050958424,0.000025019815,0.98629797,0.000086064334,0.012136913,0.00048055206,0.000007559218],"about_ca_topic_score_codex":0.012241263,"about_ca_topic_score_gemma":0.007329579,"teacher_disagreement_score":0.012241263,"about_ca_system_score_codex":0.0008117962,"about_ca_system_score_gemma":0.0010965033,"threshold_uncertainty_score":0.035758793},"labels":[],"label_agreement":null},{"id":"W2254104116","doi":"10.6000/1929-6029.2016.05.01.4","title":"An Empirical Method of Detecting Time-Dependent Confounding: An Observational Study of Next Day Delirium in a Medical ICU","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Nursing Research; Yale University; American Lung Association; Hartford Foundation for Public Giving; National Institute on Aging; CHEST Foundation","keywords":"Confounding; Observational study; Delirium; Medicine; Longitudinal study; Marginal structural model; Marginal model; Haloperidol; Intubation; Psychological intervention; Regression analysis; Intensive care medicine; Anesthesia; Psychiatry; Internal medicine; Statistics; Mathematics","score_opus":0.26246184524402655,"score_gpt":0.5688894166618346,"score_spread":0.306427571417808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254104116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25145745,0.0015661954,0.7364736,0.0013907092,0.0004972507,0.0035595822,0.0023595185,0.00028615198,0.0024095296],"genre_scores_gemma":[0.74021107,0.00047155222,0.24975656,0.0004693339,0.00014885742,0.006862684,0.001210114,0.0000441455,0.0008256764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.871019,0.1097098,0.0055479016,0.00772579,0.0054095476,0.0005879394],"domain_scores_gemma":[0.8013828,0.14797665,0.016305938,0.028912367,0.0045228144,0.0008994853],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09515278,0.0011826475,0.001479904,0.0038555446,0.0010896703,0.0019273905,0.002658388,0.0022741172,0.0024410542],"category_scores_gemma":[0.34023598,0.000623181,0.0028532003,0.003872274,0.0038509618,0.0017835975,0.0028807851,0.002415358,0.000308797],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004786887,0.0008489881,0.69302416,0.002399398,0.010689774,0.0010884374,0.0057775057,0.02497048,0.0040692557,0.0915191,0.0037280705,0.15709783],"study_design_scores_gemma":[0.0017756876,0.010793935,0.45157135,0.0021498774,0.00589964,0.0025474804,0.0038378595,0.2968837,0.008079563,0.18660638,0.029226063,0.00062846363],"about_ca_topic_score_codex":0.0038695305,"about_ca_topic_score_gemma":0.0016710486,"teacher_disagreement_score":0.9048472,"about_ca_system_score_codex":0.0010469097,"about_ca_system_score_gemma":0.0017045147,"threshold_uncertainty_score":0.503222},"labels":[],"label_agreement":null},{"id":"W2265167193","doi":"10.6000/1929-6029.2016.05.01.3","title":"The Validity of Disease-Specific Quality of Life Attributions Among Adults with Multiple Chronic Conditions","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Agency for Healthcare Research and Quality; EuroQol Research Foundation; U.S. Department of Health and Human Services","keywords":"Discriminant validity; Convergent validity; Quality of life (healthcare); Construct validity; Medicine; Clinical psychology; Psychology; Internal medicine; Physical therapy; Psychometrics","score_opus":0.164047064880526,"score_gpt":0.4723969394532928,"score_spread":0.3083498745727668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265167193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984003,0.0002587389,0.00026819512,0.00009481933,0.000016315913,0.000030130632,0.0001702742,0.0000038532094,0.00075748516],"genre_scores_gemma":[0.9993358,0.00008333343,0.0002700312,0.000026478041,0.000011370895,0.00002800316,0.00019708351,0.0000016458685,0.00004620848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966217,0.0011380163,0.00047625956,0.00043976214,0.0011069914,0.0002171733],"domain_scores_gemma":[0.9726217,0.011930358,0.00975372,0.0016802084,0.0029217182,0.0010923877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008863204,0.00044288312,0.00051142875,0.001681891,0.00059031305,0.0011004815,0.0005876746,0.0005701219,0.0013147633],"category_scores_gemma":[0.03612885,0.000227758,0.0009801423,0.0010085957,0.0011103594,0.0009146033,0.0020560855,0.0008999578,0.0001671243],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003412085,0.000032792555,0.995388,0.000024979969,0.0000960451,0.000011652159,0.00037279035,0.00008624177,0.00004412493,0.000046653862,0.000055966593,0.0038066304],"study_design_scores_gemma":[0.000007288244,0.000071280985,0.9984302,0.00003619282,0.000028063938,0.00006678645,0.00048257445,0.00050985895,0.00006694249,0.00015853214,0.00013534096,0.000007018387],"about_ca_topic_score_codex":0.0030262116,"about_ca_topic_score_gemma":0.0037366732,"teacher_disagreement_score":0.008863204,"about_ca_system_score_codex":0.000616429,"about_ca_system_score_gemma":0.00050560763,"threshold_uncertainty_score":0.04687369},"labels":[],"label_agreement":null},{"id":"W2269782984","doi":"10.6000/1929-6029.2016.05.01.2","title":"Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Nursing Research; National Institute on Aging","keywords":"Confounding; Delirium; Medicine; Intensive care unit; Crossover study; Intensive care; Confidence interval; Cohort; Gee; Poisson regression; Cohort study; Generalized estimating equation; Internal medicine; Intensive care medicine; Statistics; Population; Placebo; Mathematics","score_opus":0.0670245474532372,"score_gpt":0.4291393783956073,"score_spread":0.36211483094237007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269782984","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9641031,0.0008064979,0.03294967,0.00016897844,0.000098559925,0.001253227,0.00013614896,0.00003543031,0.0004483326],"genre_scores_gemma":[0.9812088,0.00015428645,0.017057966,0.00010286843,0.00006286074,0.0010518935,0.00016730459,0.000012958692,0.00018117268],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92492837,0.06367973,0.0024354707,0.0051957453,0.0029616181,0.0007992187],"domain_scores_gemma":[0.9347849,0.039823163,0.009230628,0.013377991,0.0021057513,0.0006775621],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.053064603,0.00082304643,0.0013956724,0.0011987506,0.0012598808,0.0010929243,0.0017757331,0.0021309624,0.0010652469],"category_scores_gemma":[0.09152945,0.00071690604,0.0031660346,0.0011948779,0.0013696084,0.0010931955,0.0013549145,0.0010721014,0.00013850612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03558174,0.009161017,0.8326392,0.00092087785,0.009051181,0.0030089712,0.0068074935,0.0045123682,0.0053806845,0.005223946,0.00096210575,0.086750306],"study_design_scores_gemma":[0.010091256,0.07583677,0.8112626,0.00042434127,0.0083035575,0.0043834522,0.003419843,0.059022155,0.00867301,0.009208322,0.008935555,0.00043919508],"about_ca_topic_score_codex":0.0032211551,"about_ca_topic_score_gemma":0.002950156,"teacher_disagreement_score":0.9469354,"about_ca_system_score_codex":0.0009787588,"about_ca_system_score_gemma":0.0011789544,"threshold_uncertainty_score":0.28063577},"labels":[],"label_agreement":null},{"id":"W2270862809","doi":"10.6000/1929-6029.2016.05.01.5","title":"Individualized Absolute Risk Calculations for Persons with Multiple Chronic Conditions: Embracing Heterogeneity, Causality, and Competing Events","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Outcome (game theory); Absolute risk reduction; Causal inference; Population; Medicine; Inference; Absolute (philosophy); Causality (physics); Intensive care medicine; Psychology; Actuarial science; Computer science; Economics","score_opus":0.3398958655905685,"score_gpt":0.5470222190976908,"score_spread":0.20712635350712227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270862809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010003054,0.0016949936,0.98271906,0.0018963631,0.00018976358,0.00043239075,0.00032968936,0.00031400702,0.0024206627],"genre_scores_gemma":[0.2847869,0.0016454558,0.7080205,0.0012264211,0.00042877946,0.001613863,0.00056082004,0.00026436851,0.0014528502],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93597025,0.050466295,0.003544616,0.0032592714,0.006279864,0.00047966148],"domain_scores_gemma":[0.80002326,0.16312264,0.014804424,0.01574551,0.0053973505,0.0009068303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06830724,0.0012971084,0.0021790764,0.0046029473,0.0007390396,0.0041521704,0.0028690214,0.0019910443,0.0031844203],"category_scores_gemma":[0.25612637,0.0007901287,0.0025282225,0.0038559865,0.0025967788,0.004434388,0.0050503933,0.0055353064,0.00051921606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005331027,0.00017051479,0.044250682,0.0013318236,0.0024262802,0.00040662297,0.0016379586,0.14064528,0.00059226516,0.41084236,0.014303317,0.3828598],"study_design_scores_gemma":[0.00015389496,0.00040129115,0.011967534,0.00109405,0.0006202591,0.00083182054,0.00036867821,0.26902834,0.001472441,0.6921696,0.021695552,0.00019663238],"about_ca_topic_score_codex":0.002357633,"about_ca_topic_score_gemma":0.0021535258,"teacher_disagreement_score":0.06830724,"about_ca_system_score_codex":0.0017934147,"about_ca_system_score_gemma":0.0025339446,"threshold_uncertainty_score":0.36124754},"labels":[],"label_agreement":null},{"id":"W2275037285","doi":"10.6000/1929-6029.2016.05.01.7","title":"Higher Performance of QuantiFERON TB Compared to Tuberculin Skin Test in Latent Tuberculosis Infection Prospective Diagnosis","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Tuberculin; Latent tuberculosis; QuantiFERON; Tuberculosis; Internal medicine; Skin test; Gastroenterology; Immunology; Mycobacterium tuberculosis; Pathology","score_opus":0.0681591330856294,"score_gpt":0.44535504847656127,"score_spread":0.3771959153909319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2275037285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953164,0.0016184028,0.000979868,0.000077440374,0.000074447125,0.000022075768,0.00017326108,0.000028738079,0.0017093887],"genre_scores_gemma":[0.999368,0.00009945949,0.0002536623,0.000013818718,0.000025919911,0.000004441124,0.000111186615,0.000003382188,0.00011991728],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994603,0.002570026,0.0005614382,0.00093752536,0.0009853848,0.00034262406],"domain_scores_gemma":[0.9776382,0.014213545,0.004216812,0.0013293904,0.0016929688,0.0009091226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01053085,0.00060023164,0.00045654908,0.0012154111,0.0003838004,0.0014149845,0.00050565845,0.00066970143,0.002237325],"category_scores_gemma":[0.023601012,0.0003109886,0.0006495614,0.0007400178,0.0005491391,0.0008001884,0.0007181102,0.0007325009,0.00094891916],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033107822,0.000045866716,0.995226,0.00003245986,0.00005273058,0.000057197794,0.00009429683,0.00013327402,0.0005734239,0.000023165383,0.000067272245,0.0033632226],"study_design_scores_gemma":[0.000025603364,0.0010501799,0.99237084,0.000051146137,0.00013684871,0.0013924186,0.00027755386,0.002180706,0.0017259362,0.00013346563,0.0006379025,0.000017471304],"about_ca_topic_score_codex":0.0007207498,"about_ca_topic_score_gemma":0.0005093621,"teacher_disagreement_score":0.01053085,"about_ca_system_score_codex":0.00028249453,"about_ca_system_score_gemma":0.00035338302,"threshold_uncertainty_score":0.05569309},"labels":[],"label_agreement":null},{"id":"W2282662065","doi":"10.6000/1929-6029.2016.05.01.6","title":"Can a Mendelian Randomization Study Predict the Results of a Clinical Trial? Yes and No","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mendelian randomization; Observational study; Randomized controlled trial; Confounding; Causal inference; Research design; Randomization; Clinical study design; Computer science; Clinical trial; Psychology; Medicine; Econometrics; Bioinformatics; Statistics; Biology; Genetics; Mathematics; Genetic variants","score_opus":0.09770947439309281,"score_gpt":0.5050527921782754,"score_spread":0.40734331778518257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282662065","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005974316,0.084482364,0.3311311,0.52382755,0.043125574,0.0019679796,0.00084062864,0.0007856318,0.007864857],"genre_scores_gemma":[0.25121275,0.039323214,0.3510071,0.2956693,0.049217664,0.009700155,0.0005066835,0.0005075126,0.0028556273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.51885664,0.3910916,0.031569965,0.02213217,0.034642614,0.0017069768],"domain_scores_gemma":[0.17406671,0.7553961,0.029873623,0.029718874,0.009329679,0.0016150246],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.49339727,0.0018195586,0.0073376144,0.00396002,0.001901816,0.009699074,0.0058140885,0.015789237,0.0057325345],"category_scores_gemma":[0.8044101,0.0014406362,0.0048411717,0.003435202,0.022751933,0.019574823,0.00423734,0.016860517,0.0022749114],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005688519,0.0002781306,0.013383121,0.014087372,0.012296544,0.0007652126,0.0029028303,0.0050531686,0.0004464512,0.5530336,0.10416159,0.28790358],"study_design_scores_gemma":[0.002075898,0.0005168407,0.0020058944,0.00720694,0.0019156135,0.0004364709,0.00034619897,0.009470649,0.0004417351,0.9281897,0.04709735,0.00029667187],"about_ca_topic_score_codex":0.0017373346,"about_ca_topic_score_gemma":0.0013919895,"teacher_disagreement_score":0.50660276,"about_ca_system_score_codex":0.004284575,"about_ca_system_score_gemma":0.007996467,"threshold_uncertainty_score":0.62473136},"labels":[],"label_agreement":null},{"id":"W2286407613","doi":"10.6000/1929-6029.2016.05.01.1","title":"The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Randomization; Cluster randomised controlled trial; Intervention (counseling); Randomized controlled trial; Cluster (spacecraft); Inference; Clinical trial; Sample size determination; Psychology; Computer science; Medicine; Statistics; Mathematics; Artificial intelligence; Psychiatry","score_opus":0.5242000113572298,"score_gpt":0.596896738966718,"score_spread":0.0726967276094882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286407613","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014026926,0.013101613,0.911023,0.027590936,0.013579096,0.026047701,0.00065804983,0.0013122188,0.0052846754],"genre_scores_gemma":[0.03447711,0.0059353076,0.8263788,0.016644407,0.0052987183,0.10898258,0.00031292476,0.00064380193,0.0013262457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.141144,0.7970116,0.020758012,0.011296709,0.02880562,0.0009840496],"domain_scores_gemma":[0.16967708,0.7327199,0.02507511,0.05165471,0.018970773,0.00190239],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5691495,0.0033922442,0.012131107,0.0053421766,0.004095685,0.012632636,0.00987978,0.011594559,0.0098274285],"category_scores_gemma":[0.75547534,0.0029537333,0.006265513,0.009068016,0.017756613,0.010162191,0.008082154,0.022931054,0.00419804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060851662,0.00043989098,0.0021917392,0.0236702,0.006175278,0.0004676835,0.0049267923,0.008737424,0.00081153767,0.47159657,0.08692564,0.387972],"study_design_scores_gemma":[0.011600657,0.0029570675,0.001870406,0.01794957,0.0018576724,0.0010816905,0.0005288941,0.04220772,0.0013195534,0.75160086,0.1663338,0.0006920654],"about_ca_topic_score_codex":0.0026810926,"about_ca_topic_score_gemma":0.0029516963,"teacher_disagreement_score":0.4308505,"about_ca_system_score_codex":0.011513303,"about_ca_system_score_gemma":0.024210302,"threshold_uncertainty_score":0.53131545},"labels":[],"label_agreement":null},{"id":"W2302358022","doi":"10.6000/1929-6029.2015.04.04.7","title":"Determinants of Utilization of Maternal Healthcare Services in Ethiopia","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Childbirth; Residence; Logistic regression; Medicine; Health care; Postnatal Care; Pregnancy; Birth order; Demography; Nursing; Family medicine; Environmental health; Population; Economic growth","score_opus":0.13700695201104204,"score_gpt":0.5257102762895632,"score_spread":0.38870332427852117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302358022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9980343,0.00047281774,0.00003996222,0.00012435763,0.0000059168196,0.000006542593,0.0003670416,0.0000021406995,0.0009468944],"genre_scores_gemma":[0.99945897,0.00025306462,0.000039540497,0.000017584754,0.000003030474,0.0000041257863,0.000098688724,4.7678074e-7,0.00012467205],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997098,0.000098703225,0.00003601011,0.000027209813,0.000049026457,0.00007933161],"domain_scores_gemma":[0.9995184,0.00014245686,0.00017112866,0.000013177463,0.000049591323,0.000105361956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004477847,0.000115006595,0.000108004024,0.00064707315,0.00019124814,0.00052078895,0.00017063343,0.00011739202,0.0011561706],"category_scores_gemma":[0.000982767,0.00013957672,0.0001646649,0.0007888535,0.00011799051,0.00013051754,0.00025705103,0.00022795047,0.00009029072],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021659167,0.000043251523,0.99558485,0.000024235127,0.00004439657,0.00015461796,0.00015278178,0.00014394725,0.00015893929,0.0001426418,0.00024048352,0.0032882192],"study_design_scores_gemma":[0.0000019295994,0.000022134796,0.9981205,0.000031639906,0.000011987733,0.00018845571,0.0006489713,0.00027049333,0.00006143062,0.000029420788,0.00061005924,0.000003050353],"about_ca_topic_score_codex":0.01445154,"about_ca_topic_score_gemma":0.016903317,"teacher_disagreement_score":0.01445154,"about_ca_system_score_codex":0.00049624685,"about_ca_system_score_gemma":0.0005137388,"threshold_uncertainty_score":0.028734803},"labels":[],"label_agreement":null},{"id":"W2317634038","doi":"10.6000/1929-6029.2015.04.04.6","title":"Specification of Variance-Covariance Structure in Bivariate Mixed Model for Unequally Time-Spaced Longitudinal Data","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autocorrelation; Bivariate analysis; Covariate; Statistics; Multivariate statistics; Covariance; Mathematics; Data set; Bivariate data; Econometrics","score_opus":0.4536980794176892,"score_gpt":0.5561041835634061,"score_spread":0.10240610414571688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317634038","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007339227,0.00023333314,0.9914135,0.00019434212,0.000043110373,0.0001333108,0.00021861582,0.00019768308,0.00022690948],"genre_scores_gemma":[0.26070517,0.0009755043,0.73093414,0.00037872166,0.00017809469,0.0026951549,0.0018099997,0.00024182723,0.0020814114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96128064,0.030346308,0.0012788245,0.0042603933,0.0019211507,0.00091259216],"domain_scores_gemma":[0.9315835,0.058612026,0.0031992488,0.003629041,0.0025753002,0.00040079188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048277866,0.0019928575,0.0031886916,0.0026303302,0.0010645093,0.0028361212,0.004070242,0.0033268298,0.0028362062],"category_scores_gemma":[0.09170668,0.0016755133,0.0043524336,0.002853294,0.0022939502,0.003414017,0.0023705303,0.0041139517,0.0007626121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007180439,0.00029204454,0.024711572,0.0010433452,0.0014120359,0.00096141675,0.0018130277,0.35567668,0.0027431424,0.5028508,0.003294269,0.10448368],"study_design_scores_gemma":[0.00008874271,0.00021062598,0.0028732491,0.00014939407,0.00021163515,0.00017119362,0.00013681196,0.8608453,0.0007681148,0.13208677,0.0023811907,0.00007688828],"about_ca_topic_score_codex":0.008931942,"about_ca_topic_score_gemma":0.008134987,"teacher_disagreement_score":0.048277866,"about_ca_system_score_codex":0.0020339729,"about_ca_system_score_gemma":0.004121212,"threshold_uncertainty_score":0.25532085},"labels":[],"label_agreement":null},{"id":"W2328099446","doi":"10.6000/1929-6029.2014.03.02.7","title":"Application of Cox’s Proportional Hazard Model and Construction of Life Table for Under-Five","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Care Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Covariate; Proportional hazards model; Statistics; Survival analysis; Regression analysis; Demography; Medicine; Breastfeeding; Mathematics; Pediatrics","score_opus":0.11577485962200976,"score_gpt":0.5676177008124439,"score_spread":0.45184284119043416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328099446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076274216,0.00076731446,0.90512997,0.0008182549,0.00029606035,0.002040839,0.0114735905,0.00077946484,0.0024201784],"genre_scores_gemma":[0.5375073,0.0014511775,0.4244262,0.00015529839,0.00027659623,0.0065085907,0.020896807,0.000185997,0.008591967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9973826,0.0014274225,0.00018264212,0.00045059613,0.00035570428,0.00020106965],"domain_scores_gemma":[0.99378407,0.004776641,0.0004180899,0.0005142888,0.00039496078,0.00011189429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069808643,0.00046411814,0.0010454446,0.002524818,0.00044439602,0.0010745114,0.0017006608,0.00074049894,0.007670497],"category_scores_gemma":[0.015131452,0.00045913167,0.002164843,0.0025914293,0.00029364793,0.0007699045,0.0008436546,0.0017977982,0.00095382665],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001252747,0.00053689734,0.23141105,0.0012216399,0.0014479039,0.0029508804,0.0017362011,0.3466138,0.0014822175,0.13280052,0.025302287,0.25324383],"study_design_scores_gemma":[0.00016330523,0.000782411,0.040811464,0.00016277938,0.00039385757,0.0013569186,0.000658214,0.8677297,0.0010403276,0.05599431,0.030770037,0.00013660092],"about_ca_topic_score_codex":0.008060931,"about_ca_topic_score_gemma":0.004305506,"teacher_disagreement_score":0.008060931,"about_ca_system_score_codex":0.0007378141,"about_ca_system_score_gemma":0.0024729972,"threshold_uncertainty_score":0.03691882},"labels":[],"label_agreement":null},{"id":"W2409439212","doi":"10.6000/1929-6029.2016.05.02.6","title":"Potential DNA Barcoding for Identification of Large Leaf Homalomena (Homalomena pendula (Blume) Bakh.f.)","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Malaria Research and Control","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Đại học Huế","keywords":"Municipal corporation; Malaria; Metropolitan area; Geography; Socioeconomics; Time series; Environmental health; Medicine; Statistics; Economics; Mathematics; Immunology","score_opus":0.036243367572523395,"score_gpt":0.42758683337241765,"score_spread":0.39134346579989426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409439212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95393425,0.006717073,0.026178788,0.0006151163,0.00025495657,0.00034271955,0.0071118088,0.00022950828,0.0046158414],"genre_scores_gemma":[0.9298808,0.0014267432,0.058217738,0.0004986003,0.00005758385,0.00021204176,0.0066010584,0.000032920583,0.0030725712],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964905,0.00003826916,0.000021452646,0.00015872144,0.00009411488,0.000038345774],"domain_scores_gemma":[0.9995239,0.0000878089,0.00017776784,0.000022096858,0.00014994487,0.000038584276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034420498,0.00031771592,0.00027453474,0.0017720019,0.00049790804,0.00040417272,0.00046425508,0.0005887019,0.0012179589],"category_scores_gemma":[0.0006801079,0.00014983612,0.00029680057,0.0009257469,0.00032834942,0.0003897807,0.0002729956,0.00044658477,0.0004943699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027169546,0.00021870565,0.08272394,0.0009307466,0.00008992806,0.00068941497,0.0012593133,0.00064695004,0.73829824,0.00076446723,0.0014684064,0.17263822],"study_design_scores_gemma":[0.000097731296,0.0009550146,0.6595864,0.00064594514,0.00054717384,0.0026487259,0.00371608,0.018617665,0.243232,0.0015239398,0.06829817,0.0001311779],"about_ca_topic_score_codex":0.0044482024,"about_ca_topic_score_gemma":0.010519974,"teacher_disagreement_score":0.0044482024,"about_ca_system_score_codex":0.00047907274,"about_ca_system_score_gemma":0.00039172065,"threshold_uncertainty_score":0.008844614},"labels":[],"label_agreement":null},{"id":"W2462213098","doi":"10.6000/1929-6029.2016.05.02.2","title":"The Usefulness of Maximum Daily Temperatures Versus Defined Heatwave Periods in Assessing the Impact of Extreme Heat on ED Admissions for Chronic Conditions","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Emergency department; Odds ratio; Logistic regression; Maximum temperature; Linear regression; Confidence interval; Demography; Internal medicine; Statistics; Mathematics; Atmospheric sciences; Psychiatry","score_opus":0.21404325671855434,"score_gpt":0.5164090902383203,"score_spread":0.3023658335197659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462213098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967662,0.00044420967,0.0010005041,0.000090366804,0.000015822696,0.00003393616,0.0007170999,0.000012128362,0.0009196854],"genre_scores_gemma":[0.9986243,0.00008287788,0.0006986305,0.000022725391,0.00002312771,0.000020875364,0.00038832283,0.0000036665556,0.0001354596],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957671,0.002511324,0.0004126863,0.00040417232,0.0006983838,0.00020627456],"domain_scores_gemma":[0.9800157,0.011342566,0.0060835346,0.0006506488,0.0012759287,0.0006315841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007442259,0.0004307644,0.0004093202,0.0014136523,0.0002258313,0.0009145026,0.00045878845,0.00045453446,0.0012850154],"category_scores_gemma":[0.0215738,0.00021013658,0.0009576324,0.0010303687,0.00043002694,0.0008615822,0.0010691615,0.00072179525,0.00029152623],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004292952,0.000045516186,0.99497896,0.000056431538,0.00015910485,0.000015173631,0.000095341304,0.00029148159,0.00024358962,0.000016772183,0.00009137232,0.0035770081],"study_design_scores_gemma":[0.0000062746067,0.00025624136,0.9985043,0.000017289687,0.00002989154,0.00004134374,0.00014571148,0.00081571145,0.00008582856,0.000017601851,0.00007525575,0.0000046368127],"about_ca_topic_score_codex":0.0026946166,"about_ca_topic_score_gemma":0.0058069127,"teacher_disagreement_score":0.007442259,"about_ca_system_score_codex":0.00031205988,"about_ca_system_score_gemma":0.0003221271,"threshold_uncertainty_score":0.039358914},"labels":[],"label_agreement":null},{"id":"W2465153955","doi":"10.6000/1929-6029.2016.05.02.1","title":"Survival Analysis of Duration of Breastfeeding and Associated Factors of Early Cessation of Breastfeeding in Ethiopia","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Breastfeeding Practices and Influences","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Breastfeeding; Medicine; Proportional hazards model; Demography; Hazard ratio; Parity (physics); Survival analysis; Breastfeeding promotion; Pregnancy; Pediatrics; Confidence interval","score_opus":0.07517982896147958,"score_gpt":0.4375793576493875,"score_spread":0.3623995286879079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465153955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99858737,0.00052814576,0.00026244312,0.00004114202,0.000005473286,0.000004422901,0.00035584287,0.0000025784877,0.00021257754],"genre_scores_gemma":[0.9991596,0.00024116799,0.00017231259,0.000009126529,0.000005388254,0.000006397026,0.00029168112,0.0000012351309,0.00011313248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99965894,0.00013317431,0.00003536392,0.00004165743,0.00004281626,0.00008805701],"domain_scores_gemma":[0.998069,0.0008842737,0.00059618306,0.000083344545,0.00015752461,0.00020962369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095320155,0.00014751656,0.00021164127,0.0007704599,0.00018358193,0.00038330417,0.00016609817,0.00018471428,0.0009663394],"category_scores_gemma":[0.00265117,0.00010885787,0.0004941532,0.0006901372,0.00009041755,0.00024211487,0.00023420615,0.000344486,0.00009138265],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032847436,0.000034614637,0.993262,0.000019551733,0.000102204285,0.000116505435,0.00008254841,0.00034660244,0.00018698956,0.000054776177,0.000105269544,0.0053605023],"study_design_scores_gemma":[0.000009472546,0.00019602122,0.99547476,0.000030471469,0.00008815842,0.000489972,0.00046564516,0.0024391827,0.00015122836,0.000115638075,0.00053077977,0.000008605256],"about_ca_topic_score_codex":0.0031957151,"about_ca_topic_score_gemma":0.001981585,"teacher_disagreement_score":0.0031957151,"about_ca_system_score_codex":0.00021380538,"about_ca_system_score_gemma":0.0005001306,"threshold_uncertainty_score":0.0063542128},"labels":[],"label_agreement":null},{"id":"W2468065610","doi":"10.6000/1929-6029.2016.05.02.5","title":"A Declaratory Model of Generalized Regression Neural Network (GRNN) for Estimating Sleep Apnea Index in the Elderly Suffering from Sleep Disturbance","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalization; Artificial neural network; Sleep apnea; Regression analysis; Regression; Obstructive sleep apnea; Artificial intelligence; Hypopnea; Computer science; Medicine; Machine learning; Statistics; Apnea; Mathematics; Polysomnography; Cardiology; Internal medicine","score_opus":0.06940681288043382,"score_gpt":0.42094301055654615,"score_spread":0.3515361976761123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468065610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46926394,0.0037633155,0.51456535,0.0017689903,0.0006533312,0.00028027018,0.0016204058,0.0011920495,0.0068923947],"genre_scores_gemma":[0.9805844,0.00034419922,0.014920743,0.00011917383,0.00006990971,0.00015885245,0.0006219194,0.00003081111,0.0031499239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918634,0.0002725686,0.00005180833,0.0003160877,0.00006236197,0.000110829365],"domain_scores_gemma":[0.9982052,0.0010200803,0.0002139309,0.00008473587,0.00040356768,0.00007251099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031328888,0.0012464907,0.00091043295,0.0007841718,0.00053968665,0.0011324384,0.001678177,0.0013548938,0.002826716],"category_scores_gemma":[0.0056462465,0.00040902864,0.0010730799,0.0004667177,0.0005569124,0.0007304111,0.000994683,0.0015620668,0.00054920453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005822161,0.00027722237,0.048404973,0.0002288109,0.000468413,0.00045174788,0.00023507442,0.856197,0.0018674638,0.0040493547,0.002699979,0.084537774],"study_design_scores_gemma":[0.000005445347,0.000042380332,0.0024707597,0.000014839742,0.000022350614,0.00002174225,0.000018623788,0.99636227,0.00007987676,0.0008128333,0.00013996937,0.000008948703],"about_ca_topic_score_codex":0.023463657,"about_ca_topic_score_gemma":0.015569375,"teacher_disagreement_score":0.023463657,"about_ca_system_score_codex":0.00091639056,"about_ca_system_score_gemma":0.0008755512,"threshold_uncertainty_score":0.046654165},"labels":[],"label_agreement":null},{"id":"W2469474306","doi":"10.6000/1929-6029.2016.05.02.3","title":"Intention-to-Treat Analysis but for Treatment Intention: How should Consumer Product Randomized Controlled Trials be Analyzed?","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Philip Morris International","keywords":"Blinding; Randomized controlled trial; Randomization; Protocol (science); Product (mathematics); De facto; Population; Intention-to-treat analysis; Marketing; Medicine; Psychology; Business; Alternative medicine; Mathematics; Surgery","score_opus":0.4179813978358019,"score_gpt":0.5845818989366804,"score_spread":0.16660050110087854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469474306","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004024238,0.062436838,0.6959597,0.1856772,0.031674042,0.0098856725,0.0012573862,0.0012130326,0.007871896],"genre_scores_gemma":[0.1323184,0.015430093,0.6822657,0.08911582,0.015011911,0.06261583,0.00080582267,0.000718716,0.0017177413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.21280807,0.7014348,0.03424969,0.014834207,0.035408385,0.0012648958],"domain_scores_gemma":[0.090851106,0.8250283,0.02517801,0.039881054,0.017896893,0.0011646524],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.62631243,0.0035057268,0.016538009,0.0065564676,0.0028433166,0.012654935,0.007732139,0.019710282,0.007715189],"category_scores_gemma":[0.8266307,0.0025790513,0.009033319,0.010014057,0.02312676,0.016232533,0.003923498,0.027901106,0.0031463008],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005301989,0.00050586456,0.0034385603,0.055027355,0.013055871,0.00043759332,0.0055518947,0.0056944187,0.0005677435,0.55424553,0.09868908,0.25748417],"study_design_scores_gemma":[0.0030447482,0.0010083119,0.0016992423,0.023186468,0.0033817664,0.00027268814,0.00047206576,0.021592082,0.0010165807,0.8950243,0.048966214,0.0003355766],"about_ca_topic_score_codex":0.0018432864,"about_ca_topic_score_gemma":0.001085919,"teacher_disagreement_score":0.37368757,"about_ca_system_score_codex":0.00846554,"about_ca_system_score_gemma":0.018024001,"threshold_uncertainty_score":0.46082336},"labels":[],"label_agreement":null},{"id":"W2474582416","doi":"10.6000/1929-6029.2016.05.02.4","title":"Confidence Intervals for the Population Correlation Coefficient","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidence interval; Statistics; Correlation coefficient; Bivariate analysis; Mathematics; Population; CDF-based nonparametric confidence interval; Correlation; Robust confidence intervals; Monte Carlo method; Fisher transformation; Pearson product-moment correlation coefficient; Coverage probability; Range (aeronautics); Standard deviation; Demography; Engineering","score_opus":0.27318694280250544,"score_gpt":0.5906416889266166,"score_spread":0.3174547461241112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474582416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024786828,0.005286066,0.9639078,0.00052023144,0.00022604364,0.00013548754,0.00048490256,0.0006351678,0.0040175156],"genre_scores_gemma":[0.6368718,0.0038591418,0.35371375,0.00047945743,0.00054217223,0.0011311575,0.001713085,0.00034921392,0.0013402177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9652739,0.0184013,0.0021455796,0.0058124117,0.0074419584,0.00092475716],"domain_scores_gemma":[0.6502081,0.29709673,0.01555387,0.015916217,0.020087719,0.0011373962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040288504,0.0014113642,0.002112591,0.0061789365,0.0010338688,0.003573092,0.0031572357,0.0030516507,0.0035516247],"category_scores_gemma":[0.31412485,0.00051286345,0.0020997848,0.004129185,0.0025388072,0.0044282246,0.002732948,0.0038912205,0.0007726976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010018514,0.00017186029,0.03286829,0.0020875311,0.001489757,0.0009896155,0.0016630967,0.28150398,0.0042054304,0.4064007,0.008367817,0.25925007],"study_design_scores_gemma":[0.00020627386,0.00063396886,0.01948436,0.0023050774,0.000712509,0.0020520424,0.0009782417,0.6820204,0.008894009,0.2578453,0.024321906,0.00054587773],"about_ca_topic_score_codex":0.002523979,"about_ca_topic_score_gemma":0.0008091797,"teacher_disagreement_score":0.040288504,"about_ca_system_score_codex":0.0014043861,"about_ca_system_score_gemma":0.0012106841,"threshold_uncertainty_score":0.21306854},"labels":[],"label_agreement":null},{"id":"W2507580714","doi":"10.6000/1929-6029.2016.05.03.2","title":"A Method to Assess Neurological Effectiveness of a Spinal Adjustment for an Individual Patient: A Descriptive Study","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Heart rate and cardiovascular health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chiropractic; Normative; Medicine; Physical therapy; Intervention (counseling); Psychology; Physical medicine and rehabilitation; Psychiatry; Alternative medicine","score_opus":0.25113853087529825,"score_gpt":0.5563677549307102,"score_spread":0.305229224055412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507580714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8553651,0.0013693449,0.08955738,0.00061616965,0.00028317518,0.0325464,0.008542295,0.0003000505,0.011420109],"genre_scores_gemma":[0.78845346,0.00063107605,0.12700832,0.00055974984,0.00016336385,0.07527325,0.004549387,0.00012759119,0.0032337145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98349416,0.008634582,0.0031970115,0.0013899155,0.002932895,0.00035144278],"domain_scores_gemma":[0.9320806,0.043341782,0.008400993,0.005684389,0.009901207,0.00059092476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02921874,0.0005613126,0.0006288658,0.0066255815,0.001008602,0.0013162538,0.00091263413,0.00064974156,0.0039965427],"category_scores_gemma":[0.047544286,0.00041479527,0.0012116815,0.0036060968,0.0012307807,0.0012067518,0.0011784722,0.00077103416,0.00093946303],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039210995,0.0058707246,0.7498821,0.0027681503,0.00079022435,0.0006201608,0.034198046,0.00066353387,0.0064632096,0.0061766524,0.008460291,0.18018575],"study_design_scores_gemma":[0.0009730503,0.027680837,0.8132549,0.0018024194,0.00091356534,0.0024443744,0.06033622,0.0069283005,0.01867647,0.003806581,0.06275337,0.00042984734],"about_ca_topic_score_codex":0.0013508224,"about_ca_topic_score_gemma":0.0019615868,"teacher_disagreement_score":0.02921874,"about_ca_system_score_codex":0.0013780987,"about_ca_system_score_gemma":0.0017820343,"threshold_uncertainty_score":0.15452534},"labels":[],"label_agreement":null},{"id":"W2508078670","doi":"10.6000/1929-6029.2016.05.03.1","title":"Constrained Bayesian Method of Composite Hypotheses Testing: Singularities and Capabilities","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shota Rustaveli National Science Foundation; National Science Foundation","keywords":"Bayesian probability; A priori and a posteriori; Simple (philosophy); Composite number; Computer science; Computation; Mathematics; Statistical hypothesis testing; Applied mathematics; Algorithm; Artificial intelligence; Statistics","score_opus":0.245924379491424,"score_gpt":0.5516594279887557,"score_spread":0.3057350484973317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508078670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014729841,0.0005464702,0.99640733,0.00023882171,0.000034245488,0.000029786583,0.000022345614,0.00006527142,0.0011827353],"genre_scores_gemma":[0.1362291,0.0019155615,0.8584215,0.0004725782,0.00028337646,0.0005083161,0.00014133214,0.0001602947,0.0018679728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9856851,0.009610136,0.00039113706,0.0010973488,0.0029608896,0.00025537892],"domain_scores_gemma":[0.9535761,0.03925271,0.001736248,0.0025315245,0.0023020422,0.00060135976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0220868,0.0012157029,0.0021040435,0.0033553438,0.0010018436,0.0028043706,0.0029190746,0.0019937872,0.0036418908],"category_scores_gemma":[0.08696305,0.00082407764,0.0013466714,0.0027629791,0.004637466,0.0039050593,0.005247297,0.004236493,0.0009315756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017260993,0.00005287012,0.0017767687,0.0005738245,0.00016459124,0.0002921758,0.00044358018,0.0549966,0.0018522,0.7768264,0.0027410248,0.16010731],"study_design_scores_gemma":[0.000043130163,0.00009357662,0.0005401043,0.00017756216,0.00006235973,0.00040091312,0.000063832405,0.40820748,0.0010574936,0.58395314,0.005329569,0.000070839276],"about_ca_topic_score_codex":0.0017082887,"about_ca_topic_score_gemma":0.000833704,"teacher_disagreement_score":0.0220868,"about_ca_system_score_codex":0.001136772,"about_ca_system_score_gemma":0.0024173371,"threshold_uncertainty_score":0.11680758},"labels":[],"label_agreement":null},{"id":"W2508330948","doi":"10.6000/1929-6029.2016.05.03.9","title":"Statistical Performance Effect of Feature Selection Techniques on Eye State Prediction Using EEG","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Çukurova Üniversitesi; University of Rwanda","keywords":"Electroencephalography; Computer science; Artificial intelligence; Feature selection; Pattern recognition (psychology); Classifier (UML); Speech recognition","score_opus":0.04292032938874533,"score_gpt":0.4378164368764633,"score_spread":0.394896107487718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508330948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93948835,0.0013790834,0.057243112,0.00023528736,0.00010599297,0.000051472587,0.00019582818,0.0005076123,0.00079325994],"genre_scores_gemma":[0.9833161,0.00019949012,0.015810413,0.000030229896,0.000034028086,0.000023788303,0.000284965,0.00001869826,0.0002823155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833566,0.0007035236,0.0001838917,0.00023858823,0.00038378898,0.00015457037],"domain_scores_gemma":[0.98999023,0.007879784,0.00045992626,0.00034993,0.0011880769,0.00013206975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051934537,0.000635873,0.0007843792,0.0008041564,0.00023031999,0.000499686,0.000257551,0.00053178077,0.00046239042],"category_scores_gemma":[0.010926104,0.00009778895,0.00055580563,0.0006199128,0.00020358802,0.000551906,0.00027536208,0.000563283,0.00019112126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004722802,0.00065990735,0.067432,0.0003027763,0.00048403963,0.00027828894,0.00027315345,0.07897451,0.041457944,0.0003425687,0.0022856141,0.80278647],"study_design_scores_gemma":[0.00011000119,0.004124016,0.12497117,0.000055362525,0.00033588888,0.0005239417,0.00031687942,0.8139326,0.053857185,0.00057948293,0.0011155177,0.00007802559],"about_ca_topic_score_codex":0.00081814796,"about_ca_topic_score_gemma":0.00049928896,"teacher_disagreement_score":0.0051934537,"about_ca_system_score_codex":0.00018850155,"about_ca_system_score_gemma":0.00027618266,"threshold_uncertainty_score":0.02746594},"labels":[],"label_agreement":null},{"id":"W2509307924","doi":"10.6000/1929-6029.2016.05.03.5","title":"Statistical Analyses of Mutually Exclusive Competing Risks in Neonatal Studies","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Neonatal Respiratory Health Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outcome (game theory); Event (particle physics); Test (biology); Chi-square test; Post-hoc analysis; Post hoc; Econometrics; Statistics; Statistical hypothesis testing; Computer science; Psychology; Mathematics; Medicine; Mathematical economics","score_opus":0.3771989157021033,"score_gpt":0.6347661424035501,"score_spread":0.2575672267014468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509307924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047253586,0.011359476,0.9235168,0.0013813904,0.0015047827,0.00825427,0.0031781178,0.0011053078,0.002446231],"genre_scores_gemma":[0.4335699,0.002030433,0.5087019,0.00084195123,0.0008188442,0.050169367,0.0016938779,0.0005518489,0.0016218419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.500689,0.44688207,0.018532733,0.0120641,0.019784873,0.002047194],"domain_scores_gemma":[0.2641869,0.69008493,0.017466357,0.023010178,0.004299433,0.00095225294],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2454701,0.0017339612,0.0049981163,0.0062164096,0.0011325128,0.0030902398,0.0043126317,0.0030737123,0.010958306],"category_scores_gemma":[0.48597518,0.0009678685,0.008620025,0.008668353,0.004017413,0.0034249916,0.0048229033,0.005593934,0.00074108556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.025222898,0.0011283344,0.13539355,0.06032019,0.07919066,0.0051867184,0.011422971,0.037610445,0.0050038723,0.22518629,0.03577696,0.3785571],"study_design_scores_gemma":[0.0033516104,0.019184446,0.09371492,0.012502411,0.019044321,0.004383313,0.0057667564,0.32793263,0.011539786,0.4032389,0.098510884,0.0008299588],"about_ca_topic_score_codex":0.0006348708,"about_ca_topic_score_gemma":0.00051140244,"teacher_disagreement_score":0.2454701,"about_ca_system_score_codex":0.0016439361,"about_ca_system_score_gemma":0.00322001,"threshold_uncertainty_score":0.9304697},"labels":[],"label_agreement":null},{"id":"W2510935139","doi":"10.6000/1929-6029.2016.05.03.8","title":"Measuring Modified Mass Energy Equivalence in Nutritional Epidemiology: A Proposal to Adapt the Biophysical Modelling Approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diet and metabolism studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dieting; Confounding; Population; Energy (signal processing); Environmental health; Sample (material); Medicine; Epidemiology; Gerontology; Psychology; Obesity; Mathematics; Weight loss; Statistics; Endocrinology; Pathology; Physics","score_opus":0.26542984739238085,"score_gpt":0.44745753466445487,"score_spread":0.18202768727207402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510935139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072444044,0.0014025389,0.97890455,0.008308749,0.00061060017,0.00022482003,0.00016139497,0.000258009,0.002884966],"genre_scores_gemma":[0.18971644,0.0030236605,0.7991659,0.002692492,0.0011582908,0.0012240489,0.00021759988,0.0001624193,0.002639182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9874178,0.008731983,0.0006940957,0.0013636529,0.0015694672,0.00022295238],"domain_scores_gemma":[0.9825522,0.012043852,0.0012706723,0.0023306108,0.0014550933,0.00034760605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020740861,0.0014887977,0.0017673377,0.004348394,0.0007701405,0.004508566,0.0044404548,0.003287764,0.0021352426],"category_scores_gemma":[0.04938453,0.0007951486,0.0025896663,0.003636028,0.0041846614,0.005522284,0.0040394836,0.0043632314,0.00086056115],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026176902,0.0006017361,0.050401706,0.00067784777,0.0005892255,0.000502534,0.001932066,0.0652224,0.0025450552,0.628285,0.006561057,0.24241954],"study_design_scores_gemma":[0.000058018453,0.00050211913,0.009722844,0.00030484732,0.00018308929,0.0006171672,0.0006693565,0.19207637,0.00086912187,0.77268624,0.022061791,0.0002490919],"about_ca_topic_score_codex":0.0039930176,"about_ca_topic_score_gemma":0.0022817228,"teacher_disagreement_score":0.020740861,"about_ca_system_score_codex":0.0014604665,"about_ca_system_score_gemma":0.002480748,"threshold_uncertainty_score":0.109689474},"labels":[],"label_agreement":null},{"id":"W2511974089","doi":"10.6000/1929-6029.2016.05.03.7","title":"Addressing the Challenge of P-Value and Sample Size when the Significance is Borderline: The Test of Random Duplication of Participants as a New Approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistical significance; Null hypothesis; Value (mathematics); p-value; Statistical hypothesis testing; Null (SQL); Psychology; Test (biology); Type I and type II errors; Statistics; Rule of thumb; Sample size determination; Clinical significance; Statistical power; Social psychology; Mathematics; Computer science; Data mining","score_opus":0.8044118254716207,"score_gpt":0.6252334575824052,"score_spread":0.17917836788921548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511974089","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013612979,0.031505097,0.6595583,0.26566115,0.016271617,0.002007078,0.00035995495,0.000628187,0.010395616],"genre_scores_gemma":[0.3735384,0.0059856623,0.5136682,0.08135889,0.014080084,0.009093283,0.00013514933,0.0005128949,0.0016275023],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.17198879,0.71598846,0.033149224,0.026362672,0.05105203,0.0014589635],"domain_scores_gemma":[0.038967453,0.9206929,0.009549016,0.020830946,0.00899993,0.00095973525],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.6934286,0.0023489627,0.011847481,0.010098539,0.0047189496,0.01243281,0.011887636,0.016859703,0.0030057472],"category_scores_gemma":[0.8932551,0.002331252,0.0054342914,0.006840246,0.06977114,0.02115275,0.015358916,0.033474334,0.0008568929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034709496,0.0002520448,0.008686851,0.008945058,0.00539115,0.002354103,0.012920979,0.003744638,0.0010689442,0.6058573,0.031091176,0.3162167],"study_design_scores_gemma":[0.0006796808,0.0008242232,0.001375561,0.00345487,0.0005687796,0.0010596238,0.0009498597,0.008890994,0.00079329166,0.96312964,0.018070059,0.00020347687],"about_ca_topic_score_codex":0.0024767653,"about_ca_topic_score_gemma":0.0015083583,"teacher_disagreement_score":0.30657142,"about_ca_system_score_codex":0.0090039605,"about_ca_system_score_gemma":0.011952631,"threshold_uncertainty_score":0.37805718},"labels":[],"label_agreement":null},{"id":"W2512944155","doi":"10.6000/1929-6029.2016.05.03.6","title":"Joint Survival Analysis of Time to Drug Change and a Terminal Event with Application to Drug Failure Analysis using Transplant Registry Data","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Covariate; Medicine; Drug; Survival analysis; Liver transplantation; Event (particle physics); Intensive care medicine; Transplantation; Statistics; Internal medicine; Pharmacology","score_opus":0.08625884042437952,"score_gpt":0.4326734361621603,"score_spread":0.3464145957377808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512944155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1792202,0.0013680551,0.8124525,0.0012517935,0.0002705957,0.0008975562,0.0025748005,0.0007018464,0.0012625881],"genre_scores_gemma":[0.8762358,0.0007157877,0.113124505,0.00025309104,0.0002430694,0.0021795654,0.0032549445,0.00014576591,0.0038475029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9732319,0.020542486,0.0012475747,0.0025660186,0.0013885432,0.0010235107],"domain_scores_gemma":[0.8957111,0.086136565,0.008001488,0.0065284693,0.0023142188,0.0013081772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049140144,0.0014438377,0.0033275129,0.0035748053,0.00088403,0.0018658361,0.0025242297,0.0020487478,0.0052516446],"category_scores_gemma":[0.06674658,0.0009456114,0.0065095406,0.0032981103,0.0015881857,0.0018946545,0.003143747,0.0029898384,0.0005818878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023605258,0.00067835016,0.16762589,0.00086095044,0.0057774675,0.0013731039,0.0009265911,0.6160317,0.0012008328,0.08324348,0.0043302253,0.115590885],"study_design_scores_gemma":[0.00017486743,0.0009138517,0.019369913,0.000091440095,0.00091045693,0.000281589,0.00012954086,0.94537634,0.0005259646,0.028935462,0.003202653,0.000087835324],"about_ca_topic_score_codex":0.010004036,"about_ca_topic_score_gemma":0.0057056015,"teacher_disagreement_score":0.049140144,"about_ca_system_score_codex":0.0014585361,"about_ca_system_score_gemma":0.0036242038,"threshold_uncertainty_score":0.25988102},"labels":[],"label_agreement":null},{"id":"W2517383475","doi":"10.6000/1929-6029.2016.05.03.3","title":"Non Invasive Cardiac Output Evaluation with CO2 Rebreathing Method for CRT Patients","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cardiac pacing and defibrillation studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cardiac resynchronization therapy; Medicine; Ejection fraction; Cardiology; Internal medicine; QRS complex; Heart failure; Sinus rhythm; Dilated cardiomyopathy; Cardiac output; Hemodynamics; Diastole; Blood pressure; Atrial fibrillation","score_opus":0.09863699820979194,"score_gpt":0.5000231434728099,"score_spread":0.4013861452630179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517383475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982809,0.0005266955,0.00029985476,0.000034782544,0.000011758197,0.000032728352,0.000111208545,0.0000051680513,0.0006969418],"genre_scores_gemma":[0.9990802,0.00014323626,0.00035451617,0.000037808637,0.00002808553,0.00004781721,0.00017322203,0.0000017100875,0.00013342978],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99980754,0.00005930649,0.00002690986,0.000043901517,0.000035275927,0.000027064849],"domain_scores_gemma":[0.9996289,0.00008135345,0.00013189486,0.000017429453,0.000038896243,0.00010153735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003434799,0.00034982775,0.00041747015,0.00032284684,0.00020651284,0.00027468792,0.00011660859,0.0002323277,0.0007967417],"category_scores_gemma":[0.00055878155,0.00005977672,0.00021069092,0.00018783234,0.00014605923,0.00013019584,0.00016651205,0.00024076016,0.0001486687],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020524135,0.00029043722,0.98134035,0.00007333332,0.00003541534,0.00053022976,0.000105209765,0.00012035734,0.0049815113,0.000018076435,0.00019660834,0.010256081],"study_design_scores_gemma":[0.00010822355,0.0019234347,0.9949733,0.000016909968,0.00005009752,0.00094941724,0.00008287189,0.00041503875,0.0010643088,0.000028365872,0.0003799085,0.000008076251],"about_ca_topic_score_codex":0.00021052605,"about_ca_topic_score_gemma":0.00044789037,"teacher_disagreement_score":0.0007967417,"about_ca_system_score_codex":0.00014097299,"about_ca_system_score_gemma":0.00018258844,"threshold_uncertainty_score":0.002665341},"labels":[],"label_agreement":null},{"id":"W2517515364","doi":"10.6000/1929-6029.2016.05.03.4","title":"The Simple Geometry of Correlated Regressors and IV Corrections","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Instrumental variable; Simple (philosophy); Variable (mathematics); Mathematics; Statistics; Variables; Econometrics; Estimation; Key (lock); Regression analysis; Omitted-variable bias; Geometry; Computer science; Mathematical analysis; Economics","score_opus":0.14411895743012743,"score_gpt":0.5256847146326933,"score_spread":0.38156575720256586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517515364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026615546,0.00064473227,0.9859999,0.0026530207,0.0002837455,0.00008498616,0.00028812783,0.00026701603,0.007116921],"genre_scores_gemma":[0.20502914,0.002829248,0.76758057,0.0028230087,0.001263837,0.00093210745,0.0007036312,0.00083227945,0.018006142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98430485,0.010544983,0.0007454994,0.0018243882,0.0021665797,0.00041377332],"domain_scores_gemma":[0.9613062,0.026987353,0.003931997,0.0053934953,0.002107617,0.00027322557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016023101,0.0013826558,0.0016994365,0.0018246049,0.00091798895,0.0033898316,0.0024621543,0.002499003,0.010519671],"category_scores_gemma":[0.10734759,0.0012463102,0.0018701516,0.0026057358,0.0054774797,0.003623305,0.0042547383,0.0046749217,0.0025990547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029244175,0.0000073570864,0.0014355553,0.00011047398,0.00006859217,0.00019721162,0.0001404126,0.01000392,0.00026849544,0.94615155,0.0046741623,0.036913134],"study_design_scores_gemma":[0.000030850606,0.000038807197,0.00040551645,0.000055412966,0.000033067394,0.00023644882,0.00004899766,0.021107556,0.00043047164,0.9618653,0.015725771,0.000021816373],"about_ca_topic_score_codex":0.0040067104,"about_ca_topic_score_gemma":0.0022749114,"teacher_disagreement_score":0.016023101,"about_ca_system_score_codex":0.0014509382,"about_ca_system_score_gemma":0.0022912202,"threshold_uncertainty_score":0.08473927},"labels":[],"label_agreement":null},{"id":"W2555124762","doi":"10.6000/1929-6029.2017.06.03.3","title":"Using Copulas to Select Prognostic Genes in Melanoma Patients","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"African Union","keywords":"False discovery rate; Copula (linguistics); Resampling; Statistics; Covariate; Mathematics; Gene selection; Parametric statistics; Computer science; Econometrics; Computational biology; Data mining; Microarray analysis techniques; Biology; Gene; Genetics","score_opus":0.09690763479675663,"score_gpt":0.4889209519417816,"score_spread":0.39201331714502496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555124762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5926361,0.00110974,0.40295723,0.0010422618,0.00008152359,0.00013784194,0.0006944059,0.00053859485,0.00080230576],"genre_scores_gemma":[0.9686216,0.0003419815,0.029370863,0.00017637729,0.00005798417,0.00010205559,0.0008419097,0.000067996065,0.00041922275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982691,0.0011334259,0.00006495847,0.0002772813,0.00010655967,0.00014865711],"domain_scores_gemma":[0.98460394,0.01252569,0.0011080063,0.00085914833,0.00059822213,0.00030500974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00873281,0.0008563309,0.0014697921,0.0015026669,0.00039288244,0.0011825743,0.00091615255,0.0007622391,0.0010807954],"category_scores_gemma":[0.025265219,0.0005283834,0.0016193443,0.0011564774,0.0007446592,0.00080377643,0.0008490871,0.0010696006,0.00030966106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011956396,0.00020913493,0.12004199,0.00012949175,0.0007599433,0.00097297557,0.00030750025,0.7967812,0.0043883645,0.008621508,0.0036388314,0.062953375],"study_design_scores_gemma":[0.000036577818,0.000054638123,0.0066946843,0.000010586145,0.00006182525,0.00006954515,0.000039221988,0.9865978,0.0005556859,0.005608703,0.00025390883,0.000016856464],"about_ca_topic_score_codex":0.0043286076,"about_ca_topic_score_gemma":0.0032168527,"teacher_disagreement_score":0.00873281,"about_ca_system_score_codex":0.0007341418,"about_ca_system_score_gemma":0.001206184,"threshold_uncertainty_score":0.046184063},"labels":[],"label_agreement":null},{"id":"W2560741072","doi":"10.6000/1929-6029.2016.05.04.1","title":"Robust Cox Regression as an Alternative Method to Estimate Adjusted Relative Risk in Prospective Studies with Common Outcomes","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Poisson regression; Proportional hazards model; Statistics; Regression analysis; Regression; Relative risk; Confidence interval; Econometrics; Linear regression; Regression dilution; Covariate; Mathematics; Regression diagnostic; Segmented regression; Polynomial regression; Medicine; Population","score_opus":0.2933030936406348,"score_gpt":0.6080564951913305,"score_spread":0.31475340155069575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560741072","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034576107,0.0011942409,0.9937651,0.00043368092,0.00012726724,0.00013107818,0.0003042304,0.00024673378,0.00034006228],"genre_scores_gemma":[0.20915277,0.0016078632,0.78475916,0.0006618396,0.0004976265,0.0012452871,0.0007107637,0.00038075945,0.0009838855],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93481445,0.05516871,0.0022771233,0.0032587813,0.0040982044,0.0003827701],"domain_scores_gemma":[0.7568678,0.20050493,0.01576743,0.02060159,0.0057458505,0.00051247556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06882866,0.0010521584,0.0015922451,0.003517482,0.00034736568,0.0017706554,0.0025747912,0.0012268131,0.0032071539],"category_scores_gemma":[0.252077,0.0006193869,0.0036317827,0.0036491635,0.0010744879,0.0018544879,0.001774235,0.0031442211,0.00046220332],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017490989,0.00022243807,0.06941679,0.0041432274,0.012989577,0.0010860598,0.0014486606,0.16787395,0.0043373695,0.25931397,0.01744402,0.45997488],"study_design_scores_gemma":[0.000744353,0.0014528505,0.029546896,0.0015532775,0.0031747008,0.0020342278,0.00039891634,0.5730895,0.0065616104,0.33225504,0.04876605,0.00042261695],"about_ca_topic_score_codex":0.0030668846,"about_ca_topic_score_gemma":0.0017339473,"teacher_disagreement_score":0.06882866,"about_ca_system_score_codex":0.0007905075,"about_ca_system_score_gemma":0.002261424,"threshold_uncertainty_score":0.3640051},"labels":[],"label_agreement":null},{"id":"W2560787432","doi":"10.6000/1929-6029.2016.05.04.2","title":"Parametric Modeling of Survival Data Based on Human Immune Virus (HIV) Infected Adult Patients under Highly Active Antiretroviral Therapy (HAART): A Case of Zewditu Referral Hospital, Addis Ababa (AA), Ethiopia","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Survival analysis; Gompertz function; Retrospective cohort study; Log-rank test; Referral; Cohort; Stage (stratigraphy); Medical record; Internal medicine; Statistics; Mathematics; Family medicine; Biology","score_opus":0.1280409555833059,"score_gpt":0.48090058194804264,"score_spread":0.3528596263647368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560787432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93755174,0.00064152805,0.057416666,0.000836069,0.00003626689,0.000114810755,0.0019992308,0.00012665948,0.0012771395],"genre_scores_gemma":[0.9916272,0.00024054806,0.005926606,0.000039227034,0.000018101287,0.000068123896,0.0011979919,0.0000136927865,0.00086846575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984091,0.0008818313,0.000096592266,0.00028729165,0.00012369323,0.00020145615],"domain_scores_gemma":[0.98692155,0.010668137,0.0010676483,0.0006762591,0.00042330392,0.00024303616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051038675,0.00060409756,0.00059418625,0.0010538639,0.00041860828,0.0015868216,0.0013452094,0.0011065112,0.0012554085],"category_scores_gemma":[0.012648717,0.00032631503,0.0012417305,0.0007434251,0.0006501355,0.00087381224,0.0009405494,0.0011241372,0.0002051248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006878582,0.00038749483,0.29856881,0.0002177368,0.00031070042,0.0038089708,0.0012687225,0.645336,0.001393338,0.016636511,0.002031025,0.029352883],"study_design_scores_gemma":[0.000016993823,0.00022252646,0.027823472,0.000050038027,0.000051816747,0.0009519722,0.0004671843,0.96079576,0.00037002724,0.007815677,0.0013969371,0.00003758882],"about_ca_topic_score_codex":0.008071676,"about_ca_topic_score_gemma":0.0066190376,"teacher_disagreement_score":0.008071676,"about_ca_system_score_codex":0.0009242914,"about_ca_system_score_gemma":0.00079679216,"threshold_uncertainty_score":0.026992202},"labels":[],"label_agreement":null},{"id":"W2580119140","doi":"10.6000/1929-6029.2016.05.04.4","title":"A Case-Control Study of Alcohol Consumption and Esophageal Cancer in the Northeast State of Mizoram, India","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Grants Commission","keywords":"Medicine; Esophageal cancer; Odds ratio; Body mass index; Betel; Family history; Cancer; Environmental health; Confidence interval; Epidemiology; Case-control study; Logistic regression; Internal medicine; Demography; Surgery","score_opus":0.07108508981975409,"score_gpt":0.46813764503803534,"score_spread":0.39705255521828126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580119140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99930036,0.00015494505,0.00008737339,0.000022710652,0.0000037577377,0.000038112445,0.00016340445,0.0000031595318,0.00022613385],"genre_scores_gemma":[0.99929965,0.00010319611,0.00012367198,0.000026922056,0.000009059099,0.000032586006,0.00022753484,0.0000015498144,0.00017585648],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999127,0.00030251863,0.00011681243,0.00017776607,0.00014923875,0.00012669957],"domain_scores_gemma":[0.9991142,0.00020000478,0.00028703865,0.00015582479,0.00013315302,0.00010973591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063429255,0.00029168525,0.00036646146,0.0012535744,0.0009183419,0.0006051383,0.00058730895,0.00038644305,0.0016365959],"category_scores_gemma":[0.0013270786,0.0005457051,0.0004914194,0.0014964852,0.0005245771,0.00022829958,0.00040597204,0.00031007023,0.00027177786],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015473526,0.0001308588,0.99705684,0.000028013952,0.0000734814,0.00071171665,0.0005364934,0.00001607007,0.0005069074,0.000025538733,0.00008058686,0.0006786946],"study_design_scores_gemma":[0.000037484268,0.0002072993,0.99757415,0.000005633845,0.0000691259,0.0011371295,0.0005470234,0.0000706601,0.00008279569,0.000008500834,0.00025608935,0.000004167179],"about_ca_topic_score_codex":0.021622844,"about_ca_topic_score_gemma":0.01727011,"teacher_disagreement_score":0.021622844,"about_ca_system_score_codex":0.0005467771,"about_ca_system_score_gemma":0.00052788755,"threshold_uncertainty_score":0.042993963},"labels":[],"label_agreement":null},{"id":"W2584521614","doi":"10.6000/1929-6029.2016.05.04.3","title":"Perceptions about the Health Effects of Passive Smoking among Bangladeshi Young Adults","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University","keywords":"Passive smoking; Harm; Logistic regression; Environmental health; Psychological intervention; Medicine; Perception; Multistage sampling; Descriptive statistics; Cluster sampling; Cross-sectional study; Young adult; Gerontology; Psychology; Social psychology; Nursing; Population","score_opus":0.03888469257546875,"score_gpt":0.44231274232866713,"score_spread":0.4034280497531984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584521614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99879503,0.0002316596,0.000032135693,0.000121961755,0.0000037999432,0.0000138165,0.00007719715,6.884547e-7,0.00072373234],"genre_scores_gemma":[0.9993363,0.00030375726,0.000031373824,0.000066825894,0.0000030165595,0.0000067805954,0.000036692763,2.6497526e-7,0.00021502162],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995802,0.00013528044,0.0000695847,0.000033357275,0.00011741135,0.00006422436],"domain_scores_gemma":[0.9987657,0.00033500892,0.00043830456,0.0000400039,0.00016936802,0.0002515836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010700511,0.00018305353,0.00026894477,0.00035451274,0.0004410147,0.0007649018,0.00012587012,0.0004431422,0.002099739],"category_scores_gemma":[0.0025823838,0.00022423414,0.00026595528,0.00031891675,0.00038292512,0.00047329,0.0003977906,0.0004909081,0.000255682],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088650995,0.00010235507,0.9715437,0.000112144575,0.00004026381,0.0002135394,0.017586267,0.00003635826,0.001121716,0.00007481633,0.00018399558,0.008896152],"study_design_scores_gemma":[0.000008879975,0.000555277,0.9721347,0.00008111147,0.000032458254,0.00026399788,0.025555462,0.000081891296,0.00014357766,0.00005703276,0.0010698024,0.000015757067],"about_ca_topic_score_codex":0.015205519,"about_ca_topic_score_gemma":0.017022211,"teacher_disagreement_score":0.015205519,"about_ca_system_score_codex":0.0003481582,"about_ca_system_score_gemma":0.0003608213,"threshold_uncertainty_score":0.030234039},"labels":[],"label_agreement":null},{"id":"W2592303047","doi":"10.6000/1929-6029.2017.06.01.3","title":"ROC Analysis for Phase II Group Sequential Basket Clinical Trial","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Parametric statistics; Clinical trial; Computer science; Group (periodic table); Receiver operating characteristic; Randomized controlled trial; Statistics; Econometrics; Artificial intelligence; Machine learning; Mathematics; Medicine; Surgery; Internal medicine","score_opus":0.8643649198917347,"score_gpt":0.7766170149383429,"score_spread":0.0877479049533918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592303047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030219868,0.020914381,0.9187907,0.003365315,0.0015309597,0.006405368,0.006651277,0.0035223109,0.008599762],"genre_scores_gemma":[0.47350332,0.00472488,0.4876534,0.0027198717,0.0015085473,0.01763891,0.007561549,0.0012028313,0.0034866105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.81250066,0.1620334,0.0068647973,0.0069752457,0.01067464,0.00095124246],"domain_scores_gemma":[0.64084476,0.2951463,0.027086133,0.022016441,0.013020754,0.0018856005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10960652,0.0019397046,0.0042805066,0.0059012314,0.00068817637,0.0034863253,0.001988321,0.002581556,0.010520992],"category_scores_gemma":[0.3119615,0.00065615674,0.0043020444,0.0044292845,0.0018316181,0.0028987427,0.0018105659,0.004534788,0.0022594237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019440155,0.0009032557,0.067375235,0.018816654,0.012306465,0.001469435,0.00089695764,0.09095737,0.0033887716,0.13673076,0.10201848,0.5456965],"study_design_scores_gemma":[0.002865028,0.00908025,0.04884895,0.0030437834,0.0044576065,0.0043377527,0.00044420006,0.6383182,0.0054694237,0.17542633,0.10713622,0.0005723557],"about_ca_topic_score_codex":0.0008061911,"about_ca_topic_score_gemma":0.0004413089,"teacher_disagreement_score":0.10960652,"about_ca_system_score_codex":0.0013810003,"about_ca_system_score_gemma":0.0036351506,"threshold_uncertainty_score":0.5796616},"labels":[],"label_agreement":null},{"id":"W2594153243","doi":"10.6000/1929-6029.2017.06.01.1","title":"Evaluation of Methods for Gene Selection in Melanoma Cell Lines","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"African Union","keywords":"Selection (genetic algorithm); DNA microarray; Microarray analysis techniques; Computational biology; Melanoma; Biology; Computer science; Gene; Genetics; Gene expression; Machine learning","score_opus":0.1320632548069261,"score_gpt":0.5555952952535591,"score_spread":0.423532040446633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594153243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14729944,0.0057550934,0.8363378,0.0007877496,0.00028467103,0.00048089502,0.003166458,0.0038678334,0.0020200766],"genre_scores_gemma":[0.49811244,0.0019587076,0.48619086,0.00046317925,0.00018173752,0.0014163853,0.009067378,0.0007644653,0.001844799],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9932768,0.003927099,0.00033891635,0.00069087057,0.0015968158,0.00016944419],"domain_scores_gemma":[0.9774074,0.017658323,0.00094044703,0.0013521182,0.0024338875,0.00020785179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014916597,0.00092473044,0.0011453693,0.0019266008,0.00043832647,0.0013154743,0.0011731146,0.000747677,0.0014632738],"category_scores_gemma":[0.028702524,0.00027357924,0.0015980379,0.0016726579,0.0005339241,0.00080269226,0.0009247401,0.0011200124,0.00068475615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002629758,0.00043735854,0.055116642,0.0016457493,0.0013457852,0.0003492937,0.00027574995,0.32262513,0.037785437,0.011696394,0.010593653,0.555499],"study_design_scores_gemma":[0.00019572333,0.00070321443,0.014630607,0.000097897086,0.0001487703,0.0002311912,0.00014022469,0.9485021,0.018664852,0.010083948,0.0065408885,0.000060539693],"about_ca_topic_score_codex":0.001547284,"about_ca_topic_score_gemma":0.0014352436,"teacher_disagreement_score":0.014916597,"about_ca_system_score_codex":0.00086768373,"about_ca_system_score_gemma":0.0009944163,"threshold_uncertainty_score":0.07888746},"labels":[],"label_agreement":null},{"id":"W2594564865","doi":"10.6000/1929-6029.2017.06.01.4","title":"Bayesian Modelling of Tuberculosis Risk Factors in South Africa 2014","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prior probability; Logistic regression; Medicine; Tuberculosis; Bayesian probability; Environmental health; Frequentist inference; Demography; Bayesian inference; Geography; Statistics; Mathematics; Pathology","score_opus":0.12733254131215174,"score_gpt":0.4587667770331584,"score_spread":0.3314342357210066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594564865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46937656,0.002324302,0.5113407,0.0035400484,0.00008525558,0.00032635615,0.002243086,0.00027548595,0.010488124],"genre_scores_gemma":[0.93845177,0.0014163671,0.050750773,0.00015366632,0.00006469233,0.0003363849,0.0011308639,0.000050275063,0.0076453704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990551,0.0006164883,0.000035428337,0.00013069235,0.00007120075,0.00009110198],"domain_scores_gemma":[0.99382246,0.0052122497,0.00044923744,0.0001252905,0.0002917281,0.000099107936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035792736,0.0006392603,0.0009746603,0.0010591923,0.0006796945,0.0016749575,0.0014445797,0.0014513286,0.00511956],"category_scores_gemma":[0.018420188,0.00077921525,0.0012174433,0.0009453123,0.00076405314,0.0011316463,0.0013064059,0.0013133767,0.00043943015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012846282,0.000072282055,0.023765763,0.0001232755,0.00023839423,0.00046422842,0.0010968687,0.8916184,0.00035272524,0.05431194,0.0014745574,0.026353195],"study_design_scores_gemma":[0.000031228803,0.000027204034,0.0052570067,0.000058446232,0.00003609253,0.00009832857,0.00015875822,0.96147865,0.000056272573,0.031187307,0.0015910608,0.000019614934],"about_ca_topic_score_codex":0.06927697,"about_ca_topic_score_gemma":0.045080673,"teacher_disagreement_score":0.06927697,"about_ca_system_score_codex":0.0016134453,"about_ca_system_score_gemma":0.0015342393,"threshold_uncertainty_score":0.13774753},"labels":[],"label_agreement":null},{"id":"W2594578343","doi":"10.6000/1929-6029.2017.06.01.2","title":"Model Based Sparse Feature Extraction for Biomedical Signal Classification","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Principal component analysis; Sparse approximation; Artificial intelligence; Computer science; SIGNAL (programming language); Feature extraction; Feature (linguistics); Signal processing; Signal reconstruction","score_opus":0.19001712708026455,"score_gpt":0.5168197255182762,"score_spread":0.3268025984380116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594578343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021501803,0.00030112578,0.99687237,0.00010534231,0.000022022314,0.000016548025,0.000059564965,0.00022296543,0.00024995627],"genre_scores_gemma":[0.27048007,0.0027289765,0.72181404,0.00022433557,0.00030527197,0.00032086353,0.001183018,0.00015474629,0.0027886361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995414,0.00014240161,0.000028765378,0.00008717613,0.00016636579,0.00003403483],"domain_scores_gemma":[0.9993338,0.0003401743,0.000085474596,0.00009061885,0.00013398436,0.000015916386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076717394,0.0007660394,0.0010782131,0.0010155181,0.0002909653,0.00071239,0.0006513142,0.0008193596,0.0017319162],"category_scores_gemma":[0.002913947,0.00031031983,0.0011128341,0.0015758278,0.00042763387,0.0009473713,0.0006809533,0.0012617914,0.0009766974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001401506,0.000115961666,0.00094531354,0.00037495123,0.00016902924,0.00015817193,0.00010704561,0.30567256,0.040073477,0.026268482,0.0068096537,0.6191652],"study_design_scores_gemma":[0.0000047978237,0.000039904975,0.00032914733,0.000013335528,0.000015744501,0.00006338496,0.000008530115,0.9879066,0.0026464625,0.0070428113,0.0019172555,0.000011978265],"about_ca_topic_score_codex":0.0014660144,"about_ca_topic_score_gemma":0.0012150725,"teacher_disagreement_score":0.0017319162,"about_ca_system_score_codex":0.00035518812,"about_ca_system_score_gemma":0.0005651472,"threshold_uncertainty_score":0.00579381},"labels":[],"label_agreement":null},{"id":"W2598433776","doi":"10.6000/1929-6029.2018.07.01.3","title":"Probability Sampling in Matched Case-Control Study in Drug Abuse","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probability sampling; Sampling (signal processing); Drug; Substance abuse; Statistics; Psychology; Medicine; Psychiatry; Mathematics; Computer science; Environmental health","score_opus":0.2978219006340884,"score_gpt":0.5581067351410094,"score_spread":0.26028483450692097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598433776","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53979295,0.006973215,0.4427046,0.0006262325,0.00081289595,0.006046714,0.00042448178,0.00026397046,0.0023548927],"genre_scores_gemma":[0.9362809,0.0007488071,0.05881343,0.00032687635,0.00022760987,0.0028959932,0.0003298299,0.000023485522,0.000353043],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7720103,0.20614389,0.004907111,0.009403404,0.0066661835,0.00086913916],"domain_scores_gemma":[0.85451144,0.115479074,0.009911304,0.01654838,0.0026473745,0.00090236776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12388606,0.0006979187,0.0019506782,0.0027410004,0.0010642234,0.0013569529,0.002016288,0.0020210876,0.0014156159],"category_scores_gemma":[0.24749069,0.0012763044,0.0016600357,0.002610155,0.0018480187,0.0015078699,0.0017286771,0.0011519348,0.00024001571],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017107254,0.0014730267,0.82517976,0.0015759142,0.0070892144,0.0023164395,0.0023115063,0.011332984,0.00166864,0.030521516,0.002542411,0.09688137],"study_design_scores_gemma":[0.006027899,0.013157532,0.60914564,0.000920332,0.006418715,0.00571515,0.0012053367,0.27905977,0.003512964,0.060427085,0.014148818,0.0002607361],"about_ca_topic_score_codex":0.0036910286,"about_ca_topic_score_gemma":0.002366803,"teacher_disagreement_score":0.12388606,"about_ca_system_score_codex":0.0009973187,"about_ca_system_score_gemma":0.0009527785,"threshold_uncertainty_score":0.65518},"labels":[],"label_agreement":null},{"id":"W2605571327","doi":"10.6000/1929-6029.2017.06.02.4","title":"Predictors of High Blood Pressure in South African Children: Quantile Regression Approach","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quantile regression; Blood pressure; Percentile; Ordinary least squares; Body mass index; Quantile; Medicine; Regression analysis; Demography; Statistics; Internal medicine; Mathematics","score_opus":0.06860554600882811,"score_gpt":0.40510019693130783,"score_spread":0.3364946509224797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605571327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9755593,0.0014757394,0.017973,0.0008358628,0.000028141687,0.000057533292,0.002697321,0.00008180865,0.0012913076],"genre_scores_gemma":[0.9954074,0.0003426145,0.0031858243,0.000025444453,0.000013678504,0.000036490488,0.00063578633,0.000010856186,0.00034186384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992224,0.00042458702,0.000033318287,0.00012961935,0.00008800345,0.00010201669],"domain_scores_gemma":[0.99822396,0.0010155063,0.0004000953,0.00011927366,0.00016720548,0.00007394013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023157739,0.0003247707,0.00042643523,0.0009845692,0.000260778,0.0006202207,0.0005046485,0.0003239188,0.0037545946],"category_scores_gemma":[0.006879733,0.00024357958,0.0009460764,0.0014809212,0.0002100092,0.00047952938,0.00058110274,0.00083550095,0.00036068098],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110681045,0.00004856574,0.9736684,0.00009377482,0.00030319768,0.00013681603,0.00048800104,0.00399373,0.00033159775,0.0008255357,0.0007390917,0.019260531],"study_design_scores_gemma":[0.000009450311,0.00009120631,0.9659104,0.000117601885,0.00015037495,0.00013721742,0.0010779531,0.029730992,0.00028535468,0.0009819996,0.0014926236,0.000014811766],"about_ca_topic_score_codex":0.017841568,"about_ca_topic_score_gemma":0.009394799,"teacher_disagreement_score":0.017841568,"about_ca_system_score_codex":0.00035046967,"about_ca_system_score_gemma":0.0004924482,"threshold_uncertainty_score":0.035475433},"labels":[],"label_agreement":null},{"id":"W2605795799","doi":"10.6000/1929-6029.2017.06.02.2","title":"A Smooth Test of Goodness-of-Fit for the Weibull Distribution: An Application to an HIV Retention Data","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Goodness of fit; Weibull distribution; Statistics; Mathematics; Empirical distribution function; Test (biology); Anderson–Darling test; Statistical hypothesis testing; Kolmogorov–Smirnov test","score_opus":0.2288280256826406,"score_gpt":0.5529034074089898,"score_spread":0.3240753817263492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605795799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43726563,0.00084046816,0.5546824,0.0009290239,0.00016003051,0.0007271257,0.0005849738,0.0007018718,0.0041084737],"genre_scores_gemma":[0.93010896,0.00016907255,0.067922,0.0001569016,0.000057311085,0.0003801028,0.00042358387,0.000092512644,0.0006894933],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98099273,0.012508239,0.0007840441,0.0015148458,0.0034459438,0.0007542063],"domain_scores_gemma":[0.7566891,0.21280304,0.009163231,0.009661608,0.010269792,0.0014133446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040221207,0.0010808402,0.0016716144,0.004112182,0.0014132169,0.002298488,0.002096187,0.0026592049,0.0036931986],"category_scores_gemma":[0.2191097,0.00046595067,0.0028721266,0.00427046,0.0035171928,0.00327712,0.0022941192,0.0033035795,0.0006663952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031399138,0.00070108037,0.33430332,0.0008146285,0.002099259,0.0019519618,0.0035733737,0.25784418,0.004441865,0.10958527,0.0053395587,0.27620572],"study_design_scores_gemma":[0.00032145134,0.0034943346,0.12752643,0.00031494527,0.00032599867,0.0014290098,0.0028194007,0.76157814,0.0028704333,0.09213715,0.0067632617,0.00041941655],"about_ca_topic_score_codex":0.0036388831,"about_ca_topic_score_gemma":0.0018672112,"teacher_disagreement_score":0.040221207,"about_ca_system_score_codex":0.0011376581,"about_ca_system_score_gemma":0.0024004357,"threshold_uncertainty_score":0.21271259},"labels":[],"label_agreement":null},{"id":"W2606481702","doi":"10.6000/1929-6029.2017.06.02.3","title":"Improving the Efficiency of Outpatient Services at Benue State University Teaching Hospital using the Queuing Theory","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Attendance; Queueing theory; Queue; Outpatient clinic; Medicine; Operations management; Health care; Teaching hospital; Medical emergency; Nursing; Family medicine; Computer science; Engineering; Computer network","score_opus":0.083598938270282,"score_gpt":0.5072408880533105,"score_spread":0.4236419497830285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606481702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8215786,0.0008117935,0.16208917,0.0017471941,0.00008453348,0.00038741945,0.0004656495,0.00034333693,0.012492286],"genre_scores_gemma":[0.9859055,0.00029571026,0.012235799,0.000031382468,0.000011200771,0.00009130664,0.00009464023,0.000010432564,0.0013239286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944276,0.00020936682,0.000027103006,0.00006560521,0.00008920053,0.00016596938],"domain_scores_gemma":[0.9993424,0.0003795201,0.00008749831,0.000015218564,0.00010339182,0.00007206096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075695134,0.0004902857,0.00035778782,0.00061343564,0.0006079861,0.0011232124,0.00078853,0.00053835625,0.0024938625],"category_scores_gemma":[0.0013159321,0.00026604504,0.00051430246,0.0005929628,0.00030714893,0.00071474526,0.00051883457,0.0003457555,0.00018445274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001819534,0.00020992417,0.019993016,0.00016184287,0.0000375147,0.00024610685,0.00017670459,0.9497809,0.0018790975,0.004362234,0.0013171418,0.021653613],"study_design_scores_gemma":[0.000018872577,0.00017249165,0.004285438,0.000032598567,0.000022886117,0.00004887566,0.00019673968,0.99235934,0.000609708,0.0015149199,0.00071819243,0.000019918516],"about_ca_topic_score_codex":0.030469531,"about_ca_topic_score_gemma":0.025782056,"teacher_disagreement_score":0.030469531,"about_ca_system_score_codex":0.0029418364,"about_ca_system_score_gemma":0.0035707413,"threshold_uncertainty_score":0.060584366},"labels":[],"label_agreement":null},{"id":"W2606788746","doi":"10.6000/1929-6029.2017.06.02.1","title":"Validation of the Smooth Test of Goodness-of-Fit for Proportional Hazards in Cancer Survival Studies","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proportional hazards model; Covariate; Goodness of fit; Mathematics; Statistics; Hazard; Test (biology); Cancer survival; Hazard ratio; Cancer; Medicine; Confidence interval","score_opus":0.5544758580659591,"score_gpt":0.6374241873825314,"score_spread":0.08294832931657226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606788746","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19506474,0.002026391,0.79643303,0.0012549063,0.00038767853,0.0007083489,0.00089920906,0.00054336793,0.002682298],"genre_scores_gemma":[0.8760615,0.00036885074,0.11968474,0.0005024662,0.00017436805,0.0008176247,0.0014230617,0.00024130123,0.0007260366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92350715,0.059714165,0.0026322482,0.00561327,0.007511754,0.0010214665],"domain_scores_gemma":[0.36559457,0.5904285,0.009352301,0.020726934,0.012126536,0.0017712032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1342023,0.0011379231,0.00213411,0.002855468,0.001280732,0.0025916412,0.002873349,0.0032142224,0.0038271768],"category_scores_gemma":[0.49154645,0.0005282374,0.0026392154,0.002648437,0.0063343938,0.0035010888,0.0040339143,0.0039399914,0.00062045245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008875495,0.00075174624,0.2551123,0.0022556186,0.0049342895,0.0024408156,0.0025030433,0.3053101,0.005248456,0.15105388,0.009869031,0.2516453],"study_design_scores_gemma":[0.0012177879,0.0036577058,0.05174518,0.0004110377,0.0007083723,0.0016614974,0.001102545,0.7351139,0.0060526794,0.18912394,0.00894456,0.0002607179],"about_ca_topic_score_codex":0.0014952202,"about_ca_topic_score_gemma":0.00081721606,"teacher_disagreement_score":0.1342023,"about_ca_system_score_codex":0.0009529037,"about_ca_system_score_gemma":0.0037656946,"threshold_uncertainty_score":0.7097381},"labels":[],"label_agreement":null},{"id":"W2742218033","doi":"10.6000/1929-6029.2017.06.03.2","title":"A Smooth Test of Goodness-of-Fit for the Baseline Hazard Function for Time-to-First Occurrence in Recurrent Events: An Application to HIV Retention Data","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS drug development and treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Goodness of fit; Statistics; Proportional hazards model; Mathematics; Hazard; Event (particle physics); Hazard ratio; Confidence interval","score_opus":0.15420581731420308,"score_gpt":0.48190537986138143,"score_spread":0.32769956254717836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742218033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41590744,0.00070995075,0.57918125,0.0006500554,0.00009769857,0.00031731138,0.0008952043,0.00078663346,0.0014543544],"genre_scores_gemma":[0.9349429,0.00014206825,0.06294491,0.00010435681,0.00006174168,0.0002158594,0.00076211186,0.00010658315,0.00071945996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9866066,0.009353192,0.00047719205,0.0014098742,0.0015865685,0.00056658196],"domain_scores_gemma":[0.7657144,0.20993714,0.00820009,0.010259775,0.0042746603,0.0016140168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035247028,0.00069696555,0.001846968,0.0030370813,0.0007968799,0.0012359275,0.0021202648,0.0019964525,0.0035819937],"category_scores_gemma":[0.16069977,0.00044320768,0.0028459826,0.0028082363,0.0030805347,0.0019837571,0.0021216543,0.0027466451,0.00043860715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00448916,0.00066059007,0.249878,0.0006823607,0.0033168648,0.0020834338,0.0015124743,0.39280713,0.006322676,0.07619437,0.005233875,0.25681916],"study_design_scores_gemma":[0.0002991081,0.0033293047,0.08560871,0.00009061301,0.0002968628,0.00073627505,0.0005470214,0.8484815,0.0022181207,0.055404134,0.0027628925,0.0002254391],"about_ca_topic_score_codex":0.0032246967,"about_ca_topic_score_gemma":0.0019276649,"teacher_disagreement_score":0.035247028,"about_ca_system_score_codex":0.00076645415,"about_ca_system_score_gemma":0.0020213742,"threshold_uncertainty_score":0.18640631},"labels":[],"label_agreement":null},{"id":"W2744405842","doi":"10.6000/1929-6029.2017.06.03.4","title":"Key Design Considerations Using a Cohort Stepped-Wedge Cluster Randomised Trial in Evaluating Community-Based Interventions: Lessons Learnt from an Australian Domiciliary Aged Care Intervention Evaluation","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"CRTS; Cohort; Context (archaeology); Cluster randomised controlled trial; Psychological intervention; Intervention (counseling); Cluster (spacecraft); Relevance (law); Computer science; Psychology; Medicine; Management science; Nursing; Engineering; Geography; Political science","score_opus":0.5831007241969052,"score_gpt":0.6621708148784471,"score_spread":0.07907009068154192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744405842","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013853663,0.034604426,0.56808037,0.18651539,0.012890087,0.1695134,0.0015380874,0.0006451854,0.012359469],"genre_scores_gemma":[0.031902764,0.0048467983,0.805378,0.022892965,0.0013469796,0.1322373,0.00012260745,0.00013441968,0.001138109],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.24732134,0.68247414,0.03493095,0.0067789312,0.0268076,0.0016869952],"domain_scores_gemma":[0.38980407,0.5535858,0.011926394,0.018360691,0.022590574,0.0037324762],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.59391874,0.002808809,0.00754532,0.003109613,0.003551471,0.010756619,0.009901961,0.013951794,0.0061195637],"category_scores_gemma":[0.5870723,0.0027243916,0.0070451214,0.0042497152,0.011091186,0.011126665,0.006877111,0.014534008,0.0016403003],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013075016,0.001047225,0.007724946,0.11243567,0.0046699755,0.002508546,0.026548909,0.019709952,0.002361945,0.33924302,0.051912792,0.41876206],"study_design_scores_gemma":[0.024959877,0.01600486,0.010672034,0.1287429,0.004784623,0.003511472,0.007967884,0.05937915,0.0057901256,0.47010073,0.26685652,0.0012297629],"about_ca_topic_score_codex":0.007912792,"about_ca_topic_score_gemma":0.018716302,"teacher_disagreement_score":0.40608126,"about_ca_system_score_codex":0.010064686,"about_ca_system_score_gemma":0.03047476,"threshold_uncertainty_score":0.50077057},"labels":[],"label_agreement":null},{"id":"W2744843261","doi":"10.6000/1929-6029.2017.06.03.1","title":"Quantile Regression for Area Disease Counts: Bayesian Estimation using Generalized Poisson Regression","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quantile regression; Quantile; Poisson regression; Statistics; Outlier; Econometrics; Poisson distribution; Mathematics; Regression analysis; Regression; Linear regression; Count data; Generalized linear model; Medicine; Population","score_opus":0.1982964698244141,"score_gpt":0.5828997674287487,"score_spread":0.38460329760433465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744843261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061997497,0.00050269073,0.9912778,0.00028459728,0.000035255878,0.00016597199,0.00046174807,0.0004919447,0.00058020424],"genre_scores_gemma":[0.29695204,0.0025728783,0.6904324,0.0004455492,0.00029579128,0.0015894563,0.0031758596,0.00050356326,0.0040324135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879089,0.009152029,0.00040997012,0.0012836024,0.00091123185,0.0003341931],"domain_scores_gemma":[0.968442,0.025675844,0.0020000103,0.0019193472,0.0017208266,0.00024196954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023442399,0.001049643,0.0028437155,0.0030341113,0.0007478398,0.0021538618,0.0037402413,0.0018537369,0.0061795455],"category_scores_gemma":[0.082419716,0.0015388575,0.0029096443,0.0044187433,0.0010812429,0.0024147318,0.0024886548,0.0037157657,0.0018000355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031679723,0.0001767648,0.029789438,0.00060741877,0.0010157243,0.00031382332,0.000589686,0.6259389,0.0008143692,0.1246908,0.010824405,0.20492193],"study_design_scores_gemma":[0.000048035916,0.000040527404,0.0035377927,0.00010631004,0.00007848505,0.00008634121,0.00007712013,0.9039867,0.00017613622,0.088599674,0.0032200895,0.000042848314],"about_ca_topic_score_codex":0.026248245,"about_ca_topic_score_gemma":0.016134014,"teacher_disagreement_score":0.026248245,"about_ca_system_score_codex":0.0016022483,"about_ca_system_score_gemma":0.00231595,"threshold_uncertainty_score":0.12397677},"labels":[],"label_agreement":null},{"id":"W2774950576","doi":"10.6000/1929-6029.2017.06.04.3","title":"Sample Size Calculation in Clinical Studies: Some Common Scenarios","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"","keywords":"Sample size determination; Sample (material); Type I and type II errors; Statistical power; Computer science; Statistics; Power (physics); Power analysis; Econometrics; Mathematics; Algorithm","score_opus":0.8047432444648496,"score_gpt":0.7485851251915935,"score_spread":0.05615811927325609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774950576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002828816,0.012058471,0.95612603,0.016923323,0.00097463187,0.0018705714,0.0002874469,0.0003658587,0.008564832],"genre_scores_gemma":[0.039779533,0.006128416,0.93664,0.0061325114,0.0012394808,0.008383191,0.00014838285,0.00025686447,0.0012915112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6497801,0.28557977,0.025872728,0.007720543,0.030044548,0.0010022435],"domain_scores_gemma":[0.4457116,0.5149006,0.013294412,0.012992415,0.012083073,0.0010178045],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2249222,0.0022769556,0.0026351796,0.0063468944,0.002552977,0.0067609656,0.0052026585,0.012389978,0.005092942],"category_scores_gemma":[0.52031493,0.0017378316,0.002304999,0.0068873153,0.013196523,0.009507768,0.007637185,0.009918258,0.0022010158],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057561777,0.000106515334,0.0024821942,0.005297562,0.00026804028,0.0008243201,0.0034796572,0.0053939163,0.0011297897,0.75238174,0.018517103,0.20954342],"study_design_scores_gemma":[0.00036833825,0.0005391306,0.0010503108,0.0051600356,0.00014484918,0.002297494,0.00058304536,0.016487624,0.002251818,0.88429445,0.086646535,0.00017635165],"about_ca_topic_score_codex":0.0011144154,"about_ca_topic_score_gemma":0.0009199187,"teacher_disagreement_score":0.7750778,"about_ca_system_score_codex":0.0041417372,"about_ca_system_score_gemma":0.0043854816,"threshold_uncertainty_score":0.95580894},"labels":[],"label_agreement":null},{"id":"W2775350017","doi":"10.6000/1929-6029.2017.06.04.2","title":"The MAX Statistic is Less Powerful for Genome Wide Association Studies Under Most Alternative Hypotheses","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Multiplicative function; Type I and type II errors; Statistic; Statistics; Sample size determination; Test statistic; Statistical hypothesis testing; Multiple comparisons problem; Genetic model; Range (aeronautics); Mathematics; Monte Carlo method; Econometrics; Genetics; Biology; Engineering","score_opus":0.12700749989067775,"score_gpt":0.4893246325564562,"score_spread":0.3623171326657785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775350017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042125195,0.0030643968,0.9420096,0.002086471,0.0004478092,0.00051747117,0.0013585669,0.0024288872,0.005961612],"genre_scores_gemma":[0.5102505,0.0014794432,0.4752521,0.004242396,0.0007157798,0.0019864966,0.0020097662,0.0013610468,0.0027024648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.83717275,0.1331039,0.006088162,0.012410987,0.009716264,0.0015080025],"domain_scores_gemma":[0.4138872,0.53523874,0.0141306175,0.031660832,0.003920912,0.0011618148],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14305516,0.002369395,0.0062261675,0.0056088767,0.0015928919,0.0046903207,0.0039084586,0.003336628,0.012134772],"category_scores_gemma":[0.3981178,0.0014717816,0.006032084,0.007058516,0.005514758,0.0074498886,0.0036590104,0.005508156,0.0026234277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0073874197,0.00039111718,0.13514116,0.007644634,0.015540588,0.0022005662,0.0016303965,0.04565856,0.010040353,0.10975452,0.031426076,0.6331847],"study_design_scores_gemma":[0.0017383012,0.0057031284,0.0904771,0.0021558725,0.0056605814,0.014117383,0.0015172596,0.19946004,0.018276663,0.58916736,0.07050041,0.0012259888],"about_ca_topic_score_codex":0.0011172205,"about_ca_topic_score_gemma":0.0013351223,"teacher_disagreement_score":0.85694486,"about_ca_system_score_codex":0.0009800097,"about_ca_system_score_gemma":0.0027127208,"threshold_uncertainty_score":0.756557},"labels":[],"label_agreement":null},{"id":"W2781827144","doi":"10.6000/1929-6029.2017.06.04.1","title":"A Comparison of Parametric and Semi-Parametric Models for Microarray Data Analysis","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"African Union","keywords":"Parametric statistics; Nonparametric statistics; Copula (linguistics); Parametric model; Computer science; Type I and type II errors; Semiparametric model; Data mining; Mathematics; Statistics; Econometrics","score_opus":0.22547116755135047,"score_gpt":0.5501395775940571,"score_spread":0.32466841004270663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781827144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012460444,0.00115449,0.984591,0.00037017086,0.0000663657,0.00013303116,0.0002591833,0.0003946172,0.0005708211],"genre_scores_gemma":[0.36421305,0.0025517533,0.6268248,0.0005515781,0.00022895605,0.001432961,0.0020508952,0.0005095577,0.0016364538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9774825,0.016494725,0.00089993054,0.0019815115,0.0026275408,0.00051379076],"domain_scores_gemma":[0.8999816,0.08597884,0.0030089852,0.0064337975,0.004165063,0.00043169557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033071034,0.0012212878,0.0019626925,0.0019309572,0.0006636119,0.0023619076,0.0029153896,0.001786704,0.0019786928],"category_scores_gemma":[0.086107284,0.00069446,0.0028780361,0.0021163146,0.0014165,0.0028536292,0.0020032255,0.0030428243,0.0007189065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011132588,0.00029330864,0.0134329405,0.0009868327,0.001275114,0.0003722724,0.0007331664,0.60782945,0.0048312307,0.07713403,0.006750121,0.28524834],"study_design_scores_gemma":[0.00004094775,0.00019482037,0.0028574637,0.00006904173,0.0000924426,0.0001840249,0.00009178314,0.9543684,0.0009780203,0.038364887,0.0026869678,0.00007109415],"about_ca_topic_score_codex":0.0026568803,"about_ca_topic_score_gemma":0.002500696,"teacher_disagreement_score":0.033071034,"about_ca_system_score_codex":0.0011887635,"about_ca_system_score_gemma":0.0020606264,"threshold_uncertainty_score":0.17489839},"labels":[],"label_agreement":null},{"id":"W2784276292","doi":"10.6000/1929-6029.2017.06.04.4","title":"Lindley Approximation Technique for the Parameters of Lomax Distribution","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lomax distribution; Prior probability; Applied mathematics; Mathematics; Exponential function; Bayesian probability; Bayes' theorem; Exponential distribution; Bayes estimator; Statistics; Mathematical optimization; Maximum likelihood; Mathematical analysis","score_opus":0.22750787146191873,"score_gpt":0.551320226571386,"score_spread":0.32381235510946726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784276292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019411641,0.00034091307,0.995956,0.00009856677,0.000024924388,0.000016520331,0.00005301622,0.00016348518,0.0014053447],"genre_scores_gemma":[0.22050849,0.0035458275,0.7588668,0.0006471448,0.00032203563,0.00063624803,0.0010917018,0.00082556286,0.013556208],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980464,0.00082259433,0.0000889456,0.00032107948,0.00054957974,0.00017138648],"domain_scores_gemma":[0.995223,0.0032320265,0.00035865506,0.00046389605,0.00062258757,0.00009980152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040854905,0.0011830707,0.0009827975,0.0021413085,0.0008271655,0.0024315466,0.0025379849,0.001776105,0.008643419],"category_scores_gemma":[0.020026386,0.00056589965,0.0014647718,0.0019282869,0.0011675151,0.0034930278,0.0017722162,0.0039521586,0.0044282605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030518786,0.00011410554,0.005768785,0.00060583954,0.00024299385,0.00066361617,0.000995523,0.24049197,0.009840977,0.50349265,0.01075908,0.22671925],"study_design_scores_gemma":[0.000024305766,0.00005699006,0.0014917655,0.00018116827,0.000058275036,0.00063547643,0.00014684752,0.8006915,0.0038087682,0.17773415,0.015087267,0.00008340892],"about_ca_topic_score_codex":0.0048723025,"about_ca_topic_score_gemma":0.003758662,"teacher_disagreement_score":0.008643419,"about_ca_system_score_codex":0.0011797694,"about_ca_system_score_gemma":0.001127342,"threshold_uncertainty_score":0.028915107},"labels":[],"label_agreement":null},{"id":"W2793351244","doi":"10.6000/1929-6029.2018.07.01.2","title":"The Trend of the Bibliographical Output from Libyan Engineering Schools: A 30-Year Review From 1984-2013","year":2018,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Productivity; Standardization; Population; Gross domestic product; China; Political science; Library science; Geography; Economic growth; Demography; Sociology; Economics; Computer science; Law","score_opus":0.17775483981713933,"score_gpt":0.5795258249668963,"score_spread":0.40177098514975695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793351244","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029461272,0.958492,0.00021992403,0.0014052243,0.00041190992,0.00004307754,0.0062582335,0.00004422232,0.0036642072],"genre_scores_gemma":[0.07825843,0.9132418,0.0010805415,0.00059062615,0.0005913239,0.000110039735,0.0048883986,0.000029262035,0.0012095653],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9934331,0.0011886474,0.0026625404,0.0006062269,0.0018184388,0.0002909177],"domain_scores_gemma":[0.949677,0.024219103,0.011228277,0.00084722874,0.013060909,0.000967346],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007970584,0.00082930113,0.0016368857,0.07756196,0.00093496876,0.0053179255,0.0010567432,0.00080183905,0.0036261792],"category_scores_gemma":[0.026040263,0.00050056604,0.0015558744,0.117207825,0.00091308646,0.0035864674,0.0014624692,0.000617987,0.00090442697],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005820058,0.00006869746,0.12979977,0.2933453,0.0040580314,0.0017300136,0.0060338625,0.0006169954,0.002032844,0.0016702018,0.024414757,0.53564745],"study_design_scores_gemma":[0.0000833567,0.00035653517,0.42791286,0.17318296,0.008815152,0.0053402428,0.010869932,0.00033438054,0.0028392342,0.0007325093,0.3693935,0.00013936302],"about_ca_topic_score_codex":0.0067109377,"about_ca_topic_score_gemma":0.014991797,"teacher_disagreement_score":0.922438,"about_ca_system_score_codex":0.0032633347,"about_ca_system_score_gemma":0.009174394,"threshold_uncertainty_score":0.042153},"labels":[],"label_agreement":null},{"id":"W2793942827","doi":"10.6000/1929-6029.2018.07.01.1","title":"Effects of some Biological Covariates on the Probability of First Recurrence of Malaria following Treatment with Artemisinin Combination Therapy","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Malaria Research and Control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Karolinska Institutet","keywords":"Sulfadoxine/pyrimethamine; Artesunate; Malaria; Artemisinin; Sulfadoxine; Logistic regression; Covariate; Medicine; Combination therapy; Pyrimethamine; Internal medicine; Plasmodium falciparum; Immunology; Statistics; Mathematics","score_opus":0.06827682727719883,"score_gpt":0.4138752579500839,"score_spread":0.3455984306728851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793942827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99551094,0.001347305,0.001264394,0.000398535,0.000053198208,0.00003311997,0.00066456967,0.00003961341,0.00068839104],"genre_scores_gemma":[0.9988391,0.00014370281,0.0002711208,0.000026569169,0.000030602074,0.000023482655,0.00031575165,0.000008658004,0.00034105615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99101806,0.005647956,0.000496763,0.0013003225,0.00063784904,0.00089909154],"domain_scores_gemma":[0.85876,0.106069624,0.023550069,0.0062759104,0.0012849736,0.004059401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012526776,0.0004949133,0.0013769311,0.0006292102,0.00041353246,0.0012834698,0.0009370583,0.0018424189,0.006141959],"category_scores_gemma":[0.061116405,0.0002788026,0.002585703,0.0012111294,0.0008374422,0.00083682523,0.00063923164,0.0028000695,0.00044852783],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020747302,0.0005549344,0.96180576,0.00013041505,0.0023796563,0.00050485006,0.00017581461,0.0039443807,0.0008417303,0.000276582,0.00035542157,0.008283205],"study_design_scores_gemma":[0.0001650197,0.0049232645,0.9787395,0.000027036476,0.0013131541,0.00035124487,0.00011302777,0.013164657,0.0004098331,0.00040032977,0.00035395415,0.000038817667],"about_ca_topic_score_codex":0.0019424288,"about_ca_topic_score_gemma":0.0010571928,"teacher_disagreement_score":0.012526776,"about_ca_system_score_codex":0.00039602912,"about_ca_system_score_gemma":0.00058338797,"threshold_uncertainty_score":0.066248715},"labels":[],"label_agreement":null},{"id":"W2799769883","doi":"10.6000/1929-6029.2018.07.02.3","title":"A New Method of Odds Ratio and Hazard Analysis of Head and Neck Cancer","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Odds ratio; Hazard ratio; Statistics; Odds; Mathematics; Logit; Confidence interval; Logistic regression","score_opus":0.11195359430473951,"score_gpt":0.5637187103967052,"score_spread":0.4517651160919657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799769883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019294375,0.0012602389,0.9924737,0.00044742544,0.00042714365,0.00019081558,0.00057175115,0.00080232887,0.0018970704],"genre_scores_gemma":[0.096944414,0.002515499,0.88421744,0.0009724401,0.001732851,0.0024964374,0.0016571068,0.0012015088,0.008262289],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9748127,0.01483773,0.0014792695,0.0036474366,0.0048075924,0.00041526108],"domain_scores_gemma":[0.97313696,0.020241005,0.001809608,0.002947031,0.0016195376,0.00024582105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015239066,0.0013559007,0.0015580211,0.0047464995,0.00063758914,0.0027891581,0.0021956426,0.0012535087,0.010244554],"category_scores_gemma":[0.07127608,0.0006696472,0.0032080866,0.0034393917,0.0012012733,0.0027873034,0.0026469207,0.0041152746,0.002367742],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063946575,0.00017108396,0.028444685,0.0014371099,0.0017861907,0.0005941198,0.00088943733,0.0058582625,0.0035796093,0.19153848,0.0216999,0.74336165],"study_design_scores_gemma":[0.0005219847,0.001262602,0.040003546,0.0007978658,0.001726822,0.010611269,0.00079527684,0.10504714,0.011109781,0.54405916,0.28358284,0.00048169645],"about_ca_topic_score_codex":0.0016921237,"about_ca_topic_score_gemma":0.0010221555,"teacher_disagreement_score":0.015239066,"about_ca_system_score_codex":0.0009883892,"about_ca_system_score_gemma":0.0021550325,"threshold_uncertainty_score":0.08059281},"labels":[],"label_agreement":null},{"id":"W2800268863","doi":"10.6000/1929-6029.2018.07.02.1","title":"A Pointwise Approach to Dose-Response Meta-Analysis of Aggregated Data","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pointwise; Meta-analysis; Mathematics; Statistics; Range (aeronautics); Computer science; Econometrics; Medicine; Internal medicine","score_opus":0.924761995411877,"score_gpt":0.6949515003111545,"score_spread":0.2298104951007225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800268863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011428351,0.0075181117,0.9867531,0.000703851,0.00042567885,0.001291365,0.0010720115,0.0007460285,0.00034713137],"genre_scores_gemma":[0.060451906,0.004639632,0.9217645,0.0012510908,0.0003848443,0.008864538,0.00131419,0.0004799244,0.00084946834],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6803167,0.2953319,0.008036679,0.009759477,0.005907047,0.00064816757],"domain_scores_gemma":[0.8239694,0.14575812,0.0048660254,0.021184847,0.0038851239,0.00033642657],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.20696382,0.0049985712,0.012077839,0.014163974,0.0008912121,0.004511617,0.0063389754,0.004807441,0.007263568],"category_scores_gemma":[0.25359952,0.0027171592,0.04411619,0.010808461,0.0016143203,0.0035174035,0.004660524,0.0072858487,0.0013807728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042762402,0.0002513432,0.008994026,0.04117785,0.4120386,0.0009302187,0.00093833567,0.14233,0.0035945373,0.08452871,0.014560314,0.28637984],"study_design_scores_gemma":[0.0021153216,0.0027380444,0.0059860474,0.004894269,0.16876353,0.0010591309,0.00026172114,0.29121646,0.004914601,0.48007986,0.037390027,0.0005810304],"about_ca_topic_score_codex":0.0020413676,"about_ca_topic_score_gemma":0.0021197854,"teacher_disagreement_score":0.79303616,"about_ca_system_score_codex":0.0020173793,"about_ca_system_score_gemma":0.0031724956,"threshold_uncertainty_score":0.9779548},"labels":[],"label_agreement":null},{"id":"W2800451635","doi":"10.6000/1929-6029.2018.07.02.4","title":"Bayesian Analysis of Markov Based Logistic Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayes factor; Exponential function; Statistics; Mathematics; Bayes' theorem; Variable-order Bayesian network; Bayes estimator; Function (biology); Applied mathematics; Logistic regression; Markov model; Bayesian inference; Econometrics; Computer science; Markov chain","score_opus":0.2158784806776032,"score_gpt":0.5573796766179351,"score_spread":0.3415011959403319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800451635","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02845463,0.0011384451,0.9663173,0.0008171092,0.00006900124,0.000071364775,0.00048337792,0.00022333558,0.0024255961],"genre_scores_gemma":[0.7709018,0.0053917556,0.20122291,0.00047540755,0.0005441664,0.0006405305,0.0031258566,0.0002601741,0.01743737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99675035,0.0018049554,0.000120817786,0.00049480336,0.0005659595,0.00026313664],"domain_scores_gemma":[0.9896702,0.008397214,0.0006963733,0.00036547912,0.0006666224,0.0002041432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006212457,0.00067689066,0.0016106119,0.0018745388,0.00073261856,0.0016111663,0.0018451263,0.0011786534,0.0051257326],"category_scores_gemma":[0.022695938,0.0007191786,0.0016001696,0.0016030989,0.0009837662,0.0026788802,0.0017677315,0.0024049743,0.0007552843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028173198,0.000121989026,0.009655201,0.00032595155,0.00031726484,0.00044169198,0.00041253184,0.5030254,0.0011168096,0.39654887,0.0060253562,0.081727155],"study_design_scores_gemma":[0.000015389314,0.0000251236,0.000842208,0.00003130385,0.000028509658,0.000067824665,0.000021023587,0.90847516,0.00011658399,0.08916515,0.001186834,0.00002476107],"about_ca_topic_score_codex":0.011252515,"about_ca_topic_score_gemma":0.007860741,"teacher_disagreement_score":0.011252515,"about_ca_system_score_codex":0.0013756355,"about_ca_system_score_gemma":0.001904953,"threshold_uncertainty_score":0.032855034},"labels":[],"label_agreement":null},{"id":"W2801391711","doi":"10.6000/1929-6029.2018.07.02.2","title":"Exploring the Performance of Methods to Deal Multicollinearity: Simulation and Real Data in Radiation Epidemiology Area","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multicollinearity; Collinearity; Statistics; Variance inflation factor; Regression analysis; Bivariate analysis; Regression; Mean squared error; Mathematics; Linear regression; Lasso (programming language); Econometrics; Computer science","score_opus":0.7652472274177364,"score_gpt":0.6834955550353583,"score_spread":0.08175167238237813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801391711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5096543,0.0056888987,0.47391367,0.004395313,0.00033300518,0.00031121602,0.0012177805,0.0006088539,0.0038769545],"genre_scores_gemma":[0.8313754,0.0014982534,0.16397431,0.000497465,0.00012490494,0.00030694617,0.0011692526,0.00013095669,0.00092248834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900905,0.008478175,0.00028067824,0.00049836194,0.00045970178,0.00019253844],"domain_scores_gemma":[0.8446522,0.14380367,0.0037524018,0.0033407384,0.0038238724,0.0006271389],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024577929,0.0011776732,0.0013916503,0.001332383,0.00065647514,0.0016925691,0.0016360186,0.0024825353,0.0015202087],"category_scores_gemma":[0.06585925,0.0005668336,0.001943901,0.0015706336,0.0011191538,0.001452719,0.001407953,0.0027870662,0.00026771065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030227983,0.00021707382,0.03236822,0.00063831604,0.00046020045,0.00018969462,0.00053461373,0.92354536,0.0006296995,0.0074841203,0.0017471688,0.031883303],"study_design_scores_gemma":[0.000048317233,0.00017567504,0.0036675192,0.00013936724,0.000062114435,0.000081509635,0.00020770413,0.98685116,0.00059519045,0.006644317,0.0014924109,0.000034741177],"about_ca_topic_score_codex":0.013537701,"about_ca_topic_score_gemma":0.009526799,"teacher_disagreement_score":0.9754221,"about_ca_system_score_codex":0.000931088,"about_ca_system_score_gemma":0.0018052026,"threshold_uncertainty_score":0.12998205},"labels":[],"label_agreement":null},{"id":"W2810188754","doi":"10.6000/1929-6029.2018.07.03.1","title":"A Note on the Area under the Gains Chart","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chart; Receiver operating characteristic; Computer science; Artificial intelligence; Classifier (UML); Statistics; Shewhart individuals control chart; EWMA chart; Control chart; Pattern recognition (psychology); Machine learning; Mathematics; Process (computing)","score_opus":0.17035335559078355,"score_gpt":0.49281998398945087,"score_spread":0.32246662839866735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810188754","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013293743,0.039004065,0.817181,0.051444776,0.013787922,0.0004974169,0.0024846501,0.004964731,0.0573417],"genre_scores_gemma":[0.37646145,0.021714445,0.52351236,0.021638978,0.025692072,0.0014877076,0.0027285786,0.0038344925,0.022929838],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9625119,0.019199258,0.0027389287,0.003970653,0.010762158,0.0008169888],"domain_scores_gemma":[0.7360207,0.2105594,0.007957275,0.016586013,0.027292706,0.0015839236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036400292,0.0015891872,0.0022213852,0.004394733,0.001269735,0.007863255,0.0029302016,0.0040595476,0.0083476985],"category_scores_gemma":[0.2789803,0.0006840152,0.001666637,0.003923537,0.0052600564,0.010257573,0.0034970867,0.011868752,0.0030207757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078518625,0.00013029453,0.00874591,0.0007578224,0.0002518985,0.0010084751,0.00080390804,0.021240069,0.001728378,0.43895411,0.17261945,0.3529746],"study_design_scores_gemma":[0.0001811273,0.0008000199,0.009763294,0.0011214165,0.00021315366,0.0026916948,0.0004995818,0.07262557,0.0037437472,0.6294121,0.27835426,0.0005940612],"about_ca_topic_score_codex":0.0043685148,"about_ca_topic_score_gemma":0.0017602411,"teacher_disagreement_score":0.036400292,"about_ca_system_score_codex":0.002206262,"about_ca_system_score_gemma":0.0029184837,"threshold_uncertainty_score":0.19250542},"labels":[],"label_agreement":null},{"id":"W2810389932","doi":"10.6000/1929-6029.2018.07.03.3","title":"Inference about the Population Kurtosis with Confidence: Parametric and Bootstrap Approaches","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kurtosis; Confidence interval; Statistics; Percentile; Coverage probability; Confidence distribution; Mathematics; Estimator; CDF-based nonparametric confidence interval; Parametric statistics; Inference; Econometrics; Sampling distribution; Computer science; Artificial intelligence","score_opus":0.36230253039620447,"score_gpt":0.5433454513986585,"score_spread":0.18104292100245406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810389932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01245082,0.0014827017,0.98450583,0.00020607027,0.00003856652,0.000027929196,0.000096260424,0.000164885,0.001027033],"genre_scores_gemma":[0.6767127,0.003229539,0.31720352,0.00027499069,0.0005471543,0.00038247427,0.0006415775,0.00023780469,0.0007700402],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9857606,0.008761822,0.00064750295,0.0013627529,0.0030609278,0.0004064174],"domain_scores_gemma":[0.8781838,0.103923514,0.00603846,0.0066732406,0.0046762815,0.0005047495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023587514,0.0011489021,0.0016548061,0.005705345,0.0006500252,0.0023079729,0.0025380708,0.0018680984,0.0013195876],"category_scores_gemma":[0.19217665,0.00060542277,0.001426516,0.0037858656,0.0030674634,0.0046340623,0.0029245757,0.002581561,0.00039220642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042905362,0.00016241796,0.027674794,0.0011807147,0.0007669729,0.0010253141,0.0011692672,0.32298732,0.003112827,0.28466398,0.0036541622,0.3531732],"study_design_scores_gemma":[0.00004645418,0.00016922949,0.008015302,0.0003684262,0.00012562476,0.0006959789,0.0003646985,0.67563117,0.0039265812,0.30529344,0.005204505,0.00015861311],"about_ca_topic_score_codex":0.0012602619,"about_ca_topic_score_gemma":0.0004902187,"teacher_disagreement_score":0.023587514,"about_ca_system_score_codex":0.0006502396,"about_ca_system_score_gemma":0.00081429334,"threshold_uncertainty_score":0.12474418},"labels":[],"label_agreement":null},{"id":"W2810829437","doi":"10.6000/1929-6029.2018.07.03.4","title":"On Extended Normal Distribution Model with Application in Health Care","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Probability and Statistical Research","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normal distribution; Distribution (mathematics); Representation (politics); Class (philosophy); Computer science; Applied mathematics; Statistical physics; Mathematics; Statistics; Artificial intelligence; Physics; Mathematical analysis","score_opus":0.09278507291033948,"score_gpt":0.5182994628810852,"score_spread":0.4255143899707457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810829437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075285463,0.0031383738,0.97973526,0.0016631973,0.00025866696,0.00005208686,0.0001901517,0.00015069301,0.007283002],"genre_scores_gemma":[0.69308877,0.011572234,0.27037293,0.0013121003,0.0012231701,0.00057673774,0.00087599456,0.00018113824,0.020796904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99687284,0.0014771941,0.00011974548,0.00055920955,0.00076577446,0.00020522182],"domain_scores_gemma":[0.99590427,0.0025730245,0.00032573074,0.00025568847,0.00079513795,0.00014611936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038767774,0.0008442373,0.0009993284,0.0015813629,0.00051980803,0.0017698144,0.0020428144,0.0019630548,0.004283786],"category_scores_gemma":[0.012035281,0.00030796084,0.0011185757,0.0027001256,0.0017533646,0.003622212,0.0017106461,0.0026327968,0.0009861176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007783417,0.00006425206,0.0029584025,0.00023097558,0.00005282087,0.00048974814,0.00036772908,0.15406121,0.0007280751,0.78107536,0.0052803596,0.054613147],"study_design_scores_gemma":[0.000023598923,0.00008852099,0.00076242397,0.00007668324,0.000023830054,0.00037735057,0.00009865572,0.5242886,0.00024453225,0.46245432,0.011518665,0.000042726995],"about_ca_topic_score_codex":0.005688614,"about_ca_topic_score_gemma":0.0023986595,"teacher_disagreement_score":0.005688614,"about_ca_system_score_codex":0.0017775004,"about_ca_system_score_gemma":0.0014882166,"threshold_uncertainty_score":0.020502567},"labels":[],"label_agreement":null},{"id":"W2811168520","doi":"10.6000/1929-6029.2018.07.03.2","title":"Intuitionistic Fuzzy Soft Set Theory and Its Application in Medical Diagnosis","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Fuzzy and Soft Set Theory","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Soft set; Vagueness; Fuzzy set; Fuzzy set operations; Type-2 fuzzy sets and systems; Mathematics; Fuzzy logic; Fuzzy classification; Set (abstract data type); Field (mathematics); Set theory; Defuzzification; Fuzzy number; Computer science; Artificial intelligence; Pure mathematics","score_opus":0.1315748271636601,"score_gpt":0.5291323717478654,"score_spread":0.39755754458420534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811168520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0109378975,0.015862491,0.9458685,0.0025823053,0.0004873565,0.000058799505,0.00010138545,0.0000948971,0.024006426],"genre_scores_gemma":[0.6155984,0.018453063,0.35680947,0.0010203742,0.0017116577,0.00020021462,0.0001648778,0.00003224632,0.006009891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834764,0.0007172933,0.00011225706,0.000193579,0.0005427923,0.000086459615],"domain_scores_gemma":[0.9981774,0.0012442169,0.00018867488,0.000093714276,0.00022130265,0.00007461756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025477526,0.00067034696,0.001023059,0.0030749415,0.00089157815,0.0024224713,0.0009561919,0.0015268342,0.0024959773],"category_scores_gemma":[0.0045515397,0.0003127002,0.001500574,0.0019818812,0.0033507198,0.0018335519,0.0014738124,0.002260201,0.00038640364],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052787822,0.00004889694,0.0010786214,0.00045691087,0.00010943828,0.00057366857,0.0005780486,0.03846035,0.0021966484,0.88345003,0.0023498258,0.07064477],"study_design_scores_gemma":[0.000015652753,0.000101423306,0.001027273,0.0002329376,0.00004362254,0.0006942649,0.00023477954,0.14741299,0.0011538574,0.8328674,0.016133778,0.00008195811],"about_ca_topic_score_codex":0.0015513764,"about_ca_topic_score_gemma":0.0007583354,"teacher_disagreement_score":0.0030749415,"about_ca_system_score_codex":0.0015794864,"about_ca_system_score_gemma":0.001303459,"threshold_uncertainty_score":0.013473988},"labels":[],"label_agreement":null},{"id":"W2811213454","doi":"10.6000/1929-6029.2018.07.03.5","title":"Parametric Analysis of Renal Failure Data using the Exponentiated Odd Weibull Distribution","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Censoring (clinical trials); Parametric statistics; Statistics; Hazard ratio; Survival analysis; Proportional hazards model; Parametric model; Mathematics; Computer science","score_opus":0.3241975562419944,"score_gpt":0.5662858724630051,"score_spread":0.24208831622101068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811213454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19922736,0.0009286851,0.79706895,0.00026892876,0.00005282208,0.000149842,0.0005594479,0.00041141547,0.0013324879],"genre_scores_gemma":[0.940203,0.00061153306,0.05710318,0.00007222982,0.00007422495,0.00019380666,0.0007901763,0.00008473757,0.0008670253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978808,0.0010973113,0.00012136257,0.00030267658,0.0004035239,0.0001943879],"domain_scores_gemma":[0.98379403,0.011790911,0.0019448056,0.0013756794,0.00091193244,0.00018267204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006245309,0.0007840609,0.0007571335,0.0022413917,0.0004049053,0.0009564729,0.00090289005,0.0006546732,0.0013697207],"category_scores_gemma":[0.02260592,0.00018604686,0.0011929225,0.0014815017,0.0007512337,0.0016085721,0.00094181934,0.0010524482,0.00031377556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045399886,0.00014837469,0.1829682,0.000342497,0.00043765362,0.0016474228,0.0011858552,0.58355886,0.005236612,0.047968097,0.002110094,0.1739423],"study_design_scores_gemma":[0.0000134255815,0.00027136912,0.030381357,0.00005923859,0.00006168339,0.0007330307,0.00028417876,0.9379315,0.0014521251,0.026452322,0.00228093,0.00007877772],"about_ca_topic_score_codex":0.0016293739,"about_ca_topic_score_gemma":0.0009585032,"teacher_disagreement_score":0.006245309,"about_ca_system_score_codex":0.0005661592,"about_ca_system_score_gemma":0.0005026389,"threshold_uncertainty_score":0.03302878},"labels":[],"label_agreement":null},{"id":"W2898416500","doi":"10.6000/1929-6029.2018.07.04.2","title":"On Comparing Survival Curves with Right-Censored Data According to the Events Occur at the Beginning, in the Middle and at the End of Study Period","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Period (music); Wilcoxon signed-rank test; Statistics; Event (particle physics); Log-rank test; Type I and type II errors; Test (biology); Mathematics; Survival analysis; Psychology; Econometrics; Demography; Biology; Mann–Whitney U test; Sociology","score_opus":0.7100981351492474,"score_gpt":0.6440371806920502,"score_spread":0.06606095445719717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898416500","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4933512,0.013477478,0.4788081,0.002363734,0.0005503656,0.0012125237,0.0043061413,0.0010738217,0.004856658],"genre_scores_gemma":[0.92320955,0.0018869743,0.06967002,0.0004401845,0.00015928794,0.0006441725,0.0028811966,0.00017350966,0.00093510037],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94961053,0.040438615,0.0018551147,0.004223716,0.0029821047,0.00088989816],"domain_scores_gemma":[0.6419772,0.33570743,0.010169703,0.008149157,0.0029725241,0.0010240681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.065986745,0.0010185839,0.0020128503,0.004468817,0.00060475,0.0022055025,0.0014699901,0.0018411742,0.0049977032],"category_scores_gemma":[0.20911145,0.00024407638,0.0036043683,0.0038326818,0.0022500898,0.003682877,0.0014288409,0.0020812706,0.0004514894],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013231802,0.00073046726,0.3498101,0.004875151,0.008981676,0.0014462549,0.0026169382,0.17041726,0.0029674417,0.038933646,0.0041919793,0.4017973],"study_design_scores_gemma":[0.0007980709,0.012972464,0.25912946,0.0018497258,0.0035494908,0.0040145717,0.0035261232,0.57082826,0.0074743205,0.11796319,0.017335238,0.0005590082],"about_ca_topic_score_codex":0.0017720197,"about_ca_topic_score_gemma":0.0009883835,"teacher_disagreement_score":0.065986745,"about_ca_system_score_codex":0.0012256948,"about_ca_system_score_gemma":0.001509793,"threshold_uncertainty_score":0.34897542},"labels":[],"label_agreement":null},{"id":"W2898548130","doi":"10.6000/1929-6029.2018.07.04.3","title":"A Correlation Technique to Reduce the Number of Predictors to Estimate the Survival Time of HIV/ AIDS Patients on ART","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Marital status; Spouse; Demography; Medicine; Categorical variable; Correlation; Variables; Gerontology; Statistics; Mathematics; Population; Environmental health","score_opus":0.0568407825366161,"score_gpt":0.49914303503948865,"score_spread":0.44230225250287253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898548130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10925534,0.0011537591,0.8836992,0.00042673922,0.0003352306,0.00034573593,0.0013724733,0.0017203259,0.0016913126],"genre_scores_gemma":[0.47298372,0.0014977342,0.5137185,0.00021013347,0.0003138926,0.0013618896,0.0038475548,0.0004365559,0.005630077],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962604,0.0021867848,0.00030153876,0.0004884693,0.0005465361,0.00021623417],"domain_scores_gemma":[0.98667485,0.010068675,0.00088815036,0.0010403472,0.0011956089,0.00013240792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066785025,0.0014532192,0.0014505176,0.0023743606,0.00071520515,0.0009024664,0.0008859805,0.00076328317,0.0042697787],"category_scores_gemma":[0.015523246,0.0005614164,0.0029163046,0.0028639894,0.000525993,0.0007260698,0.0009793988,0.002393601,0.0013885576],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001118094,0.00077077444,0.20576638,0.0013131305,0.0034857164,0.0017195834,0.0013599482,0.14315975,0.01882807,0.022077005,0.014039584,0.58636206],"study_design_scores_gemma":[0.00010831237,0.0015014764,0.06679997,0.00028468555,0.0008584712,0.0012000011,0.00056252227,0.8876628,0.011070016,0.008915568,0.020839168,0.00019700493],"about_ca_topic_score_codex":0.006896774,"about_ca_topic_score_gemma":0.005351339,"teacher_disagreement_score":0.006896774,"about_ca_system_score_codex":0.00041890767,"about_ca_system_score_gemma":0.0021142773,"threshold_uncertainty_score":0.035319746},"labels":[],"label_agreement":null},{"id":"W2898563665","doi":"10.6000/1929-6029.2018.07.04.1","title":"A Simulation Based Evaluation of Sample Size Methods for Biomarker Studies","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Biomarker; Sample size determination; Statistical power; Type I and type II errors; Statistics; Nominal level; Sample (material); Computer science; Medicine; Oncology; Mathematics; Biology; Confidence interval; Chemistry","score_opus":0.8960419701603737,"score_gpt":0.809772509734291,"score_spread":0.08626946042608263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898563665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03162171,0.004610063,0.95468545,0.002099799,0.0003712652,0.0010132687,0.00037443617,0.00037257187,0.004851474],"genre_scores_gemma":[0.42888674,0.0028384544,0.5617001,0.0009151757,0.0002272799,0.0031773788,0.0005170536,0.00019930126,0.0015385592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9319168,0.06162957,0.0011998047,0.0012521647,0.0035205819,0.000481126],"domain_scores_gemma":[0.45610645,0.5218038,0.0066033984,0.006804468,0.007725182,0.00095663924],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08814674,0.001554794,0.0018259804,0.002065591,0.00063411583,0.0018326384,0.0020760393,0.002383229,0.0041796654],"category_scores_gemma":[0.31353563,0.0006417282,0.0016298845,0.0018008308,0.0017198346,0.002292678,0.0019930587,0.0025230509,0.00039442687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002530031,0.0002640702,0.008701084,0.0013332498,0.000714747,0.0001958124,0.00043051352,0.7504214,0.0012021428,0.12889136,0.004972388,0.100343205],"study_design_scores_gemma":[0.00037910012,0.0006560184,0.0010403527,0.0003745568,0.00014428044,0.0001361778,0.00006642146,0.9560104,0.0009443594,0.037142456,0.0030660818,0.000039831615],"about_ca_topic_score_codex":0.0024366216,"about_ca_topic_score_gemma":0.0014768647,"teacher_disagreement_score":0.91185325,"about_ca_system_score_codex":0.0023953016,"about_ca_system_score_gemma":0.0032180285,"threshold_uncertainty_score":0.46617007},"labels":[],"label_agreement":null},{"id":"W2900745997","doi":"10.6000/1929-6029.2018.07.04.4","title":"Combining Survival and Toxicity Effect Sizes from Clinical Trials: NCCTG 89-20-52 (Alliance)","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Toxicity; Medicine; Clinical trial; Radiation therapy; Internal medicine; Overall survival; Survival analysis; Quality of life (healthcare); Survival rate; Randomized controlled trial; Oncology","score_opus":0.7744812795514252,"score_gpt":0.7272900553013018,"score_spread":0.047191224250123454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900745997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041662935,0.034206092,0.88176036,0.0059182886,0.0021257526,0.020182302,0.0042061354,0.0017133851,0.00822477],"genre_scores_gemma":[0.35393628,0.0028069345,0.6203235,0.0025991288,0.0007962284,0.016428921,0.0015813019,0.0004036704,0.0011240348],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.62886196,0.30351028,0.02709379,0.010640161,0.029119281,0.00077458477],"domain_scores_gemma":[0.57032907,0.3312709,0.037758533,0.033044536,0.025961787,0.0016352352],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3060963,0.0025246944,0.004183863,0.007809184,0.00048764466,0.0035569882,0.0023014522,0.0020246683,0.002266811],"category_scores_gemma":[0.37791213,0.0009894256,0.012567079,0.0057013696,0.0017834344,0.0018451508,0.0034024767,0.00368438,0.00034895356],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011288242,0.0004448939,0.048198063,0.016379807,0.08587649,0.00025418002,0.0009770022,0.029597256,0.0072778296,0.01732076,0.0134921875,0.76889336],"study_design_scores_gemma":[0.035980124,0.026182553,0.22681294,0.009798215,0.13140996,0.0024363205,0.00043276156,0.19319889,0.028756721,0.16745414,0.17591208,0.0016252124],"about_ca_topic_score_codex":0.0015661117,"about_ca_topic_score_gemma":0.0028930027,"teacher_disagreement_score":0.6939037,"about_ca_system_score_codex":0.0029973583,"about_ca_system_score_gemma":0.0035440845,"threshold_uncertainty_score":0.8557068},"labels":[],"label_agreement":null},{"id":"W2936048997","doi":"10.6000/1929-6029.2019.08.01","title":"Bayesian Model Averaging for Selection of a Risk Prediction Model for Death within Thirty Days of Discharge: The SILVER-AMI Study","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Akaike information criterion; Bayesian information criterion; Statistics; Model selection; Observational study; Bayes' theorem; Selection (genetic algorithm); Statistic; Context (archaeology); Bayesian probability; Posterior probability; Mathematics; Econometrics; Medicine; Computer science; Artificial intelligence","score_opus":0.11784041491150081,"score_gpt":0.4817388968654298,"score_spread":0.363898481953929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936048997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3910046,0.0007353135,0.60525554,0.0010129153,0.00006184044,0.0005284247,0.00044696056,0.0002044027,0.00074999646],"genre_scores_gemma":[0.6945981,0.00035533935,0.3022565,0.00030960576,0.00009949467,0.00097294204,0.0009774115,0.00006244079,0.00036817614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9810952,0.017386727,0.00038256778,0.00039006435,0.00060562976,0.00013985163],"domain_scores_gemma":[0.96095043,0.03423488,0.0016070367,0.0018051176,0.001002464,0.00039999862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039452575,0.00069546635,0.0015786407,0.0010171711,0.0005927509,0.0007679833,0.0016571332,0.00061313726,0.00090583955],"category_scores_gemma":[0.07481188,0.00045823577,0.001960687,0.0010638823,0.00039028117,0.00060195324,0.001560291,0.0018215629,0.00013381639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038573537,0.0011365447,0.25693667,0.0005826972,0.005794968,0.001666542,0.0017702422,0.43202338,0.003515659,0.03757101,0.007398759,0.24774618],"study_design_scores_gemma":[0.00051266194,0.0007332224,0.014246308,0.00008353674,0.00049079483,0.00017253544,0.00012399074,0.9495738,0.0006471014,0.03186957,0.001495932,0.000050433977],"about_ca_topic_score_codex":0.005653444,"about_ca_topic_score_gemma":0.006767219,"teacher_disagreement_score":0.039452575,"about_ca_system_score_codex":0.0005236229,"about_ca_system_score_gemma":0.0017587127,"threshold_uncertainty_score":0.20864761},"labels":[],"label_agreement":null},{"id":"W2940062173","doi":"10.6000/1929-6029.2019.08.02","title":"Italian Version of the Risk Assessment and Prediction Tool: Properties and Usefulness of a Decision-Making Tool for Subjects’ Discharge after Total Hip and Knee Arthroplasty","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Humanitas Research Hospital; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Intraclass correlation; Physical therapy; Logistic regression; Confidence interval; Reliability (semiconductor); Medicine; Arthroplasty; Rehabilitation; Test (biology); Odds ratio; Psychological intervention; Physical medicine and rehabilitation; Surgery; Psychometrics; Internal medicine; Clinical psychology","score_opus":0.03037785026592468,"score_gpt":0.3618549584063439,"score_spread":0.33147710814041925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940062173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98202413,0.0010144484,0.00440555,0.00086984417,0.00013894463,0.0010831194,0.0024646195,0.00014171115,0.007857609],"genre_scores_gemma":[0.9786145,0.0007106983,0.014893638,0.00019364132,0.00010218573,0.0015915268,0.0025306873,0.00003476151,0.001328461],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9949055,0.0024687478,0.0010063377,0.00034150036,0.0010807558,0.0001972061],"domain_scores_gemma":[0.9809792,0.009965813,0.0048585515,0.0011031658,0.002502564,0.0005907085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009500218,0.0007836367,0.00075708947,0.0019384477,0.00042788038,0.0009884466,0.00071937975,0.0006747822,0.003558038],"category_scores_gemma":[0.03377244,0.0002522851,0.0019115346,0.001325591,0.00048908195,0.0007458903,0.0011926771,0.00084073277,0.0009325503],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011410341,0.00075568684,0.8984564,0.00048102572,0.0003811241,0.00023649189,0.0021491433,0.0013444308,0.00052615337,0.00044141168,0.006201823,0.087885365],"study_design_scores_gemma":[0.00021268775,0.0013464559,0.9859154,0.00028336237,0.00019581328,0.00070004433,0.0006033283,0.0050145006,0.00039346408,0.0006452845,0.004608974,0.00008080851],"about_ca_topic_score_codex":0.0017867363,"about_ca_topic_score_gemma":0.002127458,"teacher_disagreement_score":0.009500218,"about_ca_system_score_codex":0.00077218417,"about_ca_system_score_gemma":0.0011916369,"threshold_uncertainty_score":0.050242543},"labels":[],"label_agreement":null},{"id":"W2944438955","doi":"10.6000/1929-6029.2019.08.03","title":"Improvement in Heart Rate Variability Following Spinal Adjustment: A Case Study in Statistical Methodology for a Single Office Visit","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chiropractic; Heart rate variability; Medicine; Physical therapy; Vertebra; Subluxation; Physical medicine and rehabilitation; Outlier; Spinal manipulation; Heart rate; Surgery; Internal medicine; Computer science; Artificial intelligence; Pathology","score_opus":0.1341567271349329,"score_gpt":0.49953565860910937,"score_spread":0.36537893147417644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944438955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8914202,0.0035434423,0.08697904,0.009560973,0.0005155786,0.0006407802,0.00016416423,0.0004203891,0.0067553655],"genre_scores_gemma":[0.96546394,0.0009974231,0.030515248,0.0012250234,0.00043392723,0.00030871393,0.000060066264,0.00012322872,0.00087245926],"study_design_codex":"observational","study_design_gemma":"case_report","domain_scores_codex":[0.975255,0.016418088,0.0016409743,0.0017137636,0.0042039314,0.0007681625],"domain_scores_gemma":[0.9426708,0.038557686,0.00904109,0.0043426394,0.0031938879,0.0021937753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022596192,0.00050151785,0.0006681338,0.0015535583,0.0012553511,0.0018495217,0.001499268,0.0019263963,0.0021301422],"category_scores_gemma":[0.048440255,0.00034027765,0.0008872383,0.0015807315,0.0018853104,0.0012329004,0.0014109443,0.0022633949,0.0006725826],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071575213,0.0014579525,0.6226697,0.00093528745,0.00019293073,0.12494921,0.027794868,0.0008624526,0.008038923,0.0040852986,0.0073544104,0.20094316],"study_design_scores_gemma":[0.00009050744,0.00652593,0.49320903,0.0017045544,0.00024216369,0.40173164,0.02871195,0.015613238,0.012511531,0.007916591,0.031346466,0.00039627485],"about_ca_topic_score_codex":0.0007776216,"about_ca_topic_score_gemma":0.0016537461,"teacher_disagreement_score":0.022596192,"about_ca_system_score_codex":0.0011156327,"about_ca_system_score_gemma":0.0012406134,"threshold_uncertainty_score":0.11950153},"labels":[],"label_agreement":null},{"id":"W2956657146","doi":"10.6000/1929-6029.2019.08.04","title":"An Alternative Stratified Cox Model for Correlated Variables in Infant Mortality","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proportional hazards model; Statistics; Estimator; Econometrics; Mathematics; Regression analysis; Breastfeeding; Regression; Stratum; Variables; Medicine; Pediatrics","score_opus":0.2056084368288006,"score_gpt":0.551208023315861,"score_spread":0.34559958648706046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956657146","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01820189,0.0007068903,0.9773818,0.0006161615,0.00017881731,0.0002531622,0.0011255298,0.0002650568,0.001270781],"genre_scores_gemma":[0.6769655,0.002908281,0.28372166,0.00089776353,0.00071810465,0.002840805,0.004452966,0.00020719742,0.027287757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99465996,0.003317799,0.00016379847,0.0008653238,0.00054239266,0.00045083242],"domain_scores_gemma":[0.9922897,0.0050971587,0.0006891291,0.00084065954,0.00085581304,0.00022751586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010246885,0.0012193525,0.0017030938,0.0012439282,0.0005927427,0.0017604949,0.004112151,0.0016180944,0.0062299035],"category_scores_gemma":[0.01410336,0.00076887925,0.0028365825,0.0018569732,0.00076872756,0.0022150225,0.0013557593,0.0022516449,0.0012652188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014006731,0.0002548194,0.040668502,0.00064134796,0.001157379,0.0015327773,0.001496905,0.43315238,0.0034157175,0.39931312,0.009805816,0.10716059],"study_design_scores_gemma":[0.00017364004,0.0005365503,0.0046309447,0.00010222707,0.00040070835,0.0005413483,0.00016226717,0.8885897,0.00078562775,0.094454065,0.009520999,0.0001019708],"about_ca_topic_score_codex":0.00830716,"about_ca_topic_score_gemma":0.0061582895,"teacher_disagreement_score":0.010246885,"about_ca_system_score_codex":0.0010912134,"about_ca_system_score_gemma":0.002515472,"threshold_uncertainty_score":0.05419135},"labels":[],"label_agreement":null},{"id":"W2961449000","doi":"10.6000/1929-6029.2019.08.05","title":"Evaluation and Comparison of Patterns of Maternal Complications Using Generalized Linear Models of Count Data Time Series","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Maternal and fetal healthcare","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Childbirth; Sepsis; Eclampsia; Obstetrics; Complication; Maternal morbidity; Pregnancy; Surgery","score_opus":0.33353071648985827,"score_gpt":0.569317535929525,"score_spread":0.23578681943966678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961449000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79052204,0.0014787801,0.20095955,0.0016372256,0.00025826303,0.0004731705,0.002002025,0.0009891967,0.0016797797],"genre_scores_gemma":[0.96877635,0.00053680065,0.02659464,0.000112946334,0.00006534694,0.00037709792,0.0018093262,0.00008333535,0.0016442366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99020946,0.007108125,0.00037067517,0.0013769588,0.000473307,0.0004614161],"domain_scores_gemma":[0.9716887,0.024143858,0.001870748,0.0011399753,0.0008776578,0.00027903545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016950196,0.0014425032,0.0013006484,0.0019739245,0.0004325016,0.002097533,0.0022215364,0.0012683397,0.0027240925],"category_scores_gemma":[0.03353451,0.00054822705,0.004052877,0.001712895,0.0007273455,0.0015564206,0.0012161256,0.0019706066,0.0004945396],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000958658,0.0005143006,0.28574008,0.00057680014,0.003959525,0.00058574404,0.00091788365,0.62136483,0.0013768281,0.01113187,0.0019997628,0.07087372],"study_design_scores_gemma":[0.000040347,0.0007813061,0.028252319,0.000061079285,0.00032832567,0.00007772825,0.0003758418,0.9650007,0.00033603798,0.0037826814,0.00092151406,0.000042162897],"about_ca_topic_score_codex":0.020405805,"about_ca_topic_score_gemma":0.008405284,"teacher_disagreement_score":0.020405805,"about_ca_system_score_codex":0.0011327671,"about_ca_system_score_gemma":0.0015963529,"threshold_uncertainty_score":0.08964223},"labels":[],"label_agreement":null},{"id":"W2971022468","doi":"10.6000/1929-6029.2019.08.06","title":"Multivariate Analysis of Data on Migraine Treatment","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multidimensional scaling; Profiling (computer programming); Multivariate statistics; Multivariate analysis; Psychology; Cluster (spacecraft); Data mining; Computer science; Mathematics; Artificial intelligence; Econometrics; Machine learning","score_opus":0.25046714305381557,"score_gpt":0.5279397906446741,"score_spread":0.27747264759085855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971022468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9158607,0.0006397357,0.07074346,0.00049868086,0.00012391615,0.00018265654,0.009222496,0.0004205159,0.002307835],"genre_scores_gemma":[0.9852207,0.00017154633,0.009307868,0.000026407097,0.000048222682,0.00015030685,0.0045479606,0.000035785473,0.0004911736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99326164,0.0041868626,0.00040482864,0.0007522939,0.0010569851,0.00033731826],"domain_scores_gemma":[0.98261833,0.011188677,0.0021033098,0.0027236121,0.0009887211,0.0003773221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005130104,0.00040447398,0.0007198852,0.0019766903,0.00032945754,0.00066874793,0.00039604589,0.0003777506,0.002989578],"category_scores_gemma":[0.022247387,0.00009790595,0.00072361075,0.0030146672,0.00047312022,0.0006042735,0.0008537421,0.00085333065,0.0002935351],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018149874,0.00039970834,0.74251664,0.00045434182,0.0021394226,0.0004040002,0.0012645426,0.016087955,0.009687132,0.004382404,0.0047584614,0.21609037],"study_design_scores_gemma":[0.000026278358,0.0010251729,0.93486184,0.000058777172,0.0002759781,0.00041284098,0.00095816905,0.047906063,0.0027360292,0.0045710406,0.007093232,0.00007450112],"about_ca_topic_score_codex":0.0023198184,"about_ca_topic_score_gemma":0.001666952,"teacher_disagreement_score":0.005130104,"about_ca_system_score_codex":0.0003932542,"about_ca_system_score_gemma":0.0005127413,"threshold_uncertainty_score":0.027130842},"labels":[],"label_agreement":null},{"id":"W2980942689","doi":"10.6000/1929-6029.2019.08.09","title":"Property of Melatonin of Acting as an Antihypertensive Agent to Antagonize Nocturnal High Blood Pressure: A Meta-Analysis","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Circadian rhythm and melatonin","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Melatonin; Blood pressure; Bedtime; Placebo; Nocturnal; Medicine; Internal medicine; Endocrinology","score_opus":0.12655151897474823,"score_gpt":0.42880974993762744,"score_spread":0.3022582309628792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980942689","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032352798,0.96384454,0.0013371328,0.00031919495,0.00052670937,0.00026143473,0.0007270433,0.000077516524,0.0005536051],"genre_scores_gemma":[0.7596027,0.23363973,0.0025181381,0.0010843703,0.0006330839,0.0006168956,0.0010976193,0.00006926789,0.000738233],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9920515,0.004326283,0.0017689908,0.00097271806,0.0006492544,0.00023126212],"domain_scores_gemma":[0.99039114,0.0065959105,0.0014897399,0.0006500407,0.0006309742,0.0002422623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012278171,0.003232844,0.016539272,0.0036716787,0.00060718466,0.0032518422,0.0019563371,0.0024067424,0.0032700794],"category_scores_gemma":[0.016452674,0.0015869621,0.06066195,0.00363641,0.0006456253,0.0012977999,0.001252548,0.0023128495,0.00031532111],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007295149,0.000026646143,0.0027719738,0.028985787,0.9569401,0.00005221233,0.00001956525,0.00038115424,0.00023859461,0.0000368474,0.00014325613,0.0031086735],"study_design_scores_gemma":[0.0008612986,0.00025538533,0.002697529,0.0008638977,0.9946325,0.00003095872,0.000010876574,0.00017606377,0.0000750664,0.00006746302,0.0003191492,0.000009811492],"about_ca_topic_score_codex":0.00461549,"about_ca_topic_score_gemma":0.0072402214,"teacher_disagreement_score":0.016539272,"about_ca_system_score_codex":0.0016525844,"about_ca_system_score_gemma":0.0011179445,"threshold_uncertainty_score":0.064933956},"labels":[],"label_agreement":null},{"id":"W3004756648","doi":"10.6000/1929-6029.2020.09.01","title":"Maximum Likelihood and Bayesian Estimation of Repeatability Index: Application of Estimating Ratio of Variance Components","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Statistics; Repeatability; Mathematics; Estimator; Maximum a posteriori estimation; Maximum likelihood","score_opus":0.04189406767475662,"score_gpt":0.40947044013655226,"score_spread":0.36757637246179564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004756648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005543718,0.00041116378,0.9930306,0.00009073727,0.000032063534,0.00006378017,0.000106078565,0.00019901838,0.00052284956],"genre_scores_gemma":[0.20102993,0.00093552185,0.7943304,0.00014303155,0.00018043691,0.00071172277,0.0008752082,0.00031717203,0.0014765922],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97797966,0.014531766,0.0009402487,0.0031500559,0.002965255,0.00043309655],"domain_scores_gemma":[0.9629667,0.028544303,0.0029529212,0.0026140222,0.0026976394,0.00022436453],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019856138,0.0014725064,0.002883622,0.004236473,0.0009167687,0.0020366674,0.002306268,0.0019985808,0.0021348544],"category_scores_gemma":[0.09672637,0.00086424215,0.0024161853,0.0038728311,0.0018350589,0.0029438008,0.0021400303,0.002218634,0.00070505106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033583996,0.0002906769,0.03294005,0.0012166792,0.002359942,0.00060166616,0.0009531887,0.23143351,0.00722692,0.21124776,0.0054258974,0.50596786],"study_design_scores_gemma":[0.00006222405,0.00025721168,0.015941685,0.00024273549,0.00033025266,0.0007422222,0.0001749826,0.7888139,0.0045477957,0.18080927,0.0078018988,0.00027586447],"about_ca_topic_score_codex":0.0042548575,"about_ca_topic_score_gemma":0.0030207743,"teacher_disagreement_score":0.98014385,"about_ca_system_score_codex":0.00113365,"about_ca_system_score_gemma":0.0026499813,"threshold_uncertainty_score":0.10501057},"labels":[],"label_agreement":null},{"id":"W3012472799","doi":"10.6000/1929-6029.2020.09.02","title":"Adverse Event Risk Assessment on Patients Receiving Combination Antiretroviral Therapy in South Africa","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lipodystrophy; Lipoatrophy; Medicine; Adverse effect; Cart; Peripheral neuropathy; Cohort; Internal medicine; Incidence (geometry); Antiretroviral therapy; Diarrhea; Stavudine; Cohort study; Logistic regression; Pediatrics; Surgery; Human immunodeficiency virus (HIV); Viral load; Immunology; Endocrinology; Diabetes mellitus","score_opus":0.06958892372962097,"score_gpt":0.4609138825657886,"score_spread":0.3913249588361676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012472799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971463,0.0013075222,0.00011460435,0.0001276462,0.000006114257,0.000056838275,0.00040935265,0.000003244583,0.0008284002],"genre_scores_gemma":[0.9988311,0.00052967813,0.00022534584,0.0000319013,0.000007332689,0.000027959246,0.00022397011,8.387073e-7,0.00012181133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994241,0.00022369546,0.00010383785,0.000058473608,0.00013555154,0.000054231863],"domain_scores_gemma":[0.997755,0.0002961857,0.0016574695,0.000038953058,0.00013862787,0.0001137231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068068533,0.00022115202,0.0003083457,0.00045312155,0.00030202945,0.00034694438,0.0001290086,0.00014870768,0.0013309289],"category_scores_gemma":[0.0026469347,0.000109445115,0.00035954532,0.00086014677,0.0001064713,0.00023399254,0.0003415846,0.0003054357,0.00009802834],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002746333,0.000030126876,0.99169815,0.00010896627,0.00007087332,0.00018210376,0.00010715063,0.000053469117,0.00030754393,0.000013612233,0.00011505949,0.007038358],"study_design_scores_gemma":[0.00001532714,0.00019999787,0.99848443,0.000043478216,0.000038577942,0.0005355179,0.00010921128,0.00010316592,0.00008811735,0.000016114027,0.00036280393,0.0000033027197],"about_ca_topic_score_codex":0.0020653845,"about_ca_topic_score_gemma":0.003032273,"teacher_disagreement_score":0.0020653845,"about_ca_system_score_codex":0.0002568411,"about_ca_system_score_gemma":0.00038428034,"threshold_uncertainty_score":0.004452467},"labels":[],"label_agreement":null},{"id":"W3034218606","doi":"10.6000/1929-6029.2020.09.05","title":"Inference Procedures on the Ratio of Modified Generalized Poisson Distribution Means: Applications to RNA_SEQ Data","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Negative binomial distribution; Count data; Poisson distribution; Statistics; Mathematics; Estimator; Statistical inference; Binomial distribution; Sample size determination; Inference; Overdispersion; Confidence interval; Binomial proportion confidence interval; Poisson regression; Variance (accounting); Beta-binomial distribution; Quasi-likelihood; Population; Computer science; Artificial intelligence","score_opus":0.22167514907988428,"score_gpt":0.4941758465509761,"score_spread":0.2725006974710918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034218606","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025147218,0.00023745405,0.99637103,0.000104713145,0.000025605861,0.0000665982,0.00011488233,0.0004408737,0.00012423542],"genre_scores_gemma":[0.035545595,0.00045726763,0.9618889,0.00018278629,0.00006830537,0.0005217175,0.00049144804,0.00036098185,0.0004829957],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98758787,0.008317211,0.00069731724,0.0016917498,0.0015050386,0.00020079059],"domain_scores_gemma":[0.9362165,0.05552752,0.0026001162,0.003160916,0.0020300054,0.00046502706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029184446,0.001227049,0.0019190776,0.0039708307,0.000977267,0.0017914984,0.0038793802,0.0022030785,0.0030474481],"category_scores_gemma":[0.09687787,0.0011792037,0.0027924387,0.003179251,0.001966756,0.0024065261,0.002705627,0.0040054927,0.0008769722],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006868007,0.00023429698,0.013724302,0.0015285189,0.001468839,0.0007062786,0.0014214118,0.2082356,0.018548852,0.21184921,0.007682431,0.5339135],"study_design_scores_gemma":[0.000119482356,0.00012535362,0.0043457104,0.00018668515,0.00018931585,0.0005770486,0.00015345761,0.708769,0.0067687184,0.27002627,0.008573844,0.00016508061],"about_ca_topic_score_codex":0.0041940967,"about_ca_topic_score_gemma":0.0045823674,"teacher_disagreement_score":0.029184446,"about_ca_system_score_codex":0.0014761728,"about_ca_system_score_gemma":0.002304031,"threshold_uncertainty_score":0.15434396},"labels":[],"label_agreement":null},{"id":"W3120252081","doi":"10.6000/1929-6029.2020.09.06","title":"The Effect of the Health Personnel Exposed to the Attack of Patients and Relatives on the Perception of Aggression","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Workplace Violence and Bullying","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Aggression; Dysfunctional family; Medicine; Occupational safety and health; Perception; Psychology; Clinical psychology; Family medicine; Psychiatry","score_opus":0.0848102550908149,"score_gpt":0.47178880821861213,"score_spread":0.3869785531277972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120252081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993011,0.00012501808,0.000028196038,0.00008284906,0.0000071647555,0.0000025880197,0.000011520232,7.7449494e-7,0.00044088488],"genre_scores_gemma":[0.9997634,0.00007445533,0.000026779364,0.000024322853,0.000008804761,0.0000022677998,0.000012261176,3.7009448e-7,0.000087263455],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99841607,0.00086261076,0.00011731846,0.00008504906,0.00032611593,0.00019285131],"domain_scores_gemma":[0.9941706,0.0014448253,0.0029898987,0.00013947689,0.00039380565,0.00086134207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008474786,0.0001937116,0.00018526001,0.00039862635,0.00048101516,0.0005483138,0.00014917279,0.00028076692,0.0024293824],"category_scores_gemma":[0.006607795,0.00013463001,0.00031626047,0.00018828672,0.00032890897,0.00020044569,0.00043622337,0.0005164273,0.00022594267],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001583351,0.0001765251,0.991683,0.00003858309,0.00006211974,0.00018968926,0.0023534931,0.000027552958,0.00037673127,0.000019765264,0.00011160694,0.0048025907],"study_design_scores_gemma":[0.000003345044,0.00030646156,0.99458224,0.000027016842,0.000018110524,0.00031057477,0.004448553,0.00004363261,0.00007387287,0.000013847405,0.00016733253,0.000005102772],"about_ca_topic_score_codex":0.0024726584,"about_ca_topic_score_gemma":0.0021698195,"teacher_disagreement_score":0.0024726584,"about_ca_system_score_codex":0.0002794469,"about_ca_system_score_gemma":0.0004200261,"threshold_uncertainty_score":0.008127153},"labels":[],"label_agreement":null},{"id":"W3140682778","doi":"10.6000/1929-6029.2012.01.02.03","title":"Regressions to Monitor Health Care Quality: A User’s Guide and a New Index","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Index (typography); Quality (philosophy); Computer science; Regression analysis; Health care; Simple linear regression; Regression; Software; Data mining; Linear regression; Statistics; Machine learning; Mathematics; World Wide Web","score_opus":0.28426237101408786,"score_gpt":0.5539183320469316,"score_spread":0.2696559610328438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140682778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029097418,0.004821644,0.7876447,0.004839364,0.0014545261,0.0014690326,0.07614541,0.08691564,0.0337999],"genre_scores_gemma":[0.012679838,0.00430669,0.9073664,0.0018544735,0.00079909596,0.0029673707,0.02239541,0.018704323,0.028926369],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99388015,0.0027896352,0.00091184815,0.00043855302,0.0018445574,0.0001352667],"domain_scores_gemma":[0.95829785,0.027988529,0.002283978,0.0041542696,0.0067576654,0.00051778083],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008195619,0.0032355308,0.0020477246,0.0054374905,0.000357475,0.0023722833,0.0024446726,0.0014859821,0.066191025],"category_scores_gemma":[0.04556513,0.0017402107,0.002287085,0.0083915,0.0006061052,0.0033541163,0.0021126,0.003736778,0.05669561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012958562,0.00018591409,0.0052485233,0.0011837728,0.00016939621,0.00020735706,0.00019007262,0.0042023542,0.0021198136,0.011642787,0.72342134,0.251299],"study_design_scores_gemma":[0.00027082878,0.0002688545,0.015691778,0.0010488406,0.00011175236,0.0011936598,0.00018563497,0.03222494,0.005906042,0.024044089,0.918754,0.0002995969],"about_ca_topic_score_codex":0.0028282457,"about_ca_topic_score_gemma":0.003478631,"teacher_disagreement_score":0.99180436,"about_ca_system_score_codex":0.00054414815,"about_ca_system_score_gemma":0.0014198289,"threshold_uncertainty_score":0.22143102},"labels":[],"label_agreement":null},{"id":"W3148574772","doi":"10.6000/1929-6029.2013.02.01.04","title":"Application of Probabilistic Linkage: Compare Health Care Costs among Menopausal Women with Different Symptoms by Linking Women’s Registry &amp; Claims Data","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Confidence interval; Record linkage; Menopause; Population; Logistic regression; Database; Internal medicine; Gynecology","score_opus":0.2125785715244492,"score_gpt":0.4991815647721378,"score_spread":0.2866029932476886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148574772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93043834,0.0009113613,0.05895442,0.0006136973,0.00008265668,0.0015678029,0.004824003,0.0001877763,0.0024199472],"genre_scores_gemma":[0.9697049,0.00021449533,0.026527762,0.0001134249,0.000045597448,0.00083009043,0.0023509169,0.000024965308,0.00018788986],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9457241,0.04578063,0.0017591957,0.0032963553,0.0029728487,0.000466885],"domain_scores_gemma":[0.9609572,0.023620566,0.008222861,0.004903658,0.0019906973,0.00030490494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0429109,0.0008418999,0.0010065588,0.0037476348,0.0006580647,0.0016712595,0.0018217067,0.00091869506,0.0021659515],"category_scores_gemma":[0.091970235,0.00063465396,0.0031810983,0.0051613194,0.0004951065,0.0013662525,0.0035281463,0.0008371881,0.00023713439],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047922684,0.00079050305,0.8975318,0.00036023362,0.011401932,0.00019619997,0.0004627587,0.03516613,0.00045903306,0.0025022607,0.0013701839,0.04496674],"study_design_scores_gemma":[0.002082418,0.005435621,0.64036435,0.00021108484,0.0073130773,0.0010299573,0.0007620496,0.32546055,0.0018418639,0.010079356,0.0051954286,0.00022421722],"about_ca_topic_score_codex":0.0050622625,"about_ca_topic_score_gemma":0.0018287549,"teacher_disagreement_score":0.0429109,"about_ca_system_score_codex":0.0011301183,"about_ca_system_score_gemma":0.001612477,"threshold_uncertainty_score":0.22693723},"labels":[],"label_agreement":null},{"id":"W3157526318","doi":"10.6000/1929-6029.2021.10.03","title":"On Statistical Analysis of Forecasting COVID-19 for the Upcoming Months in the Kingdom of Saudi Arabia","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Coronavirus disease 2019 (COVID-19); Herd immunity; Statistics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Statistical analysis; Econometrics; Epidemic model; Regression analysis; Demography; Operations research; Geography; Computer science; Medicine; Vaccination; Environmental health; Mathematics; Outbreak; Time series; Virology; Sociology","score_opus":0.5620440377590277,"score_gpt":0.6030209760941476,"score_spread":0.04097693833511995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157526318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.981912,0.00035842965,0.015660545,0.00037362336,0.000032188815,0.000024828985,0.0009106411,0.00008165628,0.00064600824],"genre_scores_gemma":[0.99541086,0.00013785761,0.0029338514,0.000022977847,0.000014943404,0.000014268222,0.0011816663,0.00000901834,0.00027450695],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982333,0.0009294671,0.000100498066,0.00031906375,0.00026570144,0.00015185887],"domain_scores_gemma":[0.979935,0.01540133,0.0018142468,0.00091841305,0.0016924659,0.00023859004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006235855,0.00044695014,0.00039370972,0.0012405585,0.0003129167,0.00081842666,0.0004428789,0.0005055462,0.00078275485],"category_scores_gemma":[0.02006109,0.00016266102,0.00066383305,0.0011385736,0.00031397154,0.0006273922,0.00043321314,0.00079108245,0.00018689872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038156766,0.00011659607,0.79337114,0.000103700426,0.00037176764,0.00043456105,0.0006536329,0.15825357,0.0015534865,0.0052870726,0.0017997761,0.037673116],"study_design_scores_gemma":[0.000007544826,0.00018075321,0.21500549,0.000033194134,0.000082462095,0.00014372115,0.0005875303,0.7795645,0.0013772765,0.0017324552,0.0012515078,0.000033572607],"about_ca_topic_score_codex":0.032031693,"about_ca_topic_score_gemma":0.015534406,"teacher_disagreement_score":0.032031693,"about_ca_system_score_codex":0.00104662,"about_ca_system_score_gemma":0.0007397132,"threshold_uncertainty_score":0.06369048},"labels":[],"label_agreement":null},{"id":"W3157805300","doi":"10.6000/1929-6029.2021.10.01","title":"Socio- Demographic, Clinical and Lifestyle Determinants of Low Response Rate on a Self- Reported Psychological Multi-Item Instrument Assessing the Adults’ Hostility and its Direction: ATTICA Epidemiological Study (2002-2012)","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hostility; Epidemiology; Logistic regression; Demography; Scale (ratio); Psychology; Medicine; Population; Clinical psychology; Environmental health; Geography","score_opus":0.2535178020601622,"score_gpt":0.5967673816830077,"score_spread":0.34324957962284547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157805300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99829787,0.000228819,0.00024261863,0.00006552193,0.000010645887,0.000061578576,0.00065721,0.000003934815,0.00043191182],"genre_scores_gemma":[0.99853635,0.00009088976,0.0003699808,0.000033290966,0.000012736994,0.00011687906,0.00065233064,0.0000020593689,0.00018560242],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965754,0.0018967415,0.000452135,0.00031250692,0.00056397996,0.00019925754],"domain_scores_gemma":[0.9933577,0.0017588635,0.0029692047,0.00059970055,0.00083274755,0.00048181115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004185874,0.00027578164,0.0003905132,0.0011485574,0.0004167272,0.0006055858,0.0004721113,0.0005673092,0.0011482725],"category_scores_gemma":[0.006213891,0.00026049596,0.0006181517,0.0010875376,0.00035229351,0.00028743639,0.0006144043,0.00064854044,0.0003013676],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006805316,0.000050457005,0.9984403,0.000023952234,0.000041856223,0.000023062883,0.0001527011,0.000016882872,0.000096901975,0.0000099213885,0.0000922146,0.0009836209],"study_design_scores_gemma":[0.0000043268356,0.00011479448,0.9993513,0.0000109332,0.000015189121,0.00007861708,0.00017833718,0.00009102797,0.00004033398,0.000012483712,0.00010024859,0.000002482066],"about_ca_topic_score_codex":0.0019489328,"about_ca_topic_score_gemma":0.0032625704,"teacher_disagreement_score":0.004185874,"about_ca_system_score_codex":0.00025355688,"about_ca_system_score_gemma":0.00033126128,"threshold_uncertainty_score":0.022137284},"labels":[],"label_agreement":null},{"id":"W3158990279","doi":"10.6000/1929-6029.2021.10.04","title":"Determinants of Intraocular Pressure of Glaucoma Patients: A Case Study at Menelik IIReferral Hospital, Addis Ababa, Ethiopia","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intraocular pressure; Glaucoma; Medicine; Ophthalmology; Referral; Retrospective cohort study; Risk factor; Univariate analysis; Optometry; Multivariate analysis; Surgery; Internal medicine; Family medicine","score_opus":0.03305485582325138,"score_gpt":0.4181756133034644,"score_spread":0.385120757480213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158990279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99923134,0.00018355323,0.000040338986,0.00012782909,0.0000066057332,0.000020562957,0.00004244068,9.215792e-7,0.0003465206],"genre_scores_gemma":[0.99902046,0.0004022778,0.00009540285,0.000100669335,0.000016785343,0.000014102283,0.000035902533,0.0000010518992,0.00031327334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996605,0.00006964246,0.00003050618,0.000071565926,0.00005199421,0.000115828],"domain_scores_gemma":[0.9995784,0.00008364494,0.00012953416,0.00002590478,0.00004049676,0.00014196332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038025083,0.00032855888,0.00027249422,0.0011206142,0.0019696269,0.00073343655,0.00043189002,0.00059159077,0.002470339],"category_scores_gemma":[0.00096698786,0.0004560639,0.00036217878,0.00095609215,0.0004962471,0.00046815645,0.0006087634,0.00069625454,0.00017590603],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008446997,0.00044921026,0.9518108,0.00007706467,0.0000360886,0.03941407,0.0038784645,0.00003869575,0.000624315,0.00018120196,0.00043929587,0.0029663094],"study_design_scores_gemma":[0.00001713776,0.00037506226,0.9257999,0.00010277617,0.000061194776,0.05180147,0.020021973,0.00027882072,0.00027051743,0.00017058261,0.0010703928,0.000030069334],"about_ca_topic_score_codex":0.02241578,"about_ca_topic_score_gemma":0.03364043,"teacher_disagreement_score":0.02241578,"about_ca_system_score_codex":0.0010880723,"about_ca_system_score_gemma":0.0012854395,"threshold_uncertainty_score":0.044570625},"labels":[],"label_agreement":null},{"id":"W3159342866","doi":"10.6000/1929-6029.2021.10.02","title":"A Generalized Log-Weibull Distribution with Bio-Medical Applications","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Log-logistic distribution; Mathematics; Log-Cauchy distribution; Quantile function; Order statistic; Quantile; Exponentiated Weibull distribution; Statistics; Cumulative distribution function; Distribution (mathematics); Hazard; Applied mathematics; Distribution fitting; Probability density function; Inverse-chi-squared distribution; Probability distribution; Mathematical analysis","score_opus":0.11004672946010248,"score_gpt":0.5034979798115773,"score_spread":0.3934512503514748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159342866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012427149,0.0038800268,0.97279257,0.0016083966,0.00033161876,0.000052677475,0.00048117535,0.0005238588,0.007902644],"genre_scores_gemma":[0.6416506,0.011720174,0.31640735,0.0016537829,0.0014172501,0.00045780884,0.001210129,0.00035330886,0.025129626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991192,0.00028706173,0.000046602425,0.00017276932,0.0003033778,0.0000710172],"domain_scores_gemma":[0.99727064,0.0014194985,0.00037245927,0.0003059781,0.00053883815,0.00009257312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002019992,0.0008517814,0.00079792325,0.0020830382,0.0006093136,0.0017420864,0.0012585788,0.0018194043,0.004519861],"category_scores_gemma":[0.008230585,0.00031236437,0.0010438672,0.003277715,0.0015381288,0.0019700355,0.0010895676,0.0017739472,0.0017119094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012644618,0.00007517223,0.006118149,0.00053034857,0.00012334049,0.0021211647,0.000414694,0.17270768,0.0063758134,0.66843265,0.0152132865,0.12776123],"study_design_scores_gemma":[0.000023461273,0.00010341179,0.0037530824,0.00017366046,0.000049961152,0.0029293778,0.00022653762,0.3621763,0.0014114248,0.58861506,0.040435147,0.00010258933],"about_ca_topic_score_codex":0.0015163817,"about_ca_topic_score_gemma":0.000936666,"teacher_disagreement_score":0.004519861,"about_ca_system_score_codex":0.00078372064,"about_ca_system_score_gemma":0.00094119174,"threshold_uncertainty_score":0.015120387},"labels":[],"label_agreement":null},{"id":"W3166330802","doi":"10.6000/1929-6029.2021.10.07","title":"On the Probabilities of Environmental Extremes","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Environmental data; Computer science; Task (project management); Variable (mathematics); Threshold model; Statistics; Algorithm; Data mining; Mathematics; Machine learning; Engineering","score_opus":0.02957745976308168,"score_gpt":0.3401824254453295,"score_spread":0.3106049656822478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166330802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23506616,0.0019959987,0.7490163,0.0021315988,0.00019680102,0.00011042284,0.0008782097,0.00046140683,0.010143093],"genre_scores_gemma":[0.9509548,0.0018275243,0.04192083,0.000267726,0.0006460783,0.00023824013,0.00083534623,0.00016307167,0.00314636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963882,0.0016533539,0.00017090375,0.000948269,0.00055168435,0.00028753353],"domain_scores_gemma":[0.87648636,0.11366213,0.0035828343,0.0032881182,0.002057228,0.0009233754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009939463,0.0006710837,0.0014001563,0.0038142658,0.0010455039,0.0030772695,0.0021481437,0.0020720183,0.0056401915],"category_scores_gemma":[0.087658085,0.00081299426,0.0011495196,0.00267478,0.00533884,0.0055042943,0.0025389425,0.0031707922,0.00051930314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003645005,0.0000895579,0.02568474,0.00024485728,0.00022295008,0.0006235973,0.0006230581,0.55925703,0.001617189,0.36850798,0.003204237,0.039560292],"study_design_scores_gemma":[0.000044852026,0.0000817758,0.01078907,0.00012182142,0.0000536127,0.0004357684,0.00016044674,0.6966377,0.0007990002,0.28860924,0.0021885184,0.000078239435],"about_ca_topic_score_codex":0.0026643695,"about_ca_topic_score_gemma":0.0014335895,"teacher_disagreement_score":0.009939463,"about_ca_system_score_codex":0.0010662529,"about_ca_system_score_gemma":0.0006382759,"threshold_uncertainty_score":0.052565515},"labels":[],"label_agreement":null},{"id":"W3168255253","doi":"10.6000/1929-6029.2021.10.05","title":"Comparison between Mexican and International Medical Graduates’ scores in the ENARM Competing for Clinical Specialities in Mexico during 2012-2019: Data Visualization, Trends and Forecasting Analyses","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"IMG; Anesthesiology; Medicine; Test (biology); Family medicine; Demography; Psychiatry","score_opus":0.6308044789674444,"score_gpt":0.7018903191486741,"score_spread":0.07108584018122965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168255253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955641,0.000116896685,0.00013950531,0.00013759879,0.000013356713,0.000009405241,0.0033531939,0.000015111009,0.00065081613],"genre_scores_gemma":[0.9948914,0.00009240812,0.00019315239,0.00002008805,0.000020168713,0.00002279196,0.0044397926,0.000004050414,0.00031611306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948174,0.00009232064,0.00004959962,0.00012235992,0.00010909217,0.00014494671],"domain_scores_gemma":[0.9972192,0.0006237477,0.0013804979,0.0001231434,0.00038506268,0.00026837445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013971746,0.00033469664,0.00033690684,0.0020151322,0.00024997527,0.0007590471,0.00046013395,0.00036331668,0.0022728248],"category_scores_gemma":[0.0034026562,0.00016046823,0.0007105938,0.002109766,0.00023980891,0.0005012065,0.00070934684,0.00062155095,0.00022051236],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063778985,0.000022672797,0.99729806,0.000017676935,0.00005922266,0.000015662212,0.00012559888,0.00011799711,0.000046694382,0.00003572065,0.0004156022,0.0017813379],"study_design_scores_gemma":[0.0000027517087,0.000053673302,0.99813265,0.000009493555,0.00002581285,0.000020586944,0.0007040067,0.00058968697,0.000038834693,0.000013128332,0.00040597116,0.000003405144],"about_ca_topic_score_codex":0.033771288,"about_ca_topic_score_gemma":0.024276497,"teacher_disagreement_score":0.033771288,"about_ca_system_score_codex":0.0007272221,"about_ca_system_score_gemma":0.00061940815,"threshold_uncertainty_score":0.0671494},"labels":[],"label_agreement":null},{"id":"W3170257233","doi":"10.6000/1929-6029.2021.10.06","title":"Existing Approaches and Development Perspectives for Inferences","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dilemma; Simple (philosophy); Computer science; Management science; Resolution (logic); Data science; Statistical inference; Development (topology); Mathematics; Artificial intelligence; Statistics; Epistemology; Engineering","score_opus":0.33896670534320916,"score_gpt":0.48128146677752254,"score_spread":0.14231476143431337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170257233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031413925,0.028286237,0.8164457,0.033327058,0.0011625803,0.00016048022,0.000597119,0.00028340722,0.11659603],"genre_scores_gemma":[0.2645358,0.041401338,0.65315527,0.0076153437,0.0042860867,0.001205166,0.0009006554,0.00032467025,0.02657565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9872018,0.007868692,0.0006177553,0.0017958675,0.0021520897,0.00036382134],"domain_scores_gemma":[0.9732289,0.020348575,0.0011891383,0.0024988144,0.0022360513,0.00049850054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01963347,0.0015570971,0.0011544114,0.0066285296,0.0025616856,0.0086897975,0.0054563805,0.0033933339,0.015617134],"category_scores_gemma":[0.028886741,0.00070965994,0.0016633217,0.0047547957,0.011349199,0.010596863,0.0046186587,0.0054611214,0.003833789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005981364,0.000010392858,0.00011758455,0.00013011959,0.000012084185,0.000038512208,0.00024211025,0.00038977014,0.00002887431,0.98411,0.0012381383,0.013676387],"study_design_scores_gemma":[0.00000533858,0.0000083006325,0.00009046298,0.00013913565,0.000008766347,0.000050146904,0.00016559252,0.0018325245,0.000078977515,0.9756005,0.022013927,0.000006380609],"about_ca_topic_score_codex":0.0022220998,"about_ca_topic_score_gemma":0.0016037861,"teacher_disagreement_score":0.01963347,"about_ca_system_score_codex":0.004482993,"about_ca_system_score_gemma":0.0039756293,"threshold_uncertainty_score":0.10383296},"labels":[],"label_agreement":null},{"id":"W3173188981","doi":"10.6000/1929-6029.2019.08.07","title":"Troubles of Atrial Mechanical Recovery after Electrical Cardioversion in Patients with Persistent or Long-Lasting Persistent Atrial Fibrillation","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Atrial fibrillation; Medicine; Sinus rhythm; Cardiology; Internal medicine; Cardioversion; Population","score_opus":0.05657231312759222,"score_gpt":0.38510376057471274,"score_spread":0.32853144744712054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173188981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994624,0.00020834614,0.00006651317,0.000013339704,0.0000046804353,0.0000033174317,0.00006833547,0.0000018340301,0.00017115595],"genre_scores_gemma":[0.99970955,0.000046977133,0.000030833577,0.000008825951,0.000010616645,0.000004213906,0.00012582108,0.0000011124827,0.00006209799],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995839,0.00006876403,0.00005762565,0.00011350653,0.00008359643,0.000092537586],"domain_scores_gemma":[0.998114,0.0003601232,0.00091636414,0.00017330406,0.00014564967,0.0002904894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054864626,0.00026548078,0.00037043553,0.0006292822,0.00037221235,0.000609958,0.00031831895,0.0005031842,0.0012269679],"category_scores_gemma":[0.0025924684,0.00014685272,0.00041415464,0.00051958585,0.0002594455,0.0005120236,0.00041054163,0.00050289294,0.00023882138],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017789751,0.000027854376,0.9979796,0.000006109232,0.000018371926,0.00040731614,0.00004528051,0.00002025481,0.00015212927,0.000009395103,0.000021515205,0.0011343639],"study_design_scores_gemma":[0.000009398299,0.00040060745,0.9953644,0.000005353703,0.000029602845,0.0036532332,0.00014501909,0.00011434,0.00008338721,0.000024053037,0.00016569173,0.000004938897],"about_ca_topic_score_codex":0.00038729407,"about_ca_topic_score_gemma":0.0004341305,"teacher_disagreement_score":0.0012269679,"about_ca_system_score_codex":0.0001306545,"about_ca_system_score_gemma":0.0002185094,"threshold_uncertainty_score":0.0041046143},"labels":[],"label_agreement":null},{"id":"W3176721814","doi":"10.6000/1929-6029.2019.08.08","title":"Meta-Analysis of Incidence Rate Data in the Presence of Zero-Event and Single-Arm Studies","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Negative binomial distribution; Bivariate analysis; Poisson distribution; Statistics; Univariate; Binomial distribution; Continuity correction; Count data; Mathematics; Dispersion (optics); Context (archaeology); Random effects model; Event (particle physics); Meta-analysis; Econometrics; Beta-binomial distribution; Multivariate statistics; Medicine; Physics","score_opus":0.8980292824772418,"score_gpt":0.7181876361161611,"score_spread":0.1798416463610807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176721814","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07097,0.45860344,0.4509378,0.0037401516,0.0029546306,0.0034551022,0.0048336554,0.0013108068,0.0031945193],"genre_scores_gemma":[0.811799,0.038859237,0.1369796,0.0024334614,0.0008116626,0.0040695393,0.0033877622,0.00036903747,0.0012907191],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83036107,0.14201619,0.01229558,0.008422708,0.005805019,0.001099524],"domain_scores_gemma":[0.71129954,0.25829625,0.01025959,0.016312415,0.0033505268,0.00048165958],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19073117,0.0036389073,0.013093626,0.0061473665,0.000879194,0.0061788955,0.0034550384,0.0034970487,0.0035409536],"category_scores_gemma":[0.26126665,0.0019130191,0.038633328,0.004855654,0.0013612075,0.0042090137,0.0025329094,0.0041271206,0.00031893287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010356501,0.0001969509,0.02333104,0.051336452,0.80333143,0.0009956724,0.00025720333,0.04927169,0.0012950775,0.00906026,0.0021423732,0.04842537],"study_design_scores_gemma":[0.0038019347,0.0014350842,0.0084130205,0.00527357,0.88555723,0.000665571,0.00010199953,0.052934658,0.0016495096,0.030836865,0.00913463,0.00019590811],"about_ca_topic_score_codex":0.0022726345,"about_ca_topic_score_gemma":0.0030198968,"teacher_disagreement_score":0.80926883,"about_ca_system_score_codex":0.00236849,"about_ca_system_score_gemma":0.0031567689,"threshold_uncertainty_score":0.99797255},"labels":[],"label_agreement":null},{"id":"W3186374827","doi":"10.6000/1929-6029.2020.09.03","title":"Analysis of Recurrent Events with Associated Informative Censoring: Application to HIV Data","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Medicine; Proportional hazards model; Psychological intervention; Hazard ratio; Human immunodeficiency virus (HIV); Statistics; Internal medicine; Family medicine; Nursing; Mathematics","score_opus":0.22995770666512147,"score_gpt":0.549643455015053,"score_spread":0.31968574834993146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186374827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103727035,0.002062811,0.8893495,0.0010335773,0.00014361784,0.0005252166,0.0017075018,0.0008644447,0.0005861941],"genre_scores_gemma":[0.6458811,0.0012696659,0.34683165,0.00031625453,0.00028336738,0.0010961975,0.002525615,0.0004298281,0.0013663751],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98334783,0.012957086,0.0008295956,0.0014415805,0.0009851118,0.00043870264],"domain_scores_gemma":[0.87783986,0.10929707,0.0046778354,0.0054852874,0.0017902678,0.0009096283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038911875,0.0010840895,0.0017559739,0.0017814059,0.0007965451,0.00212354,0.0034640455,0.0017730489,0.003768506],"category_scores_gemma":[0.09636191,0.00062076416,0.0033636892,0.0026521701,0.0008011135,0.00158781,0.002422121,0.0034113813,0.0005042146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001682122,0.00045544788,0.25354275,0.0018436961,0.004190756,0.0032718887,0.0019246138,0.3295628,0.0014313076,0.03690578,0.0062738387,0.358915],"study_design_scores_gemma":[0.00012656236,0.0005490419,0.020990802,0.00021071339,0.0004967295,0.0008981007,0.0003293546,0.9347499,0.0006146442,0.036889955,0.00404802,0.00009616684],"about_ca_topic_score_codex":0.0052591236,"about_ca_topic_score_gemma":0.0045312657,"teacher_disagreement_score":0.038911875,"about_ca_system_score_codex":0.0008166361,"about_ca_system_score_gemma":0.001921675,"threshold_uncertainty_score":0.20578814},"labels":[],"label_agreement":null},{"id":"W3199390817","doi":"10.6000/1929-6029.2021.10.10","title":"Perception and Practice of Bangladeshi Adults Towards the Prevention of COVID-19: A Statistical Analysis","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Coronavirus disease 2019 (COVID-19); Perception; Outbreak; Demography; Risk perception; Odds ratio; Disease; Gerontology; Family medicine; Psychology; Internal medicine; Infectious disease (medical specialty); Pathology","score_opus":0.16893875461821173,"score_gpt":0.5977235714074175,"score_spread":0.42878481678920577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199390817","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977443,0.00019950572,0.0001017322,0.00011257807,0.000006440823,0.000073510404,0.0009322277,0.0000028544644,0.00082685647],"genre_scores_gemma":[0.9986797,0.00013698805,0.0001313239,0.000030269088,0.000006719504,0.00014119317,0.00054031936,0.000002023077,0.00033146128],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979956,0.0006206853,0.00035339687,0.00024888967,0.00047211524,0.00030936822],"domain_scores_gemma":[0.99408937,0.0023579195,0.0018456513,0.00021117888,0.00075911847,0.0007367146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024921242,0.00027905978,0.00058400806,0.0016348377,0.0005292151,0.000815331,0.00064929057,0.0005582806,0.0046764063],"category_scores_gemma":[0.0061091036,0.0003410955,0.0017372329,0.0022224712,0.00058715703,0.00092632265,0.0011721049,0.00092057977,0.0005149718],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020121783,0.00008951658,0.99486744,0.00010006946,0.0001276502,0.00004390688,0.0017392111,0.00004377852,0.00007798807,0.000034696717,0.00020311517,0.0024712267],"study_design_scores_gemma":[0.000011804305,0.000398742,0.9917579,0.000048686423,0.000060615163,0.00007018203,0.0067750365,0.0004587348,0.000031845193,0.000038553197,0.00033636813,0.000011530001],"about_ca_topic_score_codex":0.010004498,"about_ca_topic_score_gemma":0.00627831,"teacher_disagreement_score":0.010004498,"about_ca_system_score_codex":0.00083411444,"about_ca_system_score_gemma":0.00082236447,"threshold_uncertainty_score":0.019892514},"labels":[],"label_agreement":null},{"id":"W3200586309","doi":"10.6000/1929-6029.2021.10.09","title":"Two Level Logistic Regression Model of Factors Influencing in Early Childbearing and its Consequences on Nutritional Status of Bangladeshi Mothers: Nationally Representative Data","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adolescent Sexual and Reproductive Health","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Logistic regression; Demography; Medicine; Body mass index; Pregnancy","score_opus":0.5784059614542351,"score_gpt":0.6081578951314957,"score_spread":0.02975193367726059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200586309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9701011,0.001234935,0.014698282,0.0024125914,0.00022188091,0.00035912517,0.008329907,0.00020983156,0.002432273],"genre_scores_gemma":[0.98411524,0.00047327997,0.0051489,0.0001512478,0.00005687913,0.0006528237,0.005245021,0.000035213336,0.0041213776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9939897,0.003724032,0.00035178673,0.0010244127,0.00033381293,0.0005762942],"domain_scores_gemma":[0.99016166,0.0066661295,0.0010819077,0.0007659577,0.00095555757,0.00036885164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007591749,0.0010514761,0.001449117,0.001212462,0.00074738916,0.0016467959,0.0022453493,0.0013216415,0.010723258],"category_scores_gemma":[0.014829328,0.00082291075,0.0031969198,0.0015673402,0.0004406035,0.00096360885,0.0012297812,0.0036713611,0.0016643314],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010560935,0.0005470853,0.96357316,0.00020683432,0.0023156605,0.00078186044,0.00063394173,0.01074352,0.0002181693,0.00110647,0.005261698,0.013555442],"study_design_scores_gemma":[0.00037778768,0.0015997436,0.5769468,0.00039413577,0.002672697,0.0011357429,0.0033407104,0.40228716,0.00029836083,0.0026427396,0.008174,0.00013014892],"about_ca_topic_score_codex":0.041265037,"about_ca_topic_score_gemma":0.013448428,"teacher_disagreement_score":0.041265037,"about_ca_system_score_codex":0.0008916062,"about_ca_system_score_gemma":0.0016924725,"threshold_uncertainty_score":0.08204973},"labels":[],"label_agreement":null},{"id":"W3200958297","doi":"10.6000/1929-6029.2021.10.08","title":"Relationship between the rs333 Polymorphism in the CC Chemokine Receptor Type Five (CCR5) Gene and Immunological Disorders: Data from a Meta-Analysis","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Immunodeficiency and Autoimmune Disorders","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Odds ratio; Confidence interval; Internal medicine; Medicine; Periodontitis; Publication bias; Polymorphism (computer science); Allele; Case-control study; Immunology; Gene; Biology; Genetics","score_opus":0.2100715202159917,"score_gpt":0.43808322693489693,"score_spread":0.22801170671890522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200958297","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045332532,0.9491842,0.0020782484,0.0004543318,0.00030372496,0.00031657243,0.0018032809,0.000056320052,0.000470799],"genre_scores_gemma":[0.6797481,0.3105847,0.003770656,0.00096685777,0.0004585195,0.0010046287,0.0028436007,0.00007286674,0.00055006397],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.988521,0.0050627976,0.003430991,0.0015066265,0.0010977946,0.00038088943],"domain_scores_gemma":[0.9748244,0.01927655,0.0030886822,0.0011537898,0.0013337051,0.0003227742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014821347,0.0030327863,0.015922979,0.0067151994,0.0007346394,0.0036513382,0.0021879838,0.0021521817,0.0033618114],"category_scores_gemma":[0.025779199,0.0013980262,0.05394652,0.008795618,0.0006289384,0.0015080215,0.001457544,0.0020307377,0.0003524492],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030877627,0.000032233886,0.01643631,0.04774127,0.9270767,0.00025687838,0.000056528657,0.00072020886,0.00036706295,0.00006490444,0.0002915853,0.0038685019],"study_design_scores_gemma":[0.00031186073,0.0001296069,0.0059120776,0.001871948,0.9908057,0.00010355447,0.000025681175,0.00023507775,0.00008060563,0.00010448099,0.00040547177,0.000013901865],"about_ca_topic_score_codex":0.0039236993,"about_ca_topic_score_gemma":0.0060787234,"teacher_disagreement_score":0.015922979,"about_ca_system_score_codex":0.001527157,"about_ca_system_score_gemma":0.0017464908,"threshold_uncertainty_score":0.078383744},"labels":[],"label_agreement":null},{"id":"W3204600540","doi":"10.6000/1929-6029.2021.10.11","title":"Fixed Effects High-Dimensional Profiling Models in Low Information Context","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Profiling (computer programming); Medicaid; Inference; Payment; Medicine; Health care; Actuarial science; Computer science; Business; Artificial intelligence","score_opus":0.08479732764426232,"score_gpt":0.40035089426134596,"score_spread":0.31555356661708367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204600540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1994801,0.004186209,0.76983327,0.006178549,0.0005555632,0.0004943657,0.008476883,0.0008428947,0.009952131],"genre_scores_gemma":[0.8877351,0.0022369786,0.08405089,0.0014802597,0.0005643823,0.0008113193,0.005115719,0.00014445148,0.017860943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949882,0.002638511,0.0001804366,0.0012073038,0.00030656264,0.00067894394],"domain_scores_gemma":[0.97666055,0.017408364,0.0026890072,0.0014687306,0.0012477477,0.0005255918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071002096,0.0021768182,0.0028865067,0.0013826729,0.0009810681,0.0031761292,0.0035463637,0.0032851824,0.008092853],"category_scores_gemma":[0.023951877,0.0013290517,0.0028054134,0.002082718,0.0018555562,0.0028554206,0.0025662722,0.004627027,0.0013607843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005939077,0.0003285361,0.048094485,0.000353004,0.00088500895,0.00087036256,0.00067020435,0.7294724,0.000516626,0.16919203,0.00787263,0.041150775],"study_design_scores_gemma":[0.00009414291,0.00013788158,0.0065865214,0.000108280554,0.0002595101,0.00009659868,0.00016684628,0.9184501,0.00018166241,0.07016997,0.0036733183,0.00007512143],"about_ca_topic_score_codex":0.028031968,"about_ca_topic_score_gemma":0.02002704,"teacher_disagreement_score":0.028031968,"about_ca_system_score_codex":0.0019319436,"about_ca_system_score_gemma":0.0017623528,"threshold_uncertainty_score":0.055737615},"labels":[],"label_agreement":null},{"id":"W3208115110","doi":"10.6000/1929-6029.2021.10.13","title":"Life Expectancy and Healthy Life Expectancy of Adults in Oman: Does Women’s Longer Life Expectancy than Men Mean Success or Burden for Women?","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Health Care Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Life expectancy; Demography; Census; Medicine; Gerontology; Population","score_opus":0.07659576249181987,"score_gpt":0.5210110411254283,"score_spread":0.4444152786336084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208115110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98977077,0.004813961,0.00016530752,0.001708391,0.00005480585,0.000008424523,0.0011421262,0.0000030472427,0.002333286],"genre_scores_gemma":[0.9976757,0.001336969,0.00008125305,0.00013344224,0.00007868935,0.000009967097,0.0004272054,9.025108e-7,0.00025581275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997291,0.000079137724,0.0000254018,0.00004460102,0.000043087453,0.00007861635],"domain_scores_gemma":[0.99930143,0.0001350083,0.0003829318,0.000019291181,0.00008380555,0.00007735696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058656186,0.00019384925,0.00019922358,0.000818201,0.00033437964,0.0004772539,0.00020725986,0.00029562574,0.0022157645],"category_scores_gemma":[0.0023464877,0.00006831412,0.00024632606,0.0012551394,0.00026031895,0.0009345832,0.00037879802,0.00032420704,0.0002957749],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005983343,0.00002861606,0.9783488,0.00010260224,0.00006151934,0.00012430226,0.0011681438,0.00007399485,0.00013256562,0.00045126822,0.00091502815,0.018533273],"study_design_scores_gemma":[9.754043e-7,0.000059364385,0.99597067,0.00007379033,0.000018575078,0.00015349012,0.0014464037,0.000093351715,0.000031374708,0.00020657349,0.0019415788,0.0000038422313],"about_ca_topic_score_codex":0.0058767768,"about_ca_topic_score_gemma":0.010193104,"teacher_disagreement_score":0.0058767768,"about_ca_system_score_codex":0.00046479885,"about_ca_system_score_gemma":0.00033163634,"threshold_uncertainty_score":0.011685133},"labels":[],"label_agreement":null},{"id":"W3208891486","doi":"10.6000/1929-6029.2021.10.12","title":"Cancer Growth Inhibition Using Predictive Mathematical Models of Signaling Pathways","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multicellular organism; Signalling; Process (computing); Rivalry; Mechanism (biology); Signalling pathways; Identification (biology); Cancer cell; Computer science; Ordinary differential equation; Cell division; Cancer; Biology; Neuroscience; Signal transduction; Computational biology; Cell; Differential equation; Cell biology; Economics; Ecology; Mathematics","score_opus":0.06430780700632911,"score_gpt":0.39885860791307093,"score_spread":0.33455080090674183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208891486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13032782,0.0022459694,0.836796,0.0032349315,0.00017559416,0.000102875536,0.0009150598,0.0005027085,0.025699075],"genre_scores_gemma":[0.9617442,0.001627011,0.023174357,0.0002405944,0.00013811457,0.000271183,0.00038992352,0.00008748015,0.012327134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996495,0.0001603759,0.000012767803,0.00005347146,0.00006889089,0.000054951386],"domain_scores_gemma":[0.99773324,0.0016360823,0.00033685888,0.000055802957,0.00015373243,0.000084251355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011262703,0.0010449548,0.0010308375,0.0010851687,0.00050260156,0.0015796284,0.0014446775,0.0014888863,0.002588164],"category_scores_gemma":[0.0041986834,0.0005303209,0.0011425224,0.00092423806,0.001120189,0.0012645718,0.0009117689,0.0014347744,0.00034534506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076028673,0.00000568299,0.00018069282,0.000008269545,0.000006078971,0.000014802425,0.000009952522,0.98744375,0.00009693111,0.011409226,0.00013918654,0.0006777267],"study_design_scores_gemma":[0.0000016199614,0.000001994659,0.000024023026,0.0000015838815,0.0000020171287,0.0000020971615,0.0000019120332,0.99705064,0.000018386396,0.002819405,0.00007481877,0.0000015363838],"about_ca_topic_score_codex":0.014891779,"about_ca_topic_score_gemma":0.008576865,"teacher_disagreement_score":0.014891779,"about_ca_system_score_codex":0.002083855,"about_ca_system_score_gemma":0.0012871731,"threshold_uncertainty_score":0.029610217},"labels":[],"label_agreement":null},{"id":"W3211731737","doi":"10.6000/1929-6029.2020.09.08","title":"Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Occupational and environmental lung diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Albumin; Mesothelioma; Internal medicine; Lactate dehydrogenase; Gastroenterology; Disease; Hemoglobin; Serum albumin; Pathology; Biology; Biochemistry; Enzyme","score_opus":0.11873960004753199,"score_gpt":0.42771524328178173,"score_spread":0.30897564323424975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211731737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959559,0.001548993,0.0009229132,0.0002912357,0.000024215946,0.0000057212533,0.00038562668,0.000022496399,0.0008429644],"genre_scores_gemma":[0.99920505,0.00018552916,0.00019589711,0.000010596037,0.000014547075,0.0000033264164,0.0001383468,0.0000021942992,0.00024454796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997615,0.000060794755,0.000028296869,0.000046792942,0.000064143205,0.00003852843],"domain_scores_gemma":[0.99854016,0.00042873377,0.00063924503,0.000080124824,0.00012214246,0.00018967058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038755054,0.00022230718,0.00020523103,0.00051715143,0.00014926287,0.00040948665,0.00016525364,0.00027688316,0.0020587964],"category_scores_gemma":[0.0023827867,0.000092061724,0.00047349962,0.0005165829,0.00014220523,0.00021059251,0.00025726337,0.00046500313,0.00021067144],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020976491,0.000036488094,0.9944863,0.0000364546,0.00009584176,0.00016159398,0.000028579805,0.00042890012,0.000624895,0.000068181216,0.00016323566,0.0036597697],"study_design_scores_gemma":[0.0000043131035,0.00017439513,0.995194,0.000014766554,0.000111511275,0.00095966697,0.000073696865,0.0021491994,0.00041516405,0.00023933139,0.00065743143,0.000006400553],"about_ca_topic_score_codex":0.0011254209,"about_ca_topic_score_gemma":0.0011390715,"teacher_disagreement_score":0.0020587964,"about_ca_system_score_codex":0.00017122524,"about_ca_system_score_gemma":0.00035255437,"threshold_uncertainty_score":0.0068873763},"labels":[],"label_agreement":null},{"id":"W3212410153","doi":"10.6000/1929-6029.2021.10.14","title":"COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Adamas University","keywords":"Deep learning; Computer science; Artificial intelligence; The Internet; Identification (biology); Transfer of learning; Process (computing); Automation; Machine learning; Real-time computing; Engineering","score_opus":0.10490043289754682,"score_gpt":0.4886607468151973,"score_spread":0.3837603139176505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212410153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07759233,0.0014059081,0.8894627,0.001041556,0.00057361706,0.00047508854,0.0017003615,0.018769117,0.008979399],"genre_scores_gemma":[0.7452708,0.00056084106,0.2420107,0.0006992759,0.00012415594,0.00030250114,0.0026921656,0.00014696924,0.008192585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995894,0.000052090767,0.00002617091,0.00011137619,0.00016277845,0.000058124475],"domain_scores_gemma":[0.9996499,0.000072971234,0.000044170658,0.00004314979,0.00014643914,0.000043382464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006285303,0.00067278644,0.00050447707,0.0008557473,0.0003645263,0.00081012363,0.001212612,0.0006928979,0.0035559947],"category_scores_gemma":[0.0013160775,0.0003006092,0.0004886413,0.00045102034,0.00031417154,0.0009124224,0.0011299513,0.0009063606,0.001007237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010698222,0.00071016466,0.016833393,0.0003202804,0.00023189434,0.000654635,0.00010303565,0.18301164,0.025604919,0.005426218,0.027992334,0.73804164],"study_design_scores_gemma":[0.00003429176,0.00014217703,0.0018748494,0.000020498057,0.000020897662,0.00008411066,0.00002219431,0.983105,0.0080353925,0.0019006373,0.0047408575,0.000019028897],"about_ca_topic_score_codex":0.0061717085,"about_ca_topic_score_gemma":0.0077788904,"teacher_disagreement_score":0.0061717085,"about_ca_system_score_codex":0.00078646775,"about_ca_system_score_gemma":0.0011902698,"threshold_uncertainty_score":0.012271583},"labels":[],"label_agreement":null},{"id":"W3213203991","doi":"10.6000/1929-6029.2020.09.04","title":"Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Mathematics; Function (biology); Parametric statistics; Survival function; Statistics; Polynomial; Incidence (geometry); Survival analysis; Applied mathematics; Mathematical analysis","score_opus":0.569948542737015,"score_gpt":0.6072146982246384,"score_spread":0.037266155487623354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213203991","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020168936,0.000236725,0.99659216,0.00007550645,0.00002522366,0.00004672251,0.00010320704,0.00046232357,0.00044124035],"genre_scores_gemma":[0.11545789,0.001203454,0.87839305,0.00012552938,0.00017855619,0.0007699196,0.00070279336,0.0004157265,0.0027530014],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958103,0.002115686,0.00026541084,0.00066167343,0.0010230349,0.00012386653],"domain_scores_gemma":[0.99094033,0.006156632,0.0007095102,0.00087856385,0.0011773739,0.00013755768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006455747,0.0009933761,0.0012800406,0.0038943936,0.00039909515,0.0020590345,0.0011174175,0.0011424284,0.0038729657],"category_scores_gemma":[0.019834582,0.00044627578,0.002018348,0.0025842143,0.0010705976,0.0020114456,0.0019150067,0.0026462649,0.001427175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044083231,0.000164648,0.009264277,0.00096737023,0.00037385596,0.0003840057,0.00097628747,0.17182133,0.013287016,0.14960618,0.006422651,0.64629155],"study_design_scores_gemma":[0.00007118007,0.00047591628,0.0070289266,0.00020267628,0.00018480109,0.0014531962,0.00018941461,0.86282134,0.009033316,0.085946724,0.03237807,0.00021445316],"about_ca_topic_score_codex":0.0011794054,"about_ca_topic_score_gemma":0.0005000529,"teacher_disagreement_score":0.006455747,"about_ca_system_score_codex":0.0008563844,"about_ca_system_score_gemma":0.0013228619,"threshold_uncertainty_score":0.03414166},"labels":[],"label_agreement":null},{"id":"W3215458146","doi":"10.6000/1929-6029.2021.10.15","title":"The Normative Data for Sensorineural Acuity Level (SAL) Test among Young Adults: Comparisons Between B71 and B81 Bone Transducers","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Hearing, Cochlea, Tinnitus, Genetics","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Normative; Audiology; Test (biology); Medicine; Audiometry; Psychology; Hearing loss","score_opus":0.2748624197392679,"score_gpt":0.4868158781609489,"score_spread":0.211953458421681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215458146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942,0.00067849027,0.0016684404,0.00005094863,0.000026614525,0.00008119903,0.0011821056,0.000030345505,0.0020817164],"genre_scores_gemma":[0.99656576,0.00021804431,0.0017958558,0.000029381208,0.000007955559,0.00010621231,0.0009465986,0.0000057587995,0.00032443952],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99893063,0.00019058587,0.00025939237,0.00017077506,0.00039819095,0.00005046176],"domain_scores_gemma":[0.9971968,0.00063811423,0.0007306637,0.00020743778,0.001065355,0.0001616828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015220259,0.00034179437,0.00023567134,0.0013292517,0.00029786528,0.0004926689,0.00039724508,0.00038535378,0.0016143995],"category_scores_gemma":[0.0049001225,0.000151259,0.00022329029,0.00068229024,0.00035307725,0.000513413,0.00046455322,0.00032604762,0.0005461825],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003443765,0.0001663553,0.959577,0.0001286598,0.00005716032,0.00030569217,0.00084580947,0.00016540107,0.0033525024,0.00012875075,0.0007231105,0.034205217],"study_design_scores_gemma":[0.000010229105,0.00048256293,0.99337125,0.0000697431,0.000034146055,0.0016364182,0.001132512,0.00040287393,0.0015071926,0.00011034777,0.0012280687,0.000014606699],"about_ca_topic_score_codex":0.0031271607,"about_ca_topic_score_gemma":0.0034360117,"teacher_disagreement_score":0.0031271607,"about_ca_system_score_codex":0.00026719965,"about_ca_system_score_gemma":0.00041775397,"threshold_uncertainty_score":0.008049309},"labels":[],"label_agreement":null},{"id":"W3215930002","doi":"10.6000/1929-6029.2021.10.16","title":"A Comparison of Multiple Machine Learning Algorithms to Predict Whole-Body Vibration Exposure of Dumper Operators in Iron Ore Mines in India","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Effects of Vibration on Health","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Whole body vibration; Collinearity; Multicollinearity; Linear regression; Bayesian multivariate linear regression; Statistics; Linear model; Regression analysis; Mathematics; Algorithm; Engineering; Vibration","score_opus":0.052939169653646395,"score_gpt":0.47550953605095914,"score_spread":0.4225703663973127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215930002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79714847,0.004387595,0.19005792,0.0016674036,0.00040997416,0.00024081874,0.0007232514,0.0017863535,0.0035781816],"genre_scores_gemma":[0.96267825,0.000591204,0.034638204,0.00016742937,0.000084953936,0.00012689791,0.00083650765,0.00004174986,0.00083483726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982212,0.0008744484,0.00017687115,0.00031081346,0.00021476428,0.0002018898],"domain_scores_gemma":[0.9941549,0.004096362,0.00036122045,0.00015941943,0.0010414117,0.00018678553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005543754,0.0015540504,0.0015227173,0.0021903943,0.00051063206,0.001430658,0.0014470208,0.0011664178,0.001019343],"category_scores_gemma":[0.008992391,0.00036781863,0.0013853975,0.0011748278,0.00030701573,0.0010109093,0.0007328228,0.0014269154,0.00037927282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016347534,0.0015334432,0.11579518,0.0003779551,0.0009992628,0.00029647115,0.0002657245,0.5143478,0.0014550354,0.00082200486,0.00430416,0.35816824],"study_design_scores_gemma":[0.00002184454,0.00022730601,0.006850488,0.00002828741,0.00006269656,0.000037170048,0.00010992115,0.9918521,0.00030497013,0.00030511417,0.00018645616,0.000013676027],"about_ca_topic_score_codex":0.010722975,"about_ca_topic_score_gemma":0.0047516804,"teacher_disagreement_score":0.010722975,"about_ca_system_score_codex":0.00081360847,"about_ca_system_score_gemma":0.0015838592,"threshold_uncertainty_score":0.029318511},"labels":[],"label_agreement":null},{"id":"W3216688387","doi":"10.6000/1929-6029.2021.10.17","title":"A Pragmatic Approach for Detecting nCOVID-19 using Pervasive Computing Based on Dual Diagnostic Measures","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Adamas University","keywords":"Computer science; Medical diagnosis; Task (project management); Dual (grammatical number); Point (geometry); Architecture; Artificial intelligence; Machine learning; Risk analysis (engineering); Data science; Medicine; Systems engineering; Engineering; Mathematics","score_opus":0.16160551238309684,"score_gpt":0.5000269618821311,"score_spread":0.3384214494990343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216688387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02992929,0.00070706924,0.9543416,0.003985002,0.00020776263,0.0007836551,0.00028056235,0.0007139835,0.009051134],"genre_scores_gemma":[0.3559583,0.00030285344,0.64046466,0.0007748479,0.00015170491,0.0006181625,0.00018701763,0.000055802248,0.0014866238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9914282,0.0042225737,0.0007582385,0.001255243,0.0020362027,0.0002994293],"domain_scores_gemma":[0.9888915,0.0059591127,0.0010511815,0.0008133433,0.0027782626,0.00050655595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007990852,0.0011544714,0.0013830207,0.0064977924,0.0012406288,0.004277611,0.002314764,0.0017982218,0.0022050291],"category_scores_gemma":[0.034809716,0.0006976268,0.0011291702,0.0022149074,0.0020102847,0.0027350478,0.0036297387,0.0024487483,0.00076233887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001279746,0.000870543,0.07442842,0.0013325991,0.0005155338,0.0013769874,0.0025388922,0.014434961,0.03504228,0.12453783,0.008837956,0.7348043],"study_design_scores_gemma":[0.00033467892,0.0016261316,0.050516434,0.000719034,0.0007680302,0.005869301,0.0052459394,0.6211264,0.028932823,0.2553505,0.02890407,0.0006067556],"about_ca_topic_score_codex":0.0029284633,"about_ca_topic_score_gemma":0.0046783728,"teacher_disagreement_score":0.007990852,"about_ca_system_score_codex":0.0014639399,"about_ca_system_score_gemma":0.0026018424,"threshold_uncertainty_score":0.04226017},"labels":[],"label_agreement":null},{"id":"W4200335298","doi":"10.6000/1929-6029.2021.10.18","title":"Proprioceptive Training to Improve Static and Dynamic Balance in Elderly","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dynamic balance; Proprioception; Balance (ability); Fear of falling; Physical medicine and rehabilitation; Physical therapy; Analysis of variance; Psychology; Test (biology); Medicine; Falling (accident); Poison control; Injury prevention","score_opus":0.06605141544270594,"score_gpt":0.513279935333074,"score_spread":0.4472285198903681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200335298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99579865,0.0029978983,0.00024101898,0.000094939314,0.000046034078,0.00010616072,0.00003554057,0.000018953146,0.00066074764],"genre_scores_gemma":[0.99229586,0.003511424,0.00208874,0.00017625526,0.00009259574,0.00019086293,0.00007955934,0.0000028293089,0.0015618821],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9999461,0.000011999069,0.000005475712,0.0000095581945,0.00001178934,0.000015025037],"domain_scores_gemma":[0.9999002,0.000027838605,0.000023014767,0.0000042872625,0.000011609063,0.000033044726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019210493,0.00028563774,0.0002994253,0.00019476187,0.00014542151,0.00010626236,0.00020949921,0.00029836752,0.0028607966],"category_scores_gemma":[0.00042856843,0.000070840564,0.00020493536,0.00010924137,0.0001060422,0.000120726625,0.00017383927,0.00030925722,0.00021353178],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02958862,0.09814266,0.020813067,0.005782826,0.0005242782,0.0005919151,0.0009935977,0.0010097683,0.12430667,0.0001359482,0.0015724485,0.71653825],"study_design_scores_gemma":[0.007935869,0.3866484,0.5785597,0.00074416405,0.0006984563,0.0010683085,0.00052490016,0.0010416561,0.016237589,0.00022505576,0.0062875557,0.000028220875],"about_ca_topic_score_codex":0.0005969503,"about_ca_topic_score_gemma":0.0010235221,"teacher_disagreement_score":0.0028607966,"about_ca_system_score_codex":0.000072130904,"about_ca_system_score_gemma":0.00016201723,"threshold_uncertainty_score":0.009570301},"labels":[],"label_agreement":null},{"id":"W4200572853","doi":"10.6000/1929-6029.2021.10.19","title":"Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Public Health and Nutrition","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Padjadjaran","keywords":"Quality of life (healthcare); Medicine; Structural equation modeling; Oral health; Social determinants of health; Demography; Analysis of variance; Psychology; Environmental health; Gerontology; Developmental psychology; Public health; Family medicine; Sociology","score_opus":0.1782328407439694,"score_gpt":0.5185469254000041,"score_spread":0.34031408465603474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200572853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9897737,0.00022018219,0.0074253264,0.00039023632,0.000019795525,0.00007229986,0.0007923366,0.000042369265,0.0012637882],"genre_scores_gemma":[0.99104017,0.00018799336,0.00563118,0.000028675582,0.000010105474,0.00017021516,0.00077865587,0.000009426033,0.0021434757],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999286,0.0004471127,0.000022773178,0.00010187733,0.00003923641,0.0001030749],"domain_scores_gemma":[0.9982193,0.0013924976,0.0001450739,0.000048421673,0.00009317194,0.00010150185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016343349,0.0008468159,0.00046293554,0.00074079813,0.00035325417,0.0013673544,0.0010588594,0.0009194201,0.0037720466],"category_scores_gemma":[0.003592988,0.0005837448,0.0014613499,0.0008208628,0.0003431652,0.0006218102,0.00085190806,0.0009253654,0.00035544168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005110213,0.0011842685,0.74037343,0.0002536758,0.0011417269,0.0006077366,0.002315066,0.21422218,0.00079790066,0.010647532,0.0013818999,0.026563622],"study_design_scores_gemma":[0.00009753197,0.00056292245,0.12085032,0.00012936819,0.00046380292,0.00013538715,0.0015215055,0.8693334,0.0002785227,0.0046336944,0.0019488991,0.00004463985],"about_ca_topic_score_codex":0.038528286,"about_ca_topic_score_gemma":0.034380678,"teacher_disagreement_score":0.038528286,"about_ca_system_score_codex":0.0013011179,"about_ca_system_score_gemma":0.0023278012,"threshold_uncertainty_score":0.07660806},"labels":[],"label_agreement":null},{"id":"W4205176494","doi":"10.6000/1929-6029","title":"International Journal of Statistics in Medical Research","year":2023,"lang":"en","type":"paratext","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Epidemiology","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Medical statistics; Mathematics","score_opus":0.4555814859654151,"score_gpt":0.6693592008292135,"score_spread":0.21377771486379843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205176494","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035998928,0.23882668,0.21198817,0.14710847,0.07680505,0.0010615159,0.009884052,0.0026855222,0.30804062],"genre_scores_gemma":[0.11734286,0.32846662,0.15359105,0.036014944,0.12055689,0.0053287176,0.014227337,0.0033163559,0.22115524],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97925985,0.0085459035,0.0026054697,0.0017831807,0.0073782825,0.0004273785],"domain_scores_gemma":[0.88654137,0.07601217,0.0067446944,0.009609292,0.018711979,0.0023805692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0168203,0.0012395858,0.0024797171,0.008464484,0.0018556278,0.011362893,0.0013824471,0.0037933257,0.055262435],"category_scores_gemma":[0.0957936,0.00061762054,0.0010348132,0.011403531,0.0049143867,0.005107358,0.003878269,0.008577116,0.030040588],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084718085,0.00007083033,0.0016061635,0.001977828,0.00014100324,0.00022617941,0.00047064977,0.0011684536,0.00034489128,0.22317877,0.541365,0.22936547],"study_design_scores_gemma":[0.00002952943,0.000071547045,0.0014074282,0.0014913854,0.000044438384,0.00068826467,0.00023660963,0.0024504713,0.0001759781,0.21192732,0.78143394,0.00004310066],"about_ca_topic_score_codex":0.0012531661,"about_ca_topic_score_gemma":0.00088586315,"teacher_disagreement_score":0.055262435,"about_ca_system_score_codex":0.0030534489,"about_ca_system_score_gemma":0.010937473,"threshold_uncertainty_score":0.18487126},"labels":[],"label_agreement":null},{"id":"W4210739057","doi":"10.6000/1929-6029.2022.11.01","title":"Multiple Imputation of Missing Race and Ethnicity in CDC COVID-19 Case-Level Surveillance Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Imputation (statistics); Coronavirus disease 2019 (COVID-19); Missing data; Ethnic group; Race (biology); Statistics; Computer science; Political science; Sociology; Medicine; Mathematics; Gender studies","score_opus":0.21614269870612388,"score_gpt":0.5172899677975115,"score_spread":0.30114726909138756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210739057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26988465,0.0067410646,0.64188576,0.0058510667,0.0018881737,0.0021517605,0.061020367,0.0015608968,0.009016327],"genre_scores_gemma":[0.71035355,0.0023062439,0.23431687,0.0032307722,0.0004368002,0.003290981,0.042494256,0.00034044828,0.0032300695],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.94702595,0.034567766,0.006566262,0.005355237,0.0046485965,0.00183614],"domain_scores_gemma":[0.92616564,0.03742551,0.012311627,0.014047345,0.009439471,0.0006104172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06124321,0.0011851962,0.0024849596,0.0031501001,0.0013919272,0.0024520422,0.0031497092,0.0013923044,0.002869177],"category_scores_gemma":[0.15409361,0.0012730919,0.0031659142,0.0071698283,0.00070446276,0.0014323551,0.0022263518,0.003345574,0.0010250801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010994802,0.0004568563,0.693222,0.0018629296,0.0034893688,0.0012975115,0.0023291807,0.036297318,0.001101251,0.014935403,0.056876186,0.18703258],"study_design_scores_gemma":[0.0008052703,0.0012720277,0.39693993,0.0050669597,0.004556836,0.0021947687,0.003343602,0.38632146,0.0089653805,0.074300855,0.115755916,0.00047698242],"about_ca_topic_score_codex":0.023449022,"about_ca_topic_score_gemma":0.021819726,"teacher_disagreement_score":0.06124321,"about_ca_system_score_codex":0.0016071621,"about_ca_system_score_gemma":0.00396381,"threshold_uncertainty_score":0.3238889},"labels":[],"label_agreement":null},{"id":"W4221095909","doi":"10.6000/1929-6029.2022.11.02","title":"Inclusive Physical Activity to Promote the Participation of People with Disabilities: A Preliminary Study","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Sports and Physical Education Research","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Explosive strength; Anthropometry; Test (biology); Psychology; Physical therapy; Set (abstract data type); Inclusion (mineral); Physical activity; Physical medicine and rehabilitation; Squat; Gerontology; Medicine; Computer science; Social psychology","score_opus":0.12987938331199295,"score_gpt":0.6083670943955346,"score_spread":0.47848771108354166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221095909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99736756,0.0003186852,0.00018676327,0.000076178345,0.0000113818605,0.0010869388,0.00008863724,0.00000412724,0.0008597849],"genre_scores_gemma":[0.9895163,0.001940848,0.002879475,0.00031529728,0.000059763875,0.003461059,0.00034996268,0.0000033698373,0.001473806],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993969,0.00026320756,0.0000675192,0.00006146461,0.000094966046,0.00011585126],"domain_scores_gemma":[0.99869484,0.00050884945,0.00011161237,0.0000695886,0.00025231374,0.00036297436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024727841,0.0004976184,0.00052204117,0.00091309036,0.0009797902,0.00051663636,0.00041915732,0.0007575828,0.0024101085],"category_scores_gemma":[0.0019782211,0.00023402959,0.0007186154,0.0005700326,0.00044078837,0.00054007745,0.0008662299,0.000607053,0.00037588572],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020258736,0.2586713,0.31431153,0.005565046,0.00064817077,0.002907347,0.04897309,0.00060564233,0.018605666,0.0006918837,0.0019928864,0.3267687],"study_design_scores_gemma":[0.0027017188,0.26778367,0.69333136,0.0006882313,0.00060834514,0.0008636736,0.023143662,0.0004980012,0.0019060791,0.00028345242,0.008122625,0.00006918669],"about_ca_topic_score_codex":0.0025581876,"about_ca_topic_score_gemma":0.0043789716,"teacher_disagreement_score":0.0025581876,"about_ca_system_score_codex":0.0003538209,"about_ca_system_score_gemma":0.0014433208,"threshold_uncertainty_score":0.0130774975},"labels":[],"label_agreement":null},{"id":"W4226153269","doi":"10.6000/1929-6029.2022.11.04","title":"Factors Relating to the Expectations and Perceptions of Post-Stroke Outpatients’ in the Rehabilitation Services of Bangladesh","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Naresuan University","keywords":"Rehabilitation; Stroke (engine); Perception; Descriptive statistics; Medicine; Statistic; Physical therapy; Psychology","score_opus":0.10743847509231005,"score_gpt":0.5233721249902221,"score_spread":0.41593364989791204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226153269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99820054,0.00011384921,0.000028170707,0.00041789384,0.000004098214,0.0000070039496,0.000066541295,0.0000010897426,0.0011608322],"genre_scores_gemma":[0.9995772,0.00011784064,0.000019782916,0.000053541782,0.0000030193448,0.000005186071,0.000045305274,5.329902e-7,0.00017762768],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991278,0.0002817977,0.00013573201,0.000044205473,0.00022594824,0.00018451455],"domain_scores_gemma":[0.9969951,0.00054158544,0.0011220333,0.00003677336,0.00035669043,0.0009478287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067064125,0.00016341983,0.00020021592,0.00031844917,0.0005813099,0.0008614837,0.00019798173,0.0003015599,0.0042941123],"category_scores_gemma":[0.004474062,0.000102059574,0.00021439411,0.00037613753,0.00037663468,0.0003018933,0.0005135836,0.000545168,0.00035689663],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001988878,0.0002100099,0.97194844,0.0001280505,0.000044257966,0.0006153303,0.014194114,0.00012322978,0.00087837066,0.00014580072,0.0010166002,0.010496992],"study_design_scores_gemma":[0.00001327882,0.0003308823,0.9428285,0.00010126499,0.000018288265,0.00058456126,0.05356379,0.00023505373,0.00017144563,0.00014595207,0.001978079,0.000028893759],"about_ca_topic_score_codex":0.014522266,"about_ca_topic_score_gemma":0.01828951,"teacher_disagreement_score":0.014522266,"about_ca_system_score_codex":0.0007643496,"about_ca_system_score_gemma":0.0009111793,"threshold_uncertainty_score":0.02887547},"labels":[],"label_agreement":null},{"id":"W4226276187","doi":"10.6000/1929-6029.2022.11.03","title":"Asthma Control Level and Relating Socio-Demographic Factors in Hospital Admissions","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Asthma and respiratory diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Asthma; Medicine; Body mass index; Test (biology); Emergency department; Public health; Cross-sectional study; Pediatrics; Physical therapy; Internal medicine; Psychiatry; Nursing","score_opus":0.051132315403659645,"score_gpt":0.41923568877139616,"score_spread":0.3681033733677365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226276187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99798954,0.00037370695,0.000034169763,0.00010364688,0.000014343531,0.000012852726,0.0004082787,0.0000032214014,0.0010601877],"genre_scores_gemma":[0.99934155,0.00010582301,0.000040298946,0.000031952386,0.0000135369755,0.000008605856,0.00031081497,9.2863866e-7,0.00014639928],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999156,0.00019333964,0.00015749643,0.000097783275,0.00026514675,0.00013027745],"domain_scores_gemma":[0.9971322,0.000472849,0.0014418232,0.0000795645,0.00029855745,0.0005748735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006068167,0.00013887984,0.00021645198,0.00093434984,0.00041536705,0.00064802886,0.0002988059,0.0004258168,0.0033059132],"category_scores_gemma":[0.0037465023,0.0001396209,0.0003178092,0.0010122528,0.00026725009,0.00040401905,0.0004582231,0.0007609145,0.0005054002],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001966253,0.00002823921,0.99944526,0.0000058519277,0.0000082865945,0.000017026,0.000031781008,0.0000064119886,0.00001634522,0.0000039975134,0.00004153786,0.00037548112],"study_design_scores_gemma":[0.0000013350382,0.0000531348,0.99958116,0.000005909438,0.000004574929,0.000075838725,0.00014989368,0.00003456354,0.000013687097,0.000006158377,0.000071961695,0.0000018291349],"about_ca_topic_score_codex":0.004284909,"about_ca_topic_score_gemma":0.004231316,"teacher_disagreement_score":0.004284909,"about_ca_system_score_codex":0.0003784874,"about_ca_system_score_gemma":0.0003002709,"threshold_uncertainty_score":0.011059403},"labels":[],"label_agreement":null},{"id":"W4280593124","doi":"10.6000/1929-6029.2022.11.05","title":"The Particulars of Applying Odontoprotectors at Different Stages of Therapeutic Process of Periodontal Diseases (A Scoping Review)","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pharmacological Effects of Natural Compounds","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Alternative medicine; Dental practice; Cochrane Library; Scientific literature; Periodontal disease; Process (computing); MEDLINE; Traditional medicine; Dentistry; Management science; Intensive care medicine; Engineering ethics; Computer science; Political science; Engineering; Pathology","score_opus":0.16105192072050142,"score_gpt":0.5641154259411241,"score_spread":0.4030635052206226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280593124","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013023806,0.99196714,0.0011343175,0.0012633913,0.0005955015,0.0011754195,0.00036854108,0.00001031785,0.002183003],"genre_scores_gemma":[0.014039698,0.97544235,0.005204013,0.0011348022,0.00027205373,0.002745186,0.00035505753,0.000010943333,0.00079595885],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9826759,0.005959093,0.0070338896,0.0011803376,0.002715906,0.00043487136],"domain_scores_gemma":[0.95309454,0.034353424,0.0056337216,0.0014134231,0.00519917,0.0003058077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02747956,0.0014905838,0.004686193,0.03091163,0.0016838858,0.007156894,0.0019608766,0.004011737,0.0042536096],"category_scores_gemma":[0.061926242,0.0010251186,0.005912215,0.02366109,0.0022628412,0.006310222,0.0029013364,0.0014463513,0.00066480483],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018638781,0.00006596817,0.001199276,0.72939944,0.0022128138,0.00030799955,0.0016579637,0.00025032045,0.0009748074,0.0061335354,0.0038364716,0.25377497],"study_design_scores_gemma":[0.00005244954,0.00016730349,0.0033408406,0.88282907,0.0085991,0.00060250255,0.0017470646,0.00011857881,0.0007377332,0.00339261,0.09836483,0.0000479541],"about_ca_topic_score_codex":0.0062265806,"about_ca_topic_score_gemma":0.015850782,"teacher_disagreement_score":0.03091163,"about_ca_system_score_codex":0.0047820504,"about_ca_system_score_gemma":0.02189802,"threshold_uncertainty_score":0.14532751},"labels":[],"label_agreement":null},{"id":"W4294733482","doi":"10.6000/1929-6029.2022.11.06","title":"A Data Driven Study on the Variant of Covid-19 in Hong Kong","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; Wuhan University of Science and Technology; Wuhan University","keywords":"Mainland China; Coronavirus disease 2019 (COVID-19); Econometrics; China; Time series; Population; Series (stratigraphy); Mainland; Statistics; Measure (data warehouse); Computer science; Demography; Geography; Data mining; Mathematics; Medicine; Sociology","score_opus":0.6737385028220841,"score_gpt":0.6312301611378479,"score_spread":0.04250834168423623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294733482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967437,0.00011534871,0.0012881886,0.0003179769,0.000017261884,0.000023265407,0.0010022272,0.000013463623,0.00047857765],"genre_scores_gemma":[0.9972766,0.00007961361,0.00063064956,0.000041079005,0.000009151553,0.00001670556,0.0010920413,0.0000050977956,0.00084910163],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989981,0.00051655143,0.00005379669,0.00019158665,0.000087007764,0.00015293628],"domain_scores_gemma":[0.993877,0.0037816658,0.0007319465,0.0005133937,0.00078074884,0.00031526294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029130464,0.00037074165,0.0003925066,0.00061727123,0.0004237381,0.0007874074,0.000860432,0.000572243,0.001327514],"category_scores_gemma":[0.0071492842,0.00025014023,0.00064966106,0.00083231024,0.00052774645,0.0006424066,0.0005248067,0.0009246877,0.00019936534],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003113905,0.00043142584,0.8759205,0.00012817625,0.0003174796,0.0016219672,0.0009909858,0.093509614,0.00092861464,0.013609711,0.0041223597,0.008107845],"study_design_scores_gemma":[0.000040826748,0.00026939154,0.4009092,0.000042522995,0.00012371031,0.00023841104,0.0018347445,0.59141594,0.0006799424,0.001974019,0.0024032816,0.00006801314],"about_ca_topic_score_codex":0.15423319,"about_ca_topic_score_gemma":0.086114675,"teacher_disagreement_score":0.15423319,"about_ca_system_score_codex":0.0019962415,"about_ca_system_score_gemma":0.0009737697,"threshold_uncertainty_score":0.3066709},"labels":[],"label_agreement":null},{"id":"W4296348253","doi":"10.6000/1929-6029.2022.11.09","title":"Role of Predictive Modeling in Healthcare Research: A Scoping Review","year":2022,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Predictive analytics; Probabilistic logic; Complement (music); Empirical research; Predictive value; Predictive modelling; Regression analysis; Quality (philosophy); Linear regression; Statistical model; Health care; Machine learning; Econometrics; Artificial intelligence; Statistics; Mathematics; Medicine","score_opus":0.8506510121827494,"score_gpt":0.6879277199178135,"score_spread":0.16272329226493587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296348253","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065544424,0.9972236,0.0007705008,0.0012517996,0.00016741587,0.000025238583,0.000034759163,0.000007167577,0.00045409007],"genre_scores_gemma":[0.0013523196,0.99678016,0.0011148124,0.00041868765,0.00014815069,0.00005795725,0.000049111935,0.000005817014,0.00007292793],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.988649,0.005309321,0.0025906898,0.0009372564,0.0022634724,0.00025021273],"domain_scores_gemma":[0.85501266,0.13065746,0.004532851,0.002130327,0.0071581784,0.0005084968],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.025153672,0.0015711744,0.0039502652,0.015045329,0.0010097831,0.005833533,0.0023329207,0.0037696874,0.004203511],"category_scores_gemma":[0.08244309,0.0012164806,0.0039541996,0.01759978,0.0022797869,0.0064278557,0.0029249291,0.0043263887,0.0010731064],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008009869,0.000060207913,0.0006145657,0.2475371,0.00089458143,0.00016574531,0.0006604441,0.0012955215,0.00027297976,0.024452874,0.012691034,0.71127486],"study_design_scores_gemma":[0.000020994672,0.00007456935,0.0009824561,0.6714959,0.002186254,0.0005039779,0.0004884227,0.0007198151,0.00025857735,0.017538283,0.3056696,0.00006119674],"about_ca_topic_score_codex":0.00597774,"about_ca_topic_score_gemma":0.008404139,"teacher_disagreement_score":0.9748463,"about_ca_system_score_codex":0.0044779098,"about_ca_system_score_gemma":0.015952384,"threshold_uncertainty_score":0.1330269},"labels":[],"label_agreement":null},{"id":"W4296369300","doi":"10.6000/1929-6029.2022.11.08","title":"The Role of Mobile Applications in the Doctor’s Working Time Management System","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Duration (music); Descriptive statistics; Health care; Population; Working time; Medical education; Descriptive research; Medicine; Family medicine; Mobile technology; Psychology; Mobile device; Computer science; Work (physics); Statistics; Engineering; Environmental health","score_opus":0.06145060174926856,"score_gpt":0.5244279880433821,"score_spread":0.46297738629411356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296369300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9334094,0.005795001,0.018704917,0.0037148343,0.00026699467,0.00028098567,0.00044726135,0.0007913968,0.036589287],"genre_scores_gemma":[0.9938544,0.0005142153,0.0040831403,0.00008561171,0.00006738293,0.000040394472,0.00008020586,0.00001568378,0.0012589797],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984573,0.0008819941,0.0001215467,0.0001432465,0.0002593785,0.00013654919],"domain_scores_gemma":[0.9899811,0.0059399735,0.0014376348,0.00037520312,0.0013088097,0.00095724844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024525684,0.00019870207,0.0002376534,0.001454103,0.00062339695,0.002214621,0.00052727835,0.00037685517,0.0020645545],"category_scores_gemma":[0.010800501,0.00012466645,0.00024188159,0.001127159,0.00035566336,0.001021655,0.00084124925,0.00032659734,0.00056226115],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009490573,0.0004625292,0.28258318,0.0006070041,0.00014429948,0.00053975964,0.007895924,0.002833281,0.00547814,0.0045499955,0.0067797718,0.68717706],"study_design_scores_gemma":[0.0001248584,0.002126695,0.8127568,0.0010102893,0.0005257627,0.0013715596,0.0136855375,0.046701297,0.007477439,0.0053708716,0.10866404,0.0001848791],"about_ca_topic_score_codex":0.003055958,"about_ca_topic_score_gemma":0.0014396728,"teacher_disagreement_score":0.003055958,"about_ca_system_score_codex":0.0010218449,"about_ca_system_score_gemma":0.0012534139,"threshold_uncertainty_score":0.012970567},"labels":[],"label_agreement":null},{"id":"W4296370445","doi":"10.6000/1929-6029.2022.11.10","title":"Evaluation of Frequency and Type of Severe Anemia in Patients Referred to the Baqiyatallah Hospital in Tehran in Six Months; A Descriptive Cross-Sectional Study","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Hemoglobinopathies and Related Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Anemia; Hemoglobin; Gastroenterology; Aplastic anemia; Internal medicine; Pediatrics; Bone marrow","score_opus":0.071022629183727,"score_gpt":0.4443562998634236,"score_spread":0.37333367067969664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296370445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9996086,0.00015630639,0.000016137246,0.000025212285,0.0000032674434,0.0000060255643,0.000063424915,0.0000010142418,0.00012006643],"genre_scores_gemma":[0.999619,0.00011359618,0.00005349543,0.000032389056,0.000010856095,0.000006897606,0.00010299603,4.1547614e-7,0.000060471608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996624,0.000070858136,0.00005187485,0.000057813428,0.000090997535,0.00006599744],"domain_scores_gemma":[0.99914503,0.00010368478,0.0004891381,0.000021583577,0.0000895561,0.00015093501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046062522,0.00019667055,0.00032350834,0.00091486506,0.00049855444,0.0003437282,0.00026426153,0.00040008652,0.0009190969],"category_scores_gemma":[0.0010585095,0.00029602824,0.00025867787,0.00083451835,0.0002702098,0.00043430662,0.00027550178,0.00035943615,0.000111395304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027328371,0.00003665575,0.9991892,0.000010056173,0.000011191532,0.00010549594,0.00012922466,0.0000061667743,0.000070758506,0.0000037892924,0.000042226453,0.0003679411],"study_design_scores_gemma":[0.0000034415325,0.00016172035,0.99844295,0.000006117177,0.000010542032,0.0006718001,0.0005519499,0.000045866796,0.000022646393,0.000004283812,0.000075911026,0.0000027710591],"about_ca_topic_score_codex":0.003906773,"about_ca_topic_score_gemma":0.006456218,"teacher_disagreement_score":0.003906773,"about_ca_system_score_codex":0.00029452718,"about_ca_system_score_gemma":0.0003262373,"threshold_uncertainty_score":0.0077680945},"labels":[],"label_agreement":null},{"id":"W4296370928","doi":"10.6000/1929-6029.2022.11.07","title":"Analysis of Statistical Knowledge of Peruvian Medical Students: A Cross-Sectional Analytical Study Based on a Survey","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Educational Research and Science Teaching","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biostatistics; Medicine; Cross-sectional study; Epidemiology; Family medicine; Internship; Statistical analysis; Medical education; Demography; Statistics; Internal medicine; Pathology; Mathematics","score_opus":0.152364190273056,"score_gpt":0.5455534905410916,"score_spread":0.39318930026803556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296370928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989489,0.0001150509,0.00013872667,0.00005986611,0.0000021780063,0.00004807153,0.00022017286,0.0000026892453,0.0004644515],"genre_scores_gemma":[0.99914277,0.00011394809,0.00019551722,0.00004869079,0.0000057401844,0.000080345555,0.00027181796,0.0000011539204,0.0001399399],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99832207,0.00075482327,0.00019560197,0.00017403654,0.00037671064,0.00017669599],"domain_scores_gemma":[0.9940432,0.0022392431,0.0021057925,0.0003049832,0.0008829283,0.00042378498],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0027140134,0.00018906605,0.00035730447,0.0020904576,0.00037602603,0.00061733916,0.00035951394,0.0004996972,0.0018170167],"category_scores_gemma":[0.0070734704,0.0003013076,0.00054882676,0.0016148253,0.0004119845,0.000722721,0.0007595573,0.0004063431,0.0002537547],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003594272,0.000092024595,0.9976267,0.000032068823,0.0000316457,0.000044147648,0.00056922686,0.000014651027,0.000075899625,0.000010964575,0.000056011322,0.0014106601],"study_design_scores_gemma":[0.00000340894,0.00025765455,0.99811965,0.00001420381,0.0000129087375,0.00009979033,0.0011917257,0.000109255016,0.000031645555,0.000009333796,0.00014766929,0.0000027359022],"about_ca_topic_score_codex":0.0033656815,"about_ca_topic_score_gemma":0.0024223006,"teacher_disagreement_score":0.99728596,"about_ca_system_score_codex":0.0003615723,"about_ca_system_score_gemma":0.0005511947,"threshold_uncertainty_score":0.014353216},"labels":[],"label_agreement":null},{"id":"W4296371025","doi":"10.6000/1929-6029.2022.11.11","title":"Comprehensive Evaluation of Reference Values of Parametric and Non-Parametric Effect Size Methods for Two Independent Groups","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Parametric statistics; Skewness; Mathematics; Statistics; Parametric model; Measure (data warehouse); Nonparametric statistics; Value (mathematics); Significant difference; Computer science; Data mining","score_opus":0.6426058132913987,"score_gpt":0.7105112017097936,"score_spread":0.067905388418395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296371025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022216447,0.032321688,0.9224444,0.001838618,0.0013977787,0.0020156559,0.0010245657,0.0010368363,0.015704],"genre_scores_gemma":[0.25981295,0.0050807823,0.7238621,0.0009769257,0.00035717743,0.0065988265,0.0011168659,0.00062604813,0.001568317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8094028,0.1341327,0.0132089965,0.010414233,0.031765904,0.0010753616],"domain_scores_gemma":[0.45622328,0.46792755,0.014770692,0.025357153,0.034706395,0.0010149598],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17060189,0.0020112484,0.0026853879,0.0069284244,0.0013922732,0.0035248653,0.003927686,0.0034960716,0.004660035],"category_scores_gemma":[0.51194066,0.000729569,0.003251329,0.0045770872,0.0032695106,0.0032134005,0.0031609791,0.0037604352,0.001077614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024755585,0.00047690814,0.02722005,0.012495817,0.0030154607,0.00086595665,0.0046689804,0.02225343,0.005505857,0.17116328,0.01758408,0.7322747],"study_design_scores_gemma":[0.0021530767,0.0071470644,0.066179365,0.023932317,0.0069141793,0.005466688,0.0042912434,0.17717066,0.046507876,0.4067602,0.2522545,0.0012228795],"about_ca_topic_score_codex":0.0014904538,"about_ca_topic_score_gemma":0.0012461263,"teacher_disagreement_score":0.8293981,"about_ca_system_score_codex":0.002530678,"about_ca_system_score_gemma":0.0044980743,"threshold_uncertainty_score":0.9022398},"labels":[],"label_agreement":null},{"id":"W4306663448","doi":"10.6000/1929-6029.2022.11.12","title":"Application of Semi-Markov Process For Model Incremental Change in HIV Staging with Cost Effect","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Human immunodeficiency virus (HIV); Hazard ratio; Markov chain; Statistics; Mathematics; Average cost; Markov model; Parametric statistics; Viral load; Parametric model; Medicine; Confidence interval; Virology","score_opus":0.07217380598418492,"score_gpt":0.5034997697899131,"score_spread":0.43132596380572813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306663448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046390865,0.0012057329,0.9431504,0.0011817678,0.00022298947,0.00031925138,0.0010254723,0.00046997142,0.006033462],"genre_scores_gemma":[0.9122803,0.0016080189,0.07264526,0.00030947276,0.00013944201,0.001081832,0.0009404553,0.00009525856,0.010900035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877423,0.0005480487,0.00006723381,0.0002219464,0.00019997353,0.00018848672],"domain_scores_gemma":[0.9959751,0.0031777672,0.00031441747,0.00008071098,0.0003653413,0.00008669254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039001817,0.0012639896,0.0015530718,0.0014432563,0.0006288886,0.0016886218,0.0020734821,0.0020876683,0.0066596484],"category_scores_gemma":[0.00620724,0.0008294102,0.0023154113,0.0012824709,0.0009150686,0.0013149738,0.0011655393,0.0021928719,0.00056073104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000331966,0.00002409686,0.0011304606,0.000044724362,0.000040308376,0.00008078225,0.000041002884,0.9795139,0.00013366179,0.015697386,0.0002309733,0.0030295127],"study_design_scores_gemma":[0.000007415276,0.000015785226,0.000113186674,0.0000075488265,0.000016824148,0.000013835352,0.000007322835,0.9942825,0.000047347257,0.005205289,0.00027715313,0.0000058401106],"about_ca_topic_score_codex":0.03342587,"about_ca_topic_score_gemma":0.01841326,"teacher_disagreement_score":0.03342587,"about_ca_system_score_codex":0.0025157614,"about_ca_system_score_gemma":0.0029092957,"threshold_uncertainty_score":0.066462636},"labels":[],"label_agreement":null},{"id":"W4307267011","doi":"10.6000/1929-6029.2022.11.13","title":"Cox Proportional Hazard Regression Interaction Model and Its Application to Determine The Risk of Death in Breast Cancer Patients after Chemotherapy","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Public Health and Nutrition","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Chemotherapy; Proportional hazards model; Breast cancer; Hazard ratio; Internal medicine; Oncology; Cancer; Survival analysis; Confidence interval","score_opus":0.046029131561326854,"score_gpt":0.46167971137886527,"score_spread":0.4156505798175384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307267011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55833846,0.0059768236,0.41453677,0.00470809,0.0012825307,0.0018481677,0.006334917,0.0016442144,0.0053299894],"genre_scores_gemma":[0.94075066,0.001033203,0.04859961,0.00027287457,0.0004777185,0.0015838403,0.0026534528,0.00012503621,0.0045035435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98982596,0.006933532,0.00042656713,0.0011608131,0.00092501455,0.0007281207],"domain_scores_gemma":[0.96015465,0.03440567,0.0020941042,0.0011496791,0.0016018951,0.0005939883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01600151,0.001269526,0.0017746177,0.002498555,0.000825672,0.0015685658,0.0027498507,0.0011604511,0.008480194],"category_scores_gemma":[0.028334508,0.0006221419,0.0043652556,0.001665679,0.0004997248,0.0010490577,0.0017378499,0.0035648153,0.00074987224],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055416855,0.001332705,0.7396092,0.0012992049,0.006355889,0.0019900764,0.0010611024,0.09661617,0.0008626409,0.013945594,0.01049457,0.12089111],"study_design_scores_gemma":[0.00040168472,0.0022687237,0.08418464,0.00018270807,0.0025873168,0.0011083364,0.000650832,0.8899796,0.00070200925,0.009467402,0.008324446,0.00014229331],"about_ca_topic_score_codex":0.008286056,"about_ca_topic_score_gemma":0.00452444,"teacher_disagreement_score":0.01600151,"about_ca_system_score_codex":0.0013623805,"about_ca_system_score_gemma":0.0029099816,"threshold_uncertainty_score":0.084625065},"labels":[],"label_agreement":null},{"id":"W4309287728","doi":"10.6000/1929-6029.2022.11.16","title":"The Use of Putative Dialysis Initiation Time in Comparative Outcomes of Patients with Advanced Chronic Kidney Disease: Methodological Aspects","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Dialysis; Medicine; Kidney disease; Intensive care medicine; Quality of life (healthcare); Renal function; Disease; Cohort; Cohort study; Retrospective cohort study; Internal medicine; Nursing","score_opus":0.15724644391712606,"score_gpt":0.47725205166738954,"score_spread":0.3200056077502635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309287728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2678364,0.013903492,0.6913277,0.008052033,0.0016147094,0.008465033,0.0040853694,0.00022391089,0.0044913595],"genre_scores_gemma":[0.78809464,0.0011049917,0.19156156,0.001760574,0.00044727474,0.014669401,0.0014261997,0.000056102457,0.0008792647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.6014939,0.35405847,0.020999288,0.012478803,0.00984072,0.0011288603],"domain_scores_gemma":[0.5004406,0.40733114,0.045097627,0.037554875,0.007921389,0.001654463],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2761079,0.0010862268,0.0020938993,0.0033773251,0.001775139,0.003355475,0.005341763,0.002219243,0.0014493142],"category_scores_gemma":[0.4863929,0.00070069305,0.00732099,0.005729603,0.0039049159,0.0024673033,0.0038624553,0.0032709346,0.00020972906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060406886,0.0007950909,0.75272363,0.004540825,0.016887281,0.0006995365,0.006155364,0.018211285,0.0011039713,0.076654084,0.003485722,0.11270262],"study_design_scores_gemma":[0.0037291895,0.013968134,0.4817858,0.0051109125,0.020293606,0.0026151605,0.005851018,0.21517503,0.01016704,0.19238058,0.048114646,0.0008088612],"about_ca_topic_score_codex":0.006881968,"about_ca_topic_score_gemma":0.003587052,"teacher_disagreement_score":0.7238921,"about_ca_system_score_codex":0.0022826642,"about_ca_system_score_gemma":0.0030999768,"threshold_uncertainty_score":0.8926878},"labels":[],"label_agreement":null},{"id":"W4309730445","doi":"10.6000/1929-6029.2022.11.14","title":"Predictive Power of a Body Shape Index (ABSI) for Diabetes Mellitus and Arterial Hypertension in Peru: Demographic and Health Survey Analysis - 2020","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health and Lifestyle Studies","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Body Shape Index; Anthropometry; Waist; Body mass index; Medicine; Internal medicine; Diabetes mellitus; Confidence interval; Demography; Population; Waist-to-height ratio; Roundness (object); Obesity; Endocrinology; Mathematics; Classification of obesity; Environmental health","score_opus":0.1105609709034281,"score_gpt":0.5109040306664073,"score_spread":0.40034305976297924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309730445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963905,0.00035329149,0.00026052713,0.00011516958,0.0000054502248,0.000012744834,0.0018941158,0.000013965475,0.0009541995],"genre_scores_gemma":[0.9979043,0.00011028224,0.0002233845,0.000019726202,0.000007876396,0.000012958824,0.001570357,0.0000023202208,0.00014880586],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99954504,0.00023102247,0.000030981082,0.00006573756,0.00008601106,0.000041176212],"domain_scores_gemma":[0.99824846,0.00057214795,0.00066897285,0.00011022363,0.00024006754,0.00016011602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015281447,0.00020975545,0.00019794701,0.0011833231,0.0001766801,0.00045233587,0.00023211405,0.00027012354,0.0013804804],"category_scores_gemma":[0.0031322127,0.0001464081,0.0005293329,0.0011074218,0.00019808256,0.00024577172,0.00055250287,0.00031670553,0.0002600436],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032950367,0.000006516391,0.99919444,0.0000044441995,0.000028929206,0.000008009039,0.000021367217,0.000041698397,0.000027763086,0.00000644671,0.000051158597,0.0005763801],"study_design_scores_gemma":[0.000001935378,0.000042943873,0.9993812,0.0000035328994,0.000019309435,0.000036387268,0.00006736191,0.000295879,0.000016923004,0.0000132257455,0.00012008777,0.0000013473493],"about_ca_topic_score_codex":0.008371882,"about_ca_topic_score_gemma":0.006202074,"teacher_disagreement_score":0.008371882,"about_ca_system_score_codex":0.00021336958,"about_ca_system_score_gemma":0.00024143711,"threshold_uncertainty_score":0.016646326},"labels":[],"label_agreement":null},{"id":"W4309730503","doi":"10.6000/1929-6029.2022.11.15","title":"Hearing Loss due to Noise Exposure and its Relationship with Hypertension in Peruvian Workers","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hearing loss; Medicine; Poisson regression; Audiology; Cross-sectional study; Noise-induced hearing loss; Regression analysis; Noise exposure; Demography; Environmental health; Population; Statistics","score_opus":0.1337239811360293,"score_gpt":0.49325196583165515,"score_spread":0.35952798469562586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309730503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99864525,0.0007491782,0.000069184876,0.00007749794,0.0000034523819,0.0000044252797,0.000103948376,0.000003329261,0.00034385585],"genre_scores_gemma":[0.99939036,0.0003221917,0.000054564538,0.000019575798,0.000012510576,0.0000047800477,0.00008990211,8.471188e-7,0.00010522345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99957496,0.00014951684,0.00003731037,0.00006564931,0.000105267885,0.000067387766],"domain_scores_gemma":[0.9988193,0.00030984863,0.0005594605,0.000058077978,0.00014244388,0.00011090176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060835475,0.00018025542,0.00022924252,0.0007435392,0.00032811565,0.00043675117,0.00026433007,0.00032412392,0.0016419338],"category_scores_gemma":[0.0019502651,0.00019495602,0.0002643006,0.0008853678,0.00028036436,0.00019837022,0.00049054046,0.00029368934,0.00015499095],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040517243,0.000024774261,0.99795747,0.000021733988,0.000036420213,0.00010432861,0.0002074375,0.0000100169445,0.00021206531,0.0000059767267,0.000033403958,0.0013457759],"study_design_scores_gemma":[7.7200076e-7,0.000049624894,0.9995247,0.000005448978,0.000013357492,0.00013252489,0.00015735455,0.000028773899,0.000016686376,0.0000060841567,0.000063648506,9.922219e-7],"about_ca_topic_score_codex":0.008903141,"about_ca_topic_score_gemma":0.008445336,"teacher_disagreement_score":0.008903141,"about_ca_system_score_codex":0.00017823884,"about_ca_system_score_gemma":0.00023582464,"threshold_uncertainty_score":0.01770264},"labels":[],"label_agreement":null},{"id":"W4309833329","doi":"10.6000/1929-6029.2022.11.17","title":"An Analysis of the Survival of Gall Bladder Patients in a Tertiary Cancer Center in India using Accelerated Failure Time Models","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bladder cancer; Gall; Medicine; Hazard ratio; Proportional hazards model; Tertiary care; Survival analysis; Internal medicine; Surgery; Statistics; Oncology; Cancer; Confidence interval; Mathematics; Biology","score_opus":0.13658342799901663,"score_gpt":0.501599652675196,"score_spread":0.3650162246761794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309833329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93983203,0.00083068863,0.05467779,0.0012425104,0.00007083082,0.00015935415,0.0013709931,0.00012540972,0.001690354],"genre_scores_gemma":[0.9903964,0.00027195134,0.0076702284,0.000045371064,0.000033413296,0.00008122022,0.0007238013,0.00001618542,0.00076140586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9967403,0.0021074933,0.0001349468,0.00044500624,0.0002467873,0.00032547946],"domain_scores_gemma":[0.97994745,0.016632615,0.0017988177,0.00060586445,0.0006009219,0.00041437426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008927693,0.00068855035,0.0008254471,0.0016744265,0.0003520469,0.0012132342,0.001218372,0.0006636543,0.0021324514],"category_scores_gemma":[0.014639925,0.00034105906,0.002032622,0.0013932018,0.00036684284,0.0006741606,0.0011037325,0.0013356411,0.00023118376],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017213494,0.00052085426,0.3851415,0.00055108505,0.0013125312,0.0008693721,0.0007962609,0.53643847,0.00088865036,0.013484528,0.0033187126,0.054956753],"study_design_scores_gemma":[0.00005320204,0.00071661966,0.049971808,0.00007175645,0.00024353058,0.0003306395,0.0003086261,0.94172347,0.00032642708,0.004796744,0.0014182023,0.000038894756],"about_ca_topic_score_codex":0.008929098,"about_ca_topic_score_gemma":0.0049395533,"teacher_disagreement_score":0.008929098,"about_ca_system_score_codex":0.0012877402,"about_ca_system_score_gemma":0.0017256235,"threshold_uncertainty_score":0.047214687},"labels":[],"label_agreement":null},{"id":"W4310130539","doi":"10.6000/1929-6029.2022.11.18","title":"Treatment Patterns of Tocilizumab Utilization for Progressive Respiratory Distress during the COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tocilizumab; Medicine; Coronavirus disease 2019 (COVID-19); Pandemic; Respiratory distress; Internal medicine; Dosing; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Respiratory system; Retrospective cohort study; Surgery","score_opus":0.28965689292642005,"score_gpt":0.5926851694888762,"score_spread":0.3030282765624561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310130539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988726,0.00018760642,0.00015380439,0.000067007924,0.0000035375865,0.000012561153,0.00024133289,0.000004014533,0.00045763253],"genre_scores_gemma":[0.99948466,0.000077969795,0.00008565316,0.000019713325,0.0000047223893,0.0000071734503,0.0002629371,0.0000021184828,0.00005490424],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983949,0.0005355146,0.0003276115,0.0003033433,0.00021982129,0.00021872907],"domain_scores_gemma":[0.9948573,0.0009864267,0.0031391536,0.00025379308,0.00046128422,0.0003019921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010803841,0.00015311995,0.00023915726,0.0013700878,0.00036937653,0.0006180106,0.00035584497,0.0003618409,0.0013962275],"category_scores_gemma":[0.0051265885,0.00017275069,0.0003192462,0.0011743322,0.00033897217,0.00042126075,0.000477039,0.0003969264,0.00021111824],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009129388,0.000015539086,0.9977977,0.0000066183793,0.000018354816,0.000121297046,0.00012266413,0.00006482657,0.00017297972,0.00002632066,0.00009310426,0.0014692636],"study_design_scores_gemma":[0.000003869759,0.00015654681,0.99693406,0.000012533645,0.000012216011,0.00094068423,0.0008430322,0.00050229835,0.00025299,0.00003200172,0.00030197212,0.000007756635],"about_ca_topic_score_codex":0.004651594,"about_ca_topic_score_gemma":0.0043827984,"teacher_disagreement_score":0.004651594,"about_ca_system_score_codex":0.0006352576,"about_ca_system_score_gemma":0.00061331724,"threshold_uncertainty_score":0.009249091},"labels":[],"label_agreement":null},{"id":"W4313257594","doi":"10.6000/1929-6029.2022.11.19","title":"Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"AstraZeneca; Pfizer","keywords":"Vaccination; Coronavirus disease 2019 (COVID-19); Medicine; Pandemic; Christian ministry; Family medicine; Disease; Demography; Infectious disease (medical specialty); Immunology; Internal medicine","score_opus":0.11747609424394279,"score_gpt":0.4969864402856257,"score_spread":0.37951034604168293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313257594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997873,0.000034158034,0.000013342539,0.00003620029,0.0000011980694,0.0000055018972,0.000007733263,3.484234e-7,0.000114241564],"genre_scores_gemma":[0.99973696,0.00007195932,0.000047284426,0.000022793734,0.0000020066052,0.0000071921822,0.000011635229,3.354148e-7,0.00009990124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987451,0.00049432943,0.00014871181,0.000071228234,0.00029781775,0.00024285649],"domain_scores_gemma":[0.9964585,0.0008583739,0.0016191731,0.0000853357,0.000540572,0.00043802915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002035155,0.00024460172,0.00028980835,0.0008541087,0.0010946722,0.0009110052,0.00037276206,0.0005043751,0.0014582651],"category_scores_gemma":[0.0060830894,0.00023644873,0.00034871243,0.00053746,0.0007282215,0.0004915325,0.0006271821,0.0006355537,0.00016540119],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055308603,0.00010913883,0.9782864,0.00005058693,0.0000152421035,0.00042300773,0.0163024,0.000024968966,0.00046825927,0.00004578039,0.000088709276,0.0041301847],"study_design_scores_gemma":[0.0000034900188,0.00036589563,0.91481864,0.00006298648,0.000020615898,0.00071971415,0.08275897,0.00023957583,0.000319247,0.000061653984,0.0006098723,0.000019476047],"about_ca_topic_score_codex":0.007185701,"about_ca_topic_score_gemma":0.00840253,"teacher_disagreement_score":0.007185701,"about_ca_system_score_codex":0.00078446785,"about_ca_system_score_gemma":0.0010293963,"threshold_uncertainty_score":0.01428777},"labels":[],"label_agreement":null},{"id":"W4313470711","doi":"10.6000/1929-6029.2022.11.20","title":"Development and Validation of a Virtual Moving Auditory Localization (vMAL) Test among Healthy Children","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Kappa; Test (biology); Reliability (semiconductor); Audiology; Convergent validity; Cohen's kappa; Correlation; Spearman's rank correlation coefficient; Psychology; Statistics; Computer science; Mathematics; Psychometrics; Internal consistency; Medicine","score_opus":0.055303990909357434,"score_gpt":0.47738199408563897,"score_spread":0.4220780031762815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313470711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959876,0.00011770222,0.0024897761,0.00003358069,0.000017956458,0.00018975526,0.00016746449,0.000040381157,0.0009559131],"genre_scores_gemma":[0.9829238,0.00015938684,0.015328325,0.000029462803,0.000009448984,0.00046103995,0.0005550863,0.000010329533,0.000523121],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974686,0.0007493316,0.0005057308,0.00040707437,0.0007048125,0.00016451968],"domain_scores_gemma":[0.9951375,0.001708305,0.0009035946,0.0002734508,0.0016528383,0.00032430256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043524606,0.00072798313,0.00043240204,0.0013170055,0.00034706428,0.0007611963,0.000932111,0.0007598541,0.0011537919],"category_scores_gemma":[0.008005213,0.00036595424,0.0006861158,0.00043019545,0.0005977856,0.0009789696,0.0011981427,0.0005545687,0.0005148876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054097583,0.0007757258,0.92174304,0.00024883592,0.00005488808,0.0005291978,0.0018719754,0.0010096388,0.009544034,0.00026464128,0.0006171966,0.06279993],"study_design_scores_gemma":[0.000090902504,0.0051146345,0.9725752,0.00014679381,0.00007858385,0.0025277706,0.0018465993,0.0057522384,0.009933055,0.0001953878,0.0016829377,0.00005581248],"about_ca_topic_score_codex":0.001502066,"about_ca_topic_score_gemma":0.0014733571,"teacher_disagreement_score":0.0043524606,"about_ca_system_score_codex":0.0003442611,"about_ca_system_score_gemma":0.00092634157,"threshold_uncertainty_score":0.0230183},"labels":[],"label_agreement":null},{"id":"W4313470775","doi":"10.6000/1929-6029.2022.11.22","title":"Vaccination, Compliance with Preventive Measures and Mental Health during COVID-19 among Adults in Bangladesh: Do Vaccination and Compliance with Preventive Measures Improve Mental Health?","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Anxiety; Vaccination; Mental health; Depression (economics); Logistic regression; Pandemic; Compliance (psychology); Descriptive statistics; Environmental health; Psychiatry; Coronavirus disease 2019 (COVID-19); Disease; Psychology; Infectious disease (medical specialty); Internal medicine","score_opus":0.08581745978630752,"score_gpt":0.48636090976825336,"score_spread":0.40054344998194585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313470775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99625456,0.0013601938,0.000031289244,0.0008890377,0.000011111005,0.000017150702,0.00048337108,0.0000019761762,0.00095116714],"genre_scores_gemma":[0.99931264,0.00041205075,0.000024924673,0.000063262625,0.000005676861,0.000006901532,0.00010037145,3.4419932e-7,0.000073793824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993793,0.00018999676,0.000109307904,0.00008260544,0.00010390391,0.00013482735],"domain_scores_gemma":[0.99815875,0.00025478273,0.0010348468,0.00005355961,0.00014942067,0.000348588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094123214,0.00018585307,0.00023986152,0.0004119698,0.0003448302,0.00043054766,0.00025707652,0.0005168385,0.0019468988],"category_scores_gemma":[0.0037517475,0.00020929317,0.00036575753,0.00078800455,0.00031855787,0.00045961575,0.00037038344,0.0006322547,0.0001768328],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003225854,0.00003448222,0.9966703,0.00004055782,0.000029959443,0.00003322058,0.00027104834,0.0000140046905,0.000044566237,0.000019297522,0.00011159195,0.0026987107],"study_design_scores_gemma":[0.0000025318818,0.000047621998,0.99935025,0.000036197995,0.000011173297,0.000027325217,0.0003562207,0.000040337785,0.00000973901,0.000011205844,0.000105302446,0.0000021219464],"about_ca_topic_score_codex":0.036431037,"about_ca_topic_score_gemma":0.041091375,"teacher_disagreement_score":0.036431037,"about_ca_system_score_codex":0.0006997409,"about_ca_system_score_gemma":0.0007969573,"threshold_uncertainty_score":0.072438},"labels":[],"label_agreement":null},{"id":"W4313578976","doi":"10.6000/1929-6029.2022.11.23","title":"Evaluation and Comparison of Plasma miRNA-31 in Oral Squamous Cell Carcinoma","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"microRNA; Basal cell; Biomarker; Medicine; Carcinogenesis; Cancer; Squamous cell cancer; Internal medicine; Oncology; Gastroenterology; Cancer research; Gene; Biology","score_opus":0.05824283976127754,"score_gpt":0.4391926883894862,"score_spread":0.3809498486282087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313578976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99769163,0.00122526,0.00046219758,0.000020300244,0.00001220642,0.000019741548,0.00015777591,0.000010484708,0.0004003282],"genre_scores_gemma":[0.9985991,0.0002441646,0.00063687185,0.000017127062,0.000010442452,0.000024404038,0.00018230542,0.0000033881117,0.00028230468],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99965155,0.00008624077,0.000035311386,0.00008983397,0.00010751841,0.000029568468],"domain_scores_gemma":[0.999566,0.0001276017,0.00013679863,0.000026110618,0.000088563545,0.000054894892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005848408,0.00021195163,0.00034467882,0.0006274493,0.00025482074,0.0004906008,0.00013203619,0.00035101085,0.0015710373],"category_scores_gemma":[0.0010411965,0.0002004543,0.00019498539,0.00044053825,0.00022747152,0.00018472272,0.00023475612,0.0002810905,0.0003615006],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0074144606,0.00040660007,0.76053333,0.00033211216,0.00022438278,0.0010060761,0.00041060333,0.00029205793,0.20127249,0.00014468821,0.00030997364,0.027653206],"study_design_scores_gemma":[0.000075448486,0.003087284,0.932237,0.0000276193,0.00022522653,0.004095032,0.00042274842,0.0013894866,0.056076236,0.00022592914,0.0021188734,0.00001916007],"about_ca_topic_score_codex":0.00021875204,"about_ca_topic_score_gemma":0.00018951202,"teacher_disagreement_score":0.0015710373,"about_ca_system_score_codex":0.0001804814,"about_ca_system_score_gemma":0.00015653126,"threshold_uncertainty_score":0.0052556396},"labels":[],"label_agreement":null},{"id":"W4313582824","doi":"10.6000/1929-6029.2022.11.21","title":"Are the Normative Values of Sensorineural Acuity Level (SAL) Test Affected by Head Circumferences of Subjects?","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Normative; Audiology; Medicine; Test (biology); Psychology","score_opus":0.1112529782082808,"score_gpt":0.4575116741566568,"score_spread":0.346258695948376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313582824","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97576284,0.004268979,0.0123149445,0.00057687913,0.00015164074,0.00006535329,0.0018883958,0.00015049624,0.0048204125],"genre_scores_gemma":[0.99633443,0.0003740644,0.002056752,0.00010469033,0.00004750904,0.00006320053,0.0006575213,0.0000147448545,0.00034709217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997719,0.0006374439,0.000364326,0.000555849,0.00062240625,0.00010095479],"domain_scores_gemma":[0.99076957,0.0039803684,0.0033296018,0.0006991886,0.0010070126,0.0002142408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026528174,0.00032309318,0.00034274164,0.0008603455,0.00020248283,0.000584052,0.0006445518,0.000457756,0.0023205292],"category_scores_gemma":[0.012409758,0.00013400204,0.00024276601,0.00071346655,0.0008624306,0.00064229703,0.00036725224,0.00035129825,0.00054078264],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062175817,0.000053522617,0.93696433,0.00020521073,0.00011615147,0.00043625623,0.00044040635,0.0003249791,0.0043307575,0.00040110797,0.0010913112,0.055014204],"study_design_scores_gemma":[0.0000075028624,0.0002740062,0.9926783,0.00005764026,0.00005109489,0.0012597537,0.00034165577,0.000980585,0.0025444264,0.00054498145,0.0012412447,0.000018808378],"about_ca_topic_score_codex":0.0016575828,"about_ca_topic_score_gemma":0.0011036904,"teacher_disagreement_score":0.0026528174,"about_ca_system_score_codex":0.00024981252,"about_ca_system_score_gemma":0.0002984958,"threshold_uncertainty_score":0.014029622},"labels":[],"label_agreement":null},{"id":"W4319750128","doi":"10.6000/1929-6029.2022.11.24","title":"Assessment of the Benefits and Effectiveness of Information Systems for Drug Use as an Effort to Improve Pharmaceutical Services","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Information system; Pharmacy; Drug; Service (business); Medicine; Descriptive statistics; Computer science; Business; Family medicine; Marketing; Pharmacology; Engineering; Statistics","score_opus":0.12280591753217382,"score_gpt":0.5411906386806019,"score_spread":0.41838472114842806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319750128","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98010796,0.0009697878,0.0019837401,0.00152866,0.000051391722,0.0005788521,0.00018139726,0.000066732326,0.014531434],"genre_scores_gemma":[0.9932167,0.0004951078,0.0055637727,0.00010208059,0.00005399113,0.0001516988,0.00008675307,0.000005009611,0.00032487026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97503227,0.013558131,0.0020809346,0.00039608325,0.008211228,0.0007213697],"domain_scores_gemma":[0.8694459,0.10012568,0.013709396,0.0029823391,0.010714139,0.003022595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020874808,0.00036684668,0.00035821853,0.0038781567,0.00065697863,0.0018242734,0.00040377222,0.0006217447,0.0017940702],"category_scores_gemma":[0.070041135,0.00019181079,0.000893309,0.002165626,0.00069418474,0.0023159392,0.0011052807,0.00071065984,0.00019647242],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012780115,0.0046740184,0.58330816,0.0018876331,0.00056996336,0.00023226379,0.0036150957,0.0021844795,0.002548278,0.0022878612,0.0019714793,0.39544272],"study_design_scores_gemma":[0.0001404939,0.007740922,0.97199506,0.0008153694,0.0005425045,0.00025510127,0.0050767236,0.004435753,0.0024866355,0.0006033162,0.0058537032,0.000054411663],"about_ca_topic_score_codex":0.0008849928,"about_ca_topic_score_gemma":0.0015778067,"teacher_disagreement_score":0.020874808,"about_ca_system_score_codex":0.0011313785,"about_ca_system_score_gemma":0.002142248,"threshold_uncertainty_score":0.110397875},"labels":[],"label_agreement":null},{"id":"W4320918469","doi":"10.6000/1929-6029.2023.12.01","title":"The Effect of Health Literacy Level on the Use of E-Health Applications","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Johannesburg; Covenant University; National Aeronautics and Space Administration","keywords":"Health literacy; Public health; Test (biology); Government (linguistics); Multinomial logistic regression; Logistic regression; Literacy; Psychology; Medicine; Demography; Gerontology; Medical education; Statistics; Nursing; Sociology; Political science; Mathematics; Health care","score_opus":0.37743220437509645,"score_gpt":0.6548803557689635,"score_spread":0.2774481513938671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320918469","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976261,0.0002445656,0.00007416378,0.00016198597,0.00000683332,0.000009012356,0.00013157127,0.000003847836,0.0017418953],"genre_scores_gemma":[0.999529,0.000058094305,0.00004224585,0.000028663537,0.0000056538065,0.000004171385,0.0000695392,0.0000011190637,0.00026151605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998749,0.00040781277,0.0001663208,0.000121174984,0.0002954342,0.00026027532],"domain_scores_gemma":[0.99061334,0.0039246036,0.003393795,0.00020177194,0.00083202956,0.0010343597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092337374,0.00017623726,0.00019148845,0.0007059708,0.00022867692,0.0008497593,0.0002251105,0.00037933647,0.0042149634],"category_scores_gemma":[0.0089703845,0.00014509614,0.00044976882,0.00056954107,0.00029229687,0.0004794983,0.00055366213,0.00071288785,0.00051643467],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006407264,0.00014052837,0.9957308,0.000018556104,0.000028779836,0.00004730164,0.00015751059,0.000026758711,0.000098868964,0.00001713858,0.00008298515,0.0035867386],"study_design_scores_gemma":[0.000001944683,0.00014450653,0.9991003,0.000014786915,0.000016670894,0.000089374465,0.00027316523,0.00012912399,0.00006360394,0.000016277974,0.00014771982,0.0000025984198],"about_ca_topic_score_codex":0.002508524,"about_ca_topic_score_gemma":0.0025952996,"teacher_disagreement_score":0.0042149634,"about_ca_system_score_codex":0.00024838917,"about_ca_system_score_gemma":0.00040430858,"threshold_uncertainty_score":0.014100492},"labels":[],"label_agreement":null},{"id":"W4323545656","doi":"10.6000/1929-6029.2023.12.02","title":"Comparison of Some Prediction Models and their Relevance in the Clinical Research","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Predictive modelling; Logistic regression; Artificial intelligence; Machine learning; Feature selection; Relevance (law); Artificial neural network; Feature (linguistics); Selection (genetic algorithm); Data mining","score_opus":0.7795987724141072,"score_gpt":0.7470700299392866,"score_spread":0.03252874247482063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323545656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12290658,0.17614517,0.6217184,0.023426725,0.0025893706,0.00058133487,0.005885076,0.002099057,0.044648293],"genre_scores_gemma":[0.7430101,0.07161794,0.17307086,0.0017539643,0.002138104,0.0006545497,0.004479482,0.00032528635,0.0029496667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924528,0.003559189,0.0006839282,0.0010269926,0.002024857,0.00025221595],"domain_scores_gemma":[0.97040313,0.023156611,0.00095232495,0.0008978,0.0042632404,0.000326992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014141149,0.0013180652,0.0011977833,0.0052978727,0.0005841823,0.003651803,0.0016053452,0.0014206425,0.0026209909],"category_scores_gemma":[0.045329012,0.0002981616,0.0017265149,0.004355975,0.0006182053,0.0031451418,0.00095883425,0.0019862952,0.0008730278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010980152,0.0002610105,0.045199387,0.0024598609,0.0010815376,0.00030014227,0.0004179532,0.10234368,0.00051588286,0.055724252,0.01441344,0.7761848],"study_design_scores_gemma":[0.00018878446,0.001000782,0.037009537,0.0028384074,0.0013831629,0.0008486798,0.001008622,0.79687184,0.0018584264,0.110685565,0.04608149,0.00022457633],"about_ca_topic_score_codex":0.007007175,"about_ca_topic_score_gemma":0.0034968092,"teacher_disagreement_score":0.014141149,"about_ca_system_score_codex":0.0021164264,"about_ca_system_score_gemma":0.0019300451,"threshold_uncertainty_score":0.074786425},"labels":[],"label_agreement":null},{"id":"W4323662701","doi":"10.6000/1929-6029.2023.12.03","title":"Bayesian Formulation of Time-Dependent Carrier-Borne Epidemic Model with a Single Carrier","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Grants Commission","keywords":"Bayesian probability; Epidemic model; Statistics; Maximum likelihood; Computer science; Estimation; Econometrics; Coronavirus disease 2019 (COVID-19); Mathematics; Demography; Medicine; Engineering","score_opus":0.2861329278383517,"score_gpt":0.5255579087218576,"score_spread":0.2394249808835059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323662701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022381905,0.0005851356,0.97367144,0.00069211447,0.000052115472,0.000051482675,0.00038134298,0.00008890566,0.002095601],"genre_scores_gemma":[0.7310835,0.0041364473,0.2442441,0.00059208885,0.00050219963,0.0006808194,0.0017904013,0.00014326331,0.016827192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857044,0.00064615195,0.00006594861,0.00029679126,0.00027644233,0.00014425286],"domain_scores_gemma":[0.99660814,0.0023265039,0.00038612477,0.00015343698,0.00039702427,0.0001288186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00459377,0.0008261714,0.0016202102,0.0013080675,0.000492725,0.0015889338,0.0032520508,0.002086161,0.0033124338],"category_scores_gemma":[0.011169702,0.00077449117,0.0010579656,0.0014685721,0.0011518729,0.0031530994,0.0011517407,0.0019661675,0.0005822246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008412965,0.00005055234,0.0031747126,0.00014965213,0.00011198694,0.00043238452,0.00032573417,0.65790117,0.001313908,0.31891054,0.0018644618,0.015680818],"study_design_scores_gemma":[0.000021857726,0.000029824841,0.000542809,0.000018973089,0.00003467726,0.00011906909,0.000027215354,0.9409347,0.00011961505,0.05717229,0.0009531715,0.000025763],"about_ca_topic_score_codex":0.013056104,"about_ca_topic_score_gemma":0.0077261524,"teacher_disagreement_score":0.013056104,"about_ca_system_score_codex":0.0010906331,"about_ca_system_score_gemma":0.0015417439,"threshold_uncertainty_score":0.025960207},"labels":[],"label_agreement":null},{"id":"W4361293870","doi":"10.6000/1929-6029.2023.12.04","title":"Risk Factors for COVID-19: A Quantitative Study Conducted at Padang City Center Hospital","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Comorbidity; Anxiety; Depression (economics); Medicine; Psychological intervention; Coronavirus disease 2019 (COVID-19); Descriptive statistics; Population; Test (biology); Distancing; Demography; Family medicine; Psychology; Psychiatry; Environmental health; Internal medicine; Disease; Statistics","score_opus":0.26489754412120264,"score_gpt":0.4939740048417183,"score_spread":0.22907646072051563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361293870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99945456,0.000037417325,0.0000418507,0.00006526587,0.0000016153829,0.000017046792,0.00006858513,0.0000011914119,0.00031236868],"genre_scores_gemma":[0.99945396,0.0001076258,0.00008780778,0.000046668574,0.0000030415106,0.000036239784,0.00006903841,9.251849e-7,0.00019471461],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99932015,0.00025127069,0.00007485217,0.000065579385,0.00014547315,0.0001427494],"domain_scores_gemma":[0.99810934,0.000566649,0.0006599738,0.000056753295,0.00025083462,0.0003563874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010406371,0.00015572258,0.00022161979,0.0009958483,0.0009841402,0.0006981402,0.0002920005,0.00027853771,0.0019464616],"category_scores_gemma":[0.002527779,0.00025830523,0.0001474454,0.00089271594,0.00058865873,0.0006996315,0.00083396985,0.0003996906,0.0002128577],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037615242,0.00013814779,0.97189486,0.00009043146,0.000008317547,0.0003340466,0.023016103,0.00001847232,0.0006309242,0.000041744534,0.0002828171,0.0035066106],"study_design_scores_gemma":[0.000003008156,0.00024139721,0.89033884,0.000056112374,0.000007350844,0.00045975452,0.107813835,0.0001235791,0.00015797117,0.000015640264,0.0007677359,0.000014781182],"about_ca_topic_score_codex":0.006055332,"about_ca_topic_score_gemma":0.007271515,"teacher_disagreement_score":0.006055332,"about_ca_system_score_codex":0.00064848666,"about_ca_system_score_gemma":0.000912695,"threshold_uncertainty_score":0.012040138},"labels":[],"label_agreement":null},{"id":"W4367849871","doi":"10.6000/1929-6029.2023.12.05","title":"Socio-Psychological Factors in the Development of Emotional Intelligence of Drug Addicts","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Psychology of Development and Education","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Emotional intelligence; Addiction; Psychology; Drug addict; Descriptive statistics; Clinical psychology; Social support; Test (biology); Scale (ratio); Developmental psychology; Psychiatry; Social psychology","score_opus":0.23024371041343666,"score_gpt":0.5511835132865903,"score_spread":0.32093980287315366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367849871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987043,0.00028852417,0.000039651215,0.00007148288,0.000004320481,0.0000066519374,0.00003119754,0.0000019508172,0.000851926],"genre_scores_gemma":[0.9996983,0.0001123562,0.00004110495,0.000010069639,0.0000037983013,0.000002534211,0.000026582464,5.0674646e-7,0.00010479634],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997125,0.000089541354,0.000039919316,0.000026926606,0.00009114437,0.000039891154],"domain_scores_gemma":[0.9990804,0.00022338261,0.0003567122,0.000038680973,0.000098225886,0.00020249664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032992463,0.00018241825,0.00020455488,0.0009351243,0.00037694752,0.0006171383,0.00012725047,0.00022665513,0.0017667271],"category_scores_gemma":[0.0022918722,0.000116234245,0.0003485986,0.0005450077,0.00031979574,0.0001945169,0.0004380669,0.00049415533,0.0001271589],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038162278,0.00011998111,0.99455994,0.0000150974665,0.00003448355,0.00016423904,0.00041584513,0.000028386881,0.00022377679,0.000058330257,0.000045609275,0.004296189],"study_design_scores_gemma":[5.488078e-7,0.00003201314,0.9995122,0.000005347551,0.0000075157245,0.000081989754,0.00018901569,0.000048326605,0.000021509504,0.00002503632,0.00007502588,0.0000014725875],"about_ca_topic_score_codex":0.0017896906,"about_ca_topic_score_gemma":0.0020374234,"teacher_disagreement_score":0.0017896906,"about_ca_system_score_codex":0.00021433382,"about_ca_system_score_gemma":0.00018624974,"threshold_uncertainty_score":0.005910337},"labels":[],"label_agreement":null},{"id":"W4376877081","doi":"10.6000/1929-6029.2023.12.06","title":"Obstructive Sleep Apnea Syndrome Associated with Atrial Fibrillation in Adult Patients: A Systematic Review and Meta-Analysis","year":2023,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstructive sleep apnea; Atrial fibrillation; Medicine; Internal medicine; Cardiology; Sleep apnea; Meta-analysis; Apnea; Continuous positive airway pressure; Observational study","score_opus":0.11303192087362406,"score_gpt":0.4632620003369028,"score_spread":0.3502300794632787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376877081","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005359486,0.9930676,0.00028616615,0.00015115892,0.000099982964,0.00023972797,0.0005745337,0.000015601188,0.0002057136],"genre_scores_gemma":[0.12780072,0.86769444,0.0014127378,0.00052328856,0.00020845137,0.0011673297,0.000897781,0.000015074124,0.00028020894],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99456304,0.0019748453,0.0019421695,0.0006083602,0.00068154786,0.00023011115],"domain_scores_gemma":[0.98662555,0.009839137,0.0020526126,0.00030603324,0.0010079598,0.0001687052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073270327,0.0019308921,0.014221302,0.0055843564,0.0006750208,0.002479125,0.0016282791,0.0017225787,0.0035925373],"category_scores_gemma":[0.020103777,0.0010776119,0.024195336,0.0072774766,0.0005024689,0.0013239476,0.0011390575,0.0012810878,0.00022262259],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001213021,0.000036737423,0.0093158,0.5874719,0.38566276,0.00021118358,0.00010564835,0.00043142668,0.00019809861,0.000128185,0.0009049412,0.014320287],"study_design_scores_gemma":[0.0004191349,0.00019626913,0.008678158,0.049696766,0.9381538,0.00019051597,0.00006695531,0.00022206148,0.00010865308,0.00015905306,0.002080471,0.000028254195],"about_ca_topic_score_codex":0.00651543,"about_ca_topic_score_gemma":0.015714716,"teacher_disagreement_score":0.014221302,"about_ca_system_score_codex":0.002003459,"about_ca_system_score_gemma":0.0037375165,"threshold_uncertainty_score":0.038749516},"labels":[],"label_agreement":null},{"id":"W4378806686","doi":"10.6000/1929-6029.2023.12.07","title":"Impact of Machine Learning and Prediction Models in the Diagnosis of Oral Health Conditions","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine learning; Computer science; Bootstrapping (finance); Artificial intelligence; Predictive modelling; Oral health; Task (project management); Calibration; Data mining; Medicine; Statistics; Econometrics; Mathematics; Dentistry","score_opus":0.12921113449719054,"score_gpt":0.5502154857784424,"score_spread":0.4210043512812518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378806686","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10387562,0.6739144,0.15236667,0.03558479,0.0037732136,0.0005690128,0.009606392,0.0007317178,0.019578181],"genre_scores_gemma":[0.7298196,0.19679525,0.060932536,0.0020493343,0.0026550114,0.00040408218,0.0059688333,0.00008767109,0.0012877197],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9872416,0.0076509416,0.0011711954,0.0010379342,0.002662931,0.00023532973],"domain_scores_gemma":[0.8882456,0.1025061,0.0029804637,0.0014112522,0.0044358666,0.00042076272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025230438,0.0011192322,0.0021216332,0.00621851,0.00047067113,0.0039581005,0.0012086689,0.0012524561,0.0029428923],"category_scores_gemma":[0.085492395,0.00031977502,0.0029324454,0.0063712937,0.00071224675,0.003258103,0.0014481747,0.0021695173,0.0007570526],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008896726,0.00042388425,0.1449406,0.017953902,0.0041347765,0.0004471985,0.00043682125,0.060558952,0.00052464963,0.015582317,0.016652688,0.73745453],"study_design_scores_gemma":[0.00033720434,0.0020813695,0.13598852,0.036920957,0.009444805,0.0016331669,0.0016521848,0.585765,0.0037378052,0.13061224,0.0913561,0.00047063918],"about_ca_topic_score_codex":0.0060415147,"about_ca_topic_score_gemma":0.003810993,"teacher_disagreement_score":0.025230438,"about_ca_system_score_codex":0.001568159,"about_ca_system_score_gemma":0.0027678288,"threshold_uncertainty_score":0.13343292},"labels":[],"label_agreement":null},{"id":"W4378981332","doi":"10.6000/1929-6029.2023.12.08","title":"Factors Associated with Knowledge, Attitudes, and Practices about Tuberculosis in Peruvians","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bivariate analysis; Family history; Poisson regression; Tuberculosis; Univariate; Population; Epidemiology; Medicine; Demography; Disease; Environmental health; Family medicine; Multivariate statistics; Statistics; Mathematics; Surgery; Internal medicine; Pathology; Sociology","score_opus":0.16473316841858596,"score_gpt":0.5341927783606469,"score_spread":0.36945960994206095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378981332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983621,0.0005918555,0.00004029867,0.00020220137,0.000002530346,0.00001014867,0.00009296265,0.0000029196965,0.00069497013],"genre_scores_gemma":[0.9993932,0.00030088128,0.000057282814,0.000031594773,0.0000089787845,0.000008184973,0.00008079882,7.919689e-7,0.0001183129],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992543,0.00035334352,0.00006697449,0.00007058323,0.00015645407,0.00009837142],"domain_scores_gemma":[0.99723345,0.0006358963,0.0015568811,0.00010264798,0.0002358508,0.00023532954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087320706,0.00022903366,0.0003358784,0.0008346798,0.0004210494,0.000703199,0.00020332188,0.0005135865,0.0022292167],"category_scores_gemma":[0.007028156,0.00024198976,0.00029172417,0.00091306854,0.00038035362,0.00040118664,0.0004676259,0.00046586033,0.00018530419],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023718958,0.00006847349,0.99692047,0.000032031025,0.000027563374,0.00012286806,0.00063853024,0.000017609396,0.00012513869,0.000016249882,0.000062025705,0.0019454386],"study_design_scores_gemma":[0.0000023736682,0.00010660655,0.9985083,0.000026352795,0.000015403042,0.00023346893,0.0007812306,0.000068252266,0.000016670854,0.000019649513,0.000219154,0.0000026796051],"about_ca_topic_score_codex":0.0062938854,"about_ca_topic_score_gemma":0.0053276625,"teacher_disagreement_score":0.0062938854,"about_ca_system_score_codex":0.00020869297,"about_ca_system_score_gemma":0.0003683083,"threshold_uncertainty_score":0.012514532},"labels":[],"label_agreement":null},{"id":"W4383908165","doi":"10.6000/1929-6029.2023.12.10","title":"Elevated Lactate as a Mortality Factor in Poly Traumatised Patients: A Systematic Review and Meta-Analysis","year":2023,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Medicine; Confidence interval; Odds ratio; Internal medicine; Web of science; Diagnostic odds ratio","score_opus":0.3923231998856578,"score_gpt":0.5783748095803002,"score_spread":0.1860516096946424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383908165","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00409927,0.994651,0.00025546853,0.00019805002,0.00010036907,0.0001748806,0.0003608856,0.000014635576,0.00014537819],"genre_scores_gemma":[0.13168274,0.8634636,0.0014475393,0.0009213183,0.00031984472,0.001052542,0.0008088376,0.000015808271,0.0002877786],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9929848,0.002932452,0.0020336625,0.0008152053,0.0009465285,0.00028743703],"domain_scores_gemma":[0.98269343,0.012545156,0.0029627755,0.00041031826,0.0011715495,0.00021674485],"candidate_categories":["metaepi_narrow","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.010154228,0.0020212962,0.015429782,0.004965109,0.0005896096,0.003046017,0.0018500788,0.0021084745,0.003128127],"category_scores_gemma":[0.025322506,0.0010058916,0.028209612,0.0065513053,0.0006567404,0.0015344066,0.0011943744,0.0015588929,0.00021784601],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002047715,0.00004513294,0.0094414065,0.5607097,0.4122899,0.00019110861,0.00009451446,0.0005491358,0.0001566224,0.0001379441,0.0009820092,0.0133548165],"study_design_scores_gemma":[0.0006061972,0.00032025747,0.008622656,0.063842826,0.9233952,0.00016513372,0.000067628964,0.00031348504,0.00012234431,0.00023516075,0.002271269,0.00003774799],"about_ca_topic_score_codex":0.006884641,"about_ca_topic_score_gemma":0.013428464,"teacher_disagreement_score":0.9979787,"about_ca_system_score_codex":0.002378934,"about_ca_system_score_gemma":0.003999393,"threshold_uncertainty_score":0.05370134},"labels":[],"label_agreement":null},{"id":"W4383908199","doi":"10.6000/1929-6029.2023.12.09","title":"Application of Dengue Hemorrhagic Fever Information System (SI-DBD) for Recording and Reporting of DHF Suspects at Kota Public Health Centers in Bantaeng Regency","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Dengue hemorrhagic fever; Medicine; Disease control; Significant difference; Environmental health; Medical emergency; Dengue virus; Internal medicine; Immunology","score_opus":0.19540063963453203,"score_gpt":0.5540598985691434,"score_spread":0.35865925893461137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383908199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99685127,0.00007689358,0.0003815227,0.00021018497,0.000011301788,0.00088763505,0.00020590743,0.000037881295,0.0013375257],"genre_scores_gemma":[0.9907714,0.00030903268,0.0056008724,0.00020087729,0.000026254276,0.00091428147,0.00031367387,0.000005110752,0.0018584457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979538,0.0011931696,0.0001371162,0.00019252025,0.00032592483,0.00019743094],"domain_scores_gemma":[0.9948744,0.002500293,0.00087695714,0.00022816527,0.00063388544,0.00088631926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031812298,0.00026977478,0.00018491904,0.000657864,0.000838134,0.0003802503,0.0007999784,0.00030854685,0.004108284],"category_scores_gemma":[0.006058978,0.0002696254,0.0002637033,0.00037907998,0.00046466966,0.00047419794,0.0008944085,0.0005145805,0.0005750977],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015160423,0.012285097,0.6726587,0.0016819609,0.0000828179,0.0014920402,0.022788154,0.0005372538,0.02342969,0.00018038774,0.0029712263,0.26037663],"study_design_scores_gemma":[0.00040776646,0.01811106,0.94898885,0.00024126969,0.0001122432,0.00068896514,0.013579695,0.0013301503,0.010821958,0.00006060439,0.0055971197,0.000060285383],"about_ca_topic_score_codex":0.007052574,"about_ca_topic_score_gemma":0.011036598,"teacher_disagreement_score":0.007052574,"about_ca_system_score_codex":0.0009644162,"about_ca_system_score_gemma":0.0022452285,"threshold_uncertainty_score":0.016824186},"labels":[],"label_agreement":null},{"id":"W4386219393","doi":"10.6000/1929-6029.2023.12.11","title":"Effect of Vaccination Status on SARS-CoV-2 Antibody Levels in Gowa Regency Community, Indonesia","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Vaccination; Immune system; Antibody; Immunology; Logistic regression; Medicine; Immune status; Multivariate analysis; Internal medicine","score_opus":0.13050876236752024,"score_gpt":0.5399048799597481,"score_spread":0.4093961175922279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386219393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994436,0.000083659266,0.000010823842,0.00004462972,0.000003626179,0.0000035541268,0.00006413761,9.873257e-7,0.0003450873],"genre_scores_gemma":[0.9997342,0.00004689365,0.000019916006,0.000015521655,0.0000023544092,0.0000042915904,0.00005118413,5.174067e-7,0.00012498857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99966884,0.00012562268,0.00003456087,0.00005431446,0.0000371595,0.00007953273],"domain_scores_gemma":[0.9993261,0.00016846498,0.00020656358,0.000027388292,0.00006312757,0.0002083827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005203419,0.00021146919,0.00022146052,0.00033426232,0.0003048088,0.00045423862,0.00026669443,0.0002583998,0.0015546817],"category_scores_gemma":[0.0015225252,0.00013703204,0.0003506575,0.00038940873,0.00033760458,0.00026642985,0.00039662205,0.00038044955,0.00014831641],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005731168,0.00008043603,0.998102,0.000013206613,0.00001626731,0.00016511788,0.00022986364,0.000026650678,0.00008688521,0.000011759789,0.0000678069,0.0011426065],"study_design_scores_gemma":[0.0000025698023,0.0000854673,0.9988826,0.000009923796,0.000018454539,0.000107018386,0.0006155979,0.00015913199,0.000021992499,0.000010796812,0.000084782296,0.0000017253],"about_ca_topic_score_codex":0.022156004,"about_ca_topic_score_gemma":0.025003534,"teacher_disagreement_score":0.022156004,"about_ca_system_score_codex":0.0003814146,"about_ca_system_score_gemma":0.0005798369,"threshold_uncertainty_score":0.04405409},"labels":[],"label_agreement":null},{"id":"W4386527726","doi":"10.6000/1929-6029.2023.12.12","title":"Optimal Weighting of Preclinical Alzheimer’s Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer’s Disease Clinical Trials","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Genentech; University of California, San Diego; National Institutes of Health; Novo Nordisk; Arrowhead Pharmaceuticals","keywords":"Weighting; Clinical trial; Context (archaeology); Cognition; Standard deviation; Component (thermodynamics); Medicine; Statistics; Mathematics; Internal medicine; Psychiatry","score_opus":0.39860628414763016,"score_gpt":0.6042438162671239,"score_spread":0.2056375321194937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386527726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10231232,0.005776163,0.87699187,0.0023042345,0.00051180506,0.00426646,0.000579232,0.0007394655,0.006518486],"genre_scores_gemma":[0.4886609,0.0013593959,0.5027299,0.0009384757,0.00023636033,0.0046851104,0.0005895935,0.0002889613,0.00051139126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.86305714,0.117339835,0.008034172,0.0028417015,0.007963086,0.00076410745],"domain_scores_gemma":[0.79926515,0.14956154,0.021008223,0.015800016,0.01308479,0.0012803084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18871172,0.0016398677,0.0026820544,0.0038381254,0.00041416628,0.0033938403,0.0014962016,0.0013852048,0.0025701956],"category_scores_gemma":[0.35519177,0.0007372248,0.0022488206,0.0030964597,0.0012697895,0.0030215324,0.0032829954,0.002360352,0.00056321966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009285335,0.001003206,0.049663514,0.0031540408,0.00421553,0.00023737905,0.0010886079,0.12463223,0.007804145,0.05545084,0.009709581,0.7337556],"study_design_scores_gemma":[0.00670353,0.010904256,0.06584371,0.0029097304,0.0041031237,0.0007973944,0.00038857578,0.5799561,0.019673409,0.27399573,0.034133077,0.0005914222],"about_ca_topic_score_codex":0.00070155534,"about_ca_topic_score_gemma":0.0006454474,"teacher_disagreement_score":0.18871172,"about_ca_system_score_codex":0.0017295552,"about_ca_system_score_gemma":0.0030720998,"threshold_uncertainty_score":0.99801487},"labels":[],"label_agreement":null},{"id":"W4386814135","doi":"10.6000/1929-6029.2023.12.13","title":"Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Faculty of Medicine Siriraj Hospital, Mahidol University","keywords":"Multicollinearity; Lasso (programming language); Feature selection; Estimator; Computer science; Mean squared error; Mathematics; Penalty method; Artificial intelligence; Statistics; Pattern recognition (psychology); Algorithm; Machine learning; Regression analysis; Mathematical optimization","score_opus":0.53683462234748,"score_gpt":0.5753388564817199,"score_spread":0.03850423413423987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386814135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007572295,0.00053558015,0.9907985,0.00034991372,0.00005795911,0.000035959598,0.00007558673,0.00016335827,0.0004108492],"genre_scores_gemma":[0.37188154,0.0018285654,0.61926925,0.00071165204,0.00058838504,0.00077420525,0.0013790366,0.00029660427,0.0032707562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99344903,0.0041578985,0.0003046457,0.00077620154,0.0010331568,0.00027910576],"domain_scores_gemma":[0.99153405,0.00596941,0.0007934957,0.0005745066,0.0009692761,0.00015927613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076856404,0.0012421352,0.0020623077,0.0009877604,0.0007393414,0.0014233936,0.0017772889,0.0015545002,0.0016102515],"category_scores_gemma":[0.01869862,0.0005156713,0.0016508695,0.0017056039,0.0012336258,0.0017238035,0.0018527083,0.0038500163,0.0005082042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058110955,0.00015533814,0.0040875566,0.00082309876,0.00039585965,0.0005395785,0.00043366235,0.69859666,0.009268162,0.050608724,0.009056095,0.22545415],"study_design_scores_gemma":[0.00002151884,0.000048266447,0.00042208415,0.000027092718,0.000015178823,0.0000518911,0.000023652761,0.9868995,0.0008038442,0.010277634,0.0013926765,0.000016652495],"about_ca_topic_score_codex":0.002086832,"about_ca_topic_score_gemma":0.0014978253,"teacher_disagreement_score":0.0076856404,"about_ca_system_score_codex":0.00056785584,"about_ca_system_score_gemma":0.0015915661,"threshold_uncertainty_score":0.040646076},"labels":[],"label_agreement":null},{"id":"W4386832973","doi":"10.6000/1929-6029.2023.12.14","title":"Unveiling the Dynamics of the Omicron Variant: Prevalence, Risk Factors, and Vaccination Efficacy during the Third Wave of Covid-19 in Indonesia's Gowa Regency","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Medicine; Logistic regression; Vaccination; Coronavirus disease 2019 (COVID-19); Demography; Pandemic; Descriptive statistics; Demographics; Immunology; Internal medicine; Disease; Statistics","score_opus":0.05551420492141679,"score_gpt":0.4266070178298729,"score_spread":0.37109281290845614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386832973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99953485,0.00006594113,0.000016854476,0.000040461102,0.000001844215,0.0000049232376,0.00010474014,0.0000011068556,0.00022936267],"genre_scores_gemma":[0.99958104,0.000051664858,0.000043547723,0.000021748947,0.0000031906059,0.000007102993,0.00014906556,5.84169e-7,0.00014207939],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99981254,0.000051292376,0.000016868229,0.000033765413,0.000029083269,0.000056464243],"domain_scores_gemma":[0.999501,0.000060771552,0.00025030226,0.000016951073,0.00006108195,0.00010978667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051403366,0.00014811671,0.00016180787,0.00032816757,0.00027184474,0.00040797816,0.00027765692,0.00023111368,0.00059593446],"category_scores_gemma":[0.00096427655,0.00010785274,0.00014016421,0.00041918154,0.00021756765,0.00032202608,0.00028582787,0.00035930326,0.00016153502],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037243153,0.000046168756,0.99839205,0.0000047288927,0.000005496672,0.00004114778,0.00016888167,0.000018973695,0.00012867006,0.00000575315,0.00007907486,0.0010718845],"study_design_scores_gemma":[6.634536e-7,0.000040380004,0.99933296,0.0000039861957,0.0000027750534,0.00003868787,0.0003698847,0.0000959827,0.000036146,0.000002943388,0.00007454602,0.0000010357074],"about_ca_topic_score_codex":0.021253066,"about_ca_topic_score_gemma":0.035606213,"teacher_disagreement_score":0.021253066,"about_ca_system_score_codex":0.0004870493,"about_ca_system_score_gemma":0.00045091243,"threshold_uncertainty_score":0.04225874},"labels":[],"label_agreement":null},{"id":"W4386861886","doi":"10.6000/1929-6029.2023.12.15","title":"Diagnostic Accuracy of Anthropometric Markers of Obesity for Prediabetes: A Systematic Review and Meta-Analysis","year":2023,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Health and Lifestyle Studies","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prediabetes; Medicine; Waist; Body mass index; Anthropometry; Waist-to-height ratio; Confidence interval; Meta-analysis; Cohort study; Obesity; Cohort; Internal medicine; Cross-sectional study; Diabetes mellitus; Type 2 diabetes; Pathology; Endocrinology","score_opus":0.4917427567560632,"score_gpt":0.6710738500209408,"score_spread":0.17933109326487756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386861886","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035599761,0.9944195,0.0005901645,0.00024699984,0.000120009245,0.00026173436,0.00059614424,0.000025468216,0.00018000357],"genre_scores_gemma":[0.1596261,0.8326958,0.003483075,0.0009093135,0.00037692493,0.0014080097,0.0012312519,0.000034253047,0.00023529687],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9820557,0.008031149,0.005542056,0.0017708492,0.002168149,0.00043209436],"domain_scores_gemma":[0.94977033,0.039993074,0.005692668,0.0012529525,0.002917335,0.00037357322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025364062,0.0029623315,0.02063834,0.010376018,0.0007330561,0.0040116915,0.0027106563,0.0023025419,0.002887775],"category_scores_gemma":[0.056932755,0.0016498467,0.039838266,0.010413507,0.00096073723,0.0022847785,0.001739429,0.0018551808,0.00027420072],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011912176,0.00002431589,0.009649534,0.44548756,0.52893853,0.00014181122,0.00009637826,0.0005810895,0.0001435306,0.0001320833,0.0007362485,0.01287775],"study_design_scores_gemma":[0.00027320097,0.00011400534,0.005228481,0.034093,0.95846814,0.00010774289,0.000043219417,0.00025050688,0.000098731645,0.00017286795,0.0011239076,0.00002627362],"about_ca_topic_score_codex":0.006464032,"about_ca_topic_score_gemma":0.01292762,"teacher_disagreement_score":0.025364062,"about_ca_system_score_codex":0.0029721188,"about_ca_system_score_gemma":0.0051450925,"threshold_uncertainty_score":0.13413954},"labels":[],"label_agreement":null},{"id":"W4386927155","doi":"10.6000/1929-6029.2023.12.16","title":"Dysmenorrhea Impact and Insights: A Statistical Analysis among Allied Health Professional Students in West Bengal, India","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Menstrual Health and Disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"West bengal; Menstruation; Descriptive statistics; Medicine; Family medicine; Alternative medicine; Data collection; Psychology; Demography; Social science; Socioeconomics; Pathology; Sociology","score_opus":0.06743278734569401,"score_gpt":0.5716667117345834,"score_spread":0.5042339243888894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386927155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998765,0.00005037829,0.000054583932,0.0000672752,0.0000041175067,0.000090584894,0.0004419583,0.000004240637,0.00052188267],"genre_scores_gemma":[0.9989091,0.000091456335,0.00013564834,0.000032138934,0.000008290439,0.00015861652,0.00036878846,0.0000021901335,0.00029376283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981029,0.00045994236,0.0002916793,0.0001699265,0.0005775614,0.00039805874],"domain_scores_gemma":[0.99594903,0.0013000582,0.0012946349,0.00014945545,0.00077614706,0.0005307177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017496828,0.00026210534,0.00044703623,0.0034917188,0.0010789129,0.001287796,0.00069103396,0.00042613942,0.0034757392],"category_scores_gemma":[0.0044213976,0.0003153485,0.0009875135,0.0040004607,0.00078001956,0.0005431877,0.0015117942,0.0006349325,0.00038234767],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010626444,0.0001634654,0.993775,0.00007783115,0.00004978662,0.00013760693,0.0025591773,0.000027885366,0.00017461745,0.00003588794,0.00017501507,0.002717498],"study_design_scores_gemma":[0.0000044775834,0.00024884837,0.9886007,0.000018223149,0.000026236408,0.000103590406,0.01048169,0.00018172362,0.000059332688,0.000011039805,0.00025796625,0.000006113702],"about_ca_topic_score_codex":0.016388237,"about_ca_topic_score_gemma":0.016673943,"teacher_disagreement_score":0.016388237,"about_ca_system_score_codex":0.0012258698,"about_ca_system_score_gemma":0.0017640583,"threshold_uncertainty_score":0.03258568},"labels":[],"label_agreement":null},{"id":"W4387473718","doi":"10.6000/1929-6029.2023.12.17","title":"Enhancing Hospital Service Quality and Patient Safety through the MIRACLE Model: A Partial Least Squares Equation Approach","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Patient safety; Quality (philosophy); Partial least squares regression; Health care; Variables; Variable (mathematics); Medicine; Operations management; Psychology; Business; Statistics; Mathematics; Engineering; Political science","score_opus":0.19532427364727187,"score_gpt":0.43539431169620335,"score_spread":0.24007003804893148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387473718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3681847,0.0010720942,0.56117666,0.01367842,0.0003917367,0.0013161604,0.0013526342,0.0008913642,0.051936273],"genre_scores_gemma":[0.9035785,0.00070799916,0.084906556,0.000397258,0.00010882399,0.0009592576,0.00045856234,0.00005649554,0.00882661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99454194,0.003605088,0.00017420128,0.00061195967,0.00072079344,0.000346064],"domain_scores_gemma":[0.9944536,0.0035402612,0.0007202337,0.0001661777,0.00092688785,0.00019279968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045698257,0.0013962114,0.0007842238,0.0014049353,0.0008383488,0.0026267075,0.0025055718,0.0015152848,0.007890024],"category_scores_gemma":[0.010613809,0.00060711405,0.0018760829,0.0015449859,0.0010330392,0.0027025524,0.002837365,0.0020760472,0.0011046581],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046616068,0.0020818908,0.18939668,0.0013271864,0.001350809,0.0014419777,0.008237286,0.3548768,0.0019186613,0.25343007,0.01696104,0.16851152],"study_design_scores_gemma":[0.00012207495,0.0010505734,0.019172048,0.00034840748,0.00031282526,0.00021148575,0.0032907757,0.9184014,0.00071110856,0.044657264,0.011615333,0.0001066046],"about_ca_topic_score_codex":0.012616618,"about_ca_topic_score_gemma":0.011301312,"teacher_disagreement_score":0.012616618,"about_ca_system_score_codex":0.002422643,"about_ca_system_score_gemma":0.005753748,"threshold_uncertainty_score":0.026394725},"labels":[],"label_agreement":null},{"id":"W4387705350","doi":"10.6000/1929-6029.2023.12.18","title":"Determinant of Mental Emotional Disorder in Adolescent: A Cross-Sectional Study","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Child and Adolescent Psychosocial and Emotional Development","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Mental health; Cross-sectional study; CLARITY; Psychology; Observational study; Clinical psychology; Incidence (geometry); Psychiatry; Medicine","score_opus":0.09305645686169382,"score_gpt":0.4987891451739354,"score_spread":0.4057326883122416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387705350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993705,0.00012462262,0.000049462367,0.00002387648,0.000004098606,0.000024823114,0.00017350652,0.0000010216442,0.00022810829],"genre_scores_gemma":[0.9993088,0.00016088167,0.00012596078,0.000036017984,0.000005508198,0.000030277297,0.0002248333,7.5932746e-7,0.000106953325],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995316,0.00013560259,0.00007441046,0.00007240157,0.00012149972,0.00006450347],"domain_scores_gemma":[0.998993,0.00017263499,0.00046161885,0.000058029887,0.00013525307,0.00017953204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008993633,0.00019912541,0.00028027082,0.0005987712,0.0004562761,0.0005046127,0.0002437508,0.00031000847,0.0013012412],"category_scores_gemma":[0.0015348442,0.00032323183,0.00041330984,0.000654235,0.00019075768,0.00045793888,0.00041245137,0.00076775247,0.00021353076],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105072295,0.00007606608,0.99938476,0.000006972621,0.000013709644,0.00003185506,0.00012090974,0.0000042331476,0.00003980474,0.000007678746,0.000027617465,0.0002759263],"study_design_scores_gemma":[0.0000030619592,0.00016763694,0.998611,0.000013094663,0.00002061147,0.00024526764,0.00072371005,0.000055718832,0.000026547401,0.000008427486,0.00012327482,0.0000016432102],"about_ca_topic_score_codex":0.003192084,"about_ca_topic_score_gemma":0.0054345336,"teacher_disagreement_score":0.003192084,"about_ca_system_score_codex":0.00022218552,"about_ca_system_score_gemma":0.00032516045,"threshold_uncertainty_score":0.00634706},"labels":[],"label_agreement":null},{"id":"W4387812193","doi":"10.6000/1929-6029.2023.12.19","title":"Access to Dental Services among Hypertensive Elderly in Peru: Exploring Patterns and Implications","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Poisson regression; Medicine; Observational study; Confidence interval; Oral health; Regression analysis; Cross-sectional study; Demography; Environmental health; Family medicine; Internal medicine; Population; Statistics","score_opus":0.15039495263142696,"score_gpt":0.49870717073932064,"score_spread":0.3483122181078937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387812193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987858,0.00030306465,0.000046585854,0.00016282948,0.000001839816,0.000008565562,0.00024794624,0.0000017376719,0.00044172013],"genre_scores_gemma":[0.99930644,0.00032065168,0.00007787312,0.00004074592,0.0000057796724,0.000009002646,0.0001400658,7.06738e-7,0.00009872421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99957305,0.00016738418,0.000046402693,0.000055588676,0.00006227442,0.00009528499],"domain_scores_gemma":[0.99906653,0.00023209257,0.0004424994,0.000029840992,0.000110809306,0.00011836302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000588689,0.000126175,0.00020226785,0.0012379321,0.00041471346,0.0005429535,0.0003016939,0.00029889823,0.0011577659],"category_scores_gemma":[0.0019710485,0.00019370236,0.00027543557,0.0017590349,0.00028543876,0.00041628114,0.00065488636,0.00031595037,0.00010916327],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019494038,0.000030754243,0.99783176,0.000020005355,0.000014710705,0.000057232926,0.000490257,0.000007673361,0.00006196811,0.000015802794,0.00004733459,0.0014029319],"study_design_scores_gemma":[0.0000015526183,0.000044586555,0.99797136,0.00001433352,0.000010651788,0.00011900547,0.0015979138,0.00006243542,0.000013845055,0.000018138353,0.00014420316,0.0000019195732],"about_ca_topic_score_codex":0.024735453,"about_ca_topic_score_gemma":0.037093647,"teacher_disagreement_score":0.024735453,"about_ca_system_score_codex":0.00043881522,"about_ca_system_score_gemma":0.0004961393,"threshold_uncertainty_score":0.04918295},"labels":[],"label_agreement":null},{"id":"W4388574917","doi":"10.6000/1929-6029.2023.12.23","title":"Using Measurement Invariance to Explore the Source of Variation in Basic Medical Science Students’ Evaluation of Teaching Effectiveness","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Innovations in Medical Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Set (abstract data type); Variation (astronomy); Mathematics education; Variable (mathematics); Medical education; Medical science; Measurement invariance; Variables; Psychology; Computer science; Medicine; Mathematics; Statistics; Confirmatory factor analysis; Structural equation modeling","score_opus":0.40994163030746955,"score_gpt":0.6034593885943896,"score_spread":0.19351775828692008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388574917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8386616,0.00068159844,0.14354198,0.00076681946,0.00023227137,0.0009117159,0.0006299812,0.00013175562,0.014442235],"genre_scores_gemma":[0.9921467,0.000044192642,0.0069686463,0.000056830588,0.00002893557,0.000374766,0.00021313514,0.000017982653,0.00014874834],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.93424416,0.045512903,0.003600142,0.0050519924,0.0104097,0.0011811375],"domain_scores_gemma":[0.78307784,0.16026409,0.01891098,0.026716499,0.010104451,0.0009262223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.052795243,0.00065079576,0.0007892287,0.0024928546,0.001193338,0.0021693448,0.0010253857,0.0006481653,0.0026341283],"category_scores_gemma":[0.12954786,0.0003125011,0.0026996622,0.0033698205,0.0035120656,0.0020680223,0.002442995,0.001439836,0.00029518135],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003954368,0.00030508943,0.9043991,0.00036884504,0.002276452,0.00013284513,0.0053123105,0.0015392071,0.0015832579,0.014653311,0.00092494907,0.06810926],"study_design_scores_gemma":[0.00006360863,0.0010874218,0.95804036,0.00027107232,0.0006391556,0.00023123133,0.0030706017,0.01126903,0.002430114,0.01959858,0.0032083779,0.00009053793],"about_ca_topic_score_codex":0.0021658342,"about_ca_topic_score_gemma":0.0012858509,"teacher_disagreement_score":0.052795243,"about_ca_system_score_codex":0.0014778466,"about_ca_system_score_gemma":0.0015221395,"threshold_uncertainty_score":0.27921128},"labels":[],"label_agreement":null},{"id":"W4388575087","doi":"10.6000/1929-6029.2023.12.21","title":"Efficiency of the Crile Procedure in the Removal of Thyroid Malignancies Invaded into the Internal Jugular Vein","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Surgery; Internal jugular vein; Blood flow; Jugular vein; Thyroid; Thyroid cancer; Radiology; Vein; Thrombosis; Resection; Stage (stratigraphy); Blood supply; Internal medicine","score_opus":0.04813224619661706,"score_gpt":0.4235823269460156,"score_spread":0.37545008074939856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388575087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99306434,0.0043200366,0.0011033363,0.00006788352,0.000023253078,0.00004720634,0.00003682167,0.000024891158,0.0013122588],"genre_scores_gemma":[0.9970049,0.0011117334,0.0013501275,0.000050989856,0.00005978119,0.000017259676,0.000052577154,0.000006414604,0.00034620566],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993819,0.00027141927,0.000041715688,0.00007527631,0.00015106425,0.00007858905],"domain_scores_gemma":[0.99884295,0.0005412879,0.00024490268,0.00011093634,0.000091397014,0.00016846143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008391596,0.00021361304,0.0003670865,0.00057444966,0.00019321445,0.00033499673,0.00023635746,0.00025557695,0.0016113274],"category_scores_gemma":[0.002493241,0.00006777555,0.0004040125,0.00017395086,0.0002906634,0.0003481509,0.0003513329,0.00034424372,0.00027974276],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.022398287,0.004133121,0.21725927,0.0012371705,0.00047690625,0.004235857,0.0008972608,0.0026639884,0.13362433,0.0003483859,0.00072480855,0.6120005],"study_design_scores_gemma":[0.0008195291,0.11559614,0.7897001,0.0002234652,0.0007616886,0.017614728,0.0011752163,0.0040084408,0.058331776,0.00019737992,0.011472017,0.00009949657],"about_ca_topic_score_codex":0.00047616672,"about_ca_topic_score_gemma":0.00068332715,"teacher_disagreement_score":0.0016113274,"about_ca_system_score_codex":0.00021031017,"about_ca_system_score_gemma":0.00034368757,"threshold_uncertainty_score":0.0053904057},"labels":[],"label_agreement":null},{"id":"W4388575134","doi":"10.6000/1929-6029.2023.12.22","title":"Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Covariate; Kernel (algebra); Kernel regression; Computer science; Feature selection; Nonparametric regression; Regression analysis; Artificial intelligence; Kernel smoother; Cross-validation; Kernel method; Functional data analysis; Mean squared error; Mathematics; Machine learning; Statistics; Data mining; Support vector machine; Radial basis function kernel","score_opus":0.4373698802794771,"score_gpt":0.5709719315010723,"score_spread":0.13360205122159519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388575134","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80518794,0.0020631147,0.18704112,0.0010299829,0.00007864186,0.00013561064,0.00042144864,0.0005430823,0.0034990543],"genre_scores_gemma":[0.97463006,0.0002965659,0.024148082,0.00005395553,0.00002526362,0.000044945882,0.00032975295,0.000037811318,0.00043365636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933891,0.004315227,0.0002461932,0.00091630593,0.0009287575,0.00020430815],"domain_scores_gemma":[0.9579757,0.035714738,0.0015946703,0.0017636047,0.002673149,0.00027809807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020182684,0.001434271,0.0012373691,0.0019090435,0.00054391095,0.0017677754,0.0017696915,0.0017741413,0.0010095017],"category_scores_gemma":[0.04493159,0.00027643424,0.0012006835,0.0017282055,0.0010983204,0.0018276115,0.0012101607,0.0013497807,0.000321718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010590139,0.00052791333,0.08035937,0.0004483602,0.00036836075,0.00067955104,0.00046633242,0.7869925,0.0015554306,0.005382162,0.0015623063,0.120598786],"study_design_scores_gemma":[0.000013050196,0.00020985698,0.01065791,0.000044418106,0.00003460648,0.0001201135,0.00018831276,0.9854091,0.00096976844,0.0019474152,0.00037559264,0.00002997896],"about_ca_topic_score_codex":0.015502316,"about_ca_topic_score_gemma":0.008146341,"teacher_disagreement_score":0.020182684,"about_ca_system_score_codex":0.0012994219,"about_ca_system_score_gemma":0.0014749785,"threshold_uncertainty_score":0.106737494},"labels":[],"label_agreement":null},{"id":"W4388575308","doi":"10.6000/1929-6029.2023.12.20","title":"The Pentahelix Partnership Responses during Covid-19 Pandemic in Makassar","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Prevention and Impact","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"General partnership; Government (linguistics); Public relations; Coronavirus disease 2019 (COVID-19); Business; Pandemic; Political science; Medicine; Finance","score_opus":0.32579260678656974,"score_gpt":0.6199538670255141,"score_spread":0.2941612602389444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388575308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9746577,0.0007109931,0.00038999517,0.011149356,0.00019769509,0.00012600598,0.000048746795,0.000016762115,0.012702751],"genre_scores_gemma":[0.99370897,0.0004753214,0.00024126003,0.0015522918,0.000025800358,0.00011646119,0.00003382291,0.000008879385,0.003837111],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9939084,0.003561512,0.00012220384,0.00018238592,0.00040821295,0.0018173041],"domain_scores_gemma":[0.9953662,0.0013690415,0.0006789487,0.00012001552,0.00039116523,0.002074619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044349963,0.00036676836,0.00033370225,0.0006421611,0.017064342,0.0037492549,0.0013262293,0.0023721557,0.0055156313],"category_scores_gemma":[0.0056476253,0.00039148287,0.0002096235,0.0008262454,0.0045165517,0.0027194745,0.014099481,0.0030541485,0.00042241206],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015682835,0.00015097116,0.027185256,0.00038545916,0.000017092358,0.01177432,0.929484,0.00014373848,0.0017914904,0.005963365,0.007863239,0.015084222],"study_design_scores_gemma":[0.0000036823956,0.000054725006,0.005322285,0.000077645695,0.0000026009748,0.00041825595,0.9788873,0.000057034686,0.00012741053,0.00027200906,0.014767426,0.000009604459],"about_ca_topic_score_codex":0.018744795,"about_ca_topic_score_gemma":0.033483814,"teacher_disagreement_score":0.018744795,"about_ca_system_score_codex":0.005650031,"about_ca_system_score_gemma":0.010059811,"threshold_uncertainty_score":0.040994048},"labels":[],"label_agreement":null},{"id":"W4388698215","doi":"10.6000/1929-6029.2023.12.24","title":"High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Flagging; Profiling (computer programming); Staffing; Medicine; Intensive care medicine; Kidney disease; Population; Health care; End-stage kidney disease; End stage renal disease; Dialysis; Emergency medicine; Disease; Internal medicine; Computer science; Nursing; Environmental health","score_opus":0.052723509228584216,"score_gpt":0.4805891833010638,"score_spread":0.4278656740724796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388698215","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016266078,0.06841946,0.89005095,0.018960219,0.0005501292,0.0002357506,0.0015258053,0.00047431653,0.0035172459],"genre_scores_gemma":[0.3159399,0.12494258,0.5435507,0.004792574,0.0036252402,0.0011811418,0.0024671857,0.00028661592,0.0032139418],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98941714,0.008626463,0.00038787958,0.00080168043,0.000576935,0.0001899366],"domain_scores_gemma":[0.8601335,0.13045013,0.003553567,0.002538225,0.0026533343,0.0006711697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02756273,0.0015447704,0.0033506514,0.002232595,0.00043175803,0.0040455195,0.0029939688,0.0023252037,0.0028378414],"category_scores_gemma":[0.061021805,0.0010727784,0.0033681714,0.0032244253,0.0016200036,0.0032067527,0.0021966195,0.004591941,0.0006797046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035843742,0.00041305734,0.06544084,0.0029095882,0.0020891973,0.00032489616,0.0006818005,0.37914664,0.00026205214,0.15456514,0.013236879,0.3805715],"study_design_scores_gemma":[0.00008215815,0.00036716132,0.011164923,0.001563188,0.00041509594,0.00023951117,0.0005579683,0.7122549,0.00020889001,0.23572287,0.037244067,0.00017926517],"about_ca_topic_score_codex":0.014626978,"about_ca_topic_score_gemma":0.011492564,"teacher_disagreement_score":0.02756273,"about_ca_system_score_codex":0.0018713493,"about_ca_system_score_gemma":0.0029307744,"threshold_uncertainty_score":0.14576733},"labels":[],"label_agreement":null},{"id":"W4388969292","doi":"10.6000/1929-6029.2023.12.25","title":"Joint Frailty Mixing Model for Recurrent Event Data with an Associated Terminal Event: Application to Hospital Readmission Data","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Terminal (telecommunication); Time point; Statistics; Event data; Proportional hazards model; Joint probability distribution; Hazard ratio; Mathematics; Computer science; Covariate; Confidence interval","score_opus":0.4478116718684234,"score_gpt":0.5945595593810867,"score_spread":0.14674788751266332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388969292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18163721,0.00080916873,0.815102,0.0007213968,0.00008250939,0.00018597093,0.00055226736,0.00033872298,0.0005706901],"genre_scores_gemma":[0.8568537,0.0007084335,0.13659677,0.00017416514,0.00018867657,0.0003857682,0.0017946484,0.00008050715,0.0032174082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99706453,0.0017697861,0.00016831343,0.00049250846,0.0002711527,0.00023375495],"domain_scores_gemma":[0.9830536,0.013071273,0.0012607233,0.0013234903,0.00086982106,0.00042112212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015046113,0.0010218697,0.0017220047,0.0014586784,0.000641764,0.0011972197,0.002261992,0.0018531168,0.0015046011],"category_scores_gemma":[0.024257516,0.00044199763,0.0030110131,0.0015654288,0.00087810477,0.0015362845,0.0017185728,0.0028765942,0.0002826691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009729827,0.00042683494,0.06115356,0.0004561731,0.0010225937,0.0018684305,0.0011506315,0.7418512,0.0033682112,0.07372614,0.002355363,0.11164787],"study_design_scores_gemma":[0.000035607787,0.00014492241,0.004808785,0.00002413446,0.00008922443,0.00017552862,0.00007362845,0.9773014,0.00035485733,0.01630717,0.00064585725,0.000038885773],"about_ca_topic_score_codex":0.007503511,"about_ca_topic_score_gemma":0.0047365758,"teacher_disagreement_score":0.015046113,"about_ca_system_score_codex":0.00081468455,"about_ca_system_score_gemma":0.001294238,"threshold_uncertainty_score":0.07957244},"labels":[],"label_agreement":null},{"id":"W4388969320","doi":"10.6000/1929-6029.2023.12.26","title":"A Novel Algorithm for Predicting Antimicrobial Resistance in Unequal Groups of Bacterial Isolates","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Antibiotic Use and Resistance","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Typhoid fever; Salmonella typhi; Antimicrobial; Antibiotics; Antibiotic resistance; Medicine; Drug resistance; Algorithm; Veterinary medicine; Statistics; Mathematics; Biology; Microbiology; Virology","score_opus":0.04053707669762511,"score_gpt":0.3854508001105442,"score_spread":0.3449137234129191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388969320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029203568,0.0004364391,0.9665394,0.0004049264,0.00016476042,0.00039118546,0.000496271,0.0016421508,0.00072130404],"genre_scores_gemma":[0.09687133,0.00017528694,0.8994751,0.00024319638,0.00014538964,0.00059995224,0.0013135592,0.0000551467,0.0011211069],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972023,0.0007602751,0.00042809075,0.0008433342,0.00058426947,0.00018176626],"domain_scores_gemma":[0.9933397,0.004219077,0.00049766974,0.00026169565,0.0014784796,0.00020320789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039666933,0.001103126,0.0017761287,0.0027942704,0.0010143782,0.0019528926,0.0024794352,0.0019473872,0.0017444927],"category_scores_gemma":[0.012024559,0.00051684194,0.0011049086,0.001893315,0.0005466429,0.0014521381,0.0013455924,0.0017391368,0.0008323967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067072484,0.00055682915,0.035746917,0.0001871429,0.0003488328,0.00023769068,0.00016852592,0.17853135,0.0048854453,0.003292668,0.008198003,0.7671759],"study_design_scores_gemma":[0.000078318386,0.00012350267,0.0021049397,0.000019739786,0.00003912524,0.00023560686,0.000031896507,0.99207884,0.0012663105,0.0024182624,0.0015811389,0.000022376918],"about_ca_topic_score_codex":0.0063603423,"about_ca_topic_score_gemma":0.0049416814,"teacher_disagreement_score":0.0063603423,"about_ca_system_score_codex":0.0010928145,"about_ca_system_score_gemma":0.0026113333,"threshold_uncertainty_score":0.020978093},"labels":[],"label_agreement":null},{"id":"W4389675280","doi":"10.6000/1929-6029.2023.12.27","title":"Comparative Study on Estimation Methods of Proportional Hazard Models for Interval-Censored Data","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Finkelstein's test; Statistics; Mathematics; Estimation; Piecewise; Interval estimation; Proportional hazards model; Interval (graph theory); Sample size determination; Confidence interval; Computer science","score_opus":0.6834944017493002,"score_gpt":0.6880050945517989,"score_spread":0.004510692802498717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389675280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05685019,0.015957795,0.9218084,0.0011232108,0.00035973932,0.0003973632,0.00024447637,0.00037156296,0.0028872627],"genre_scores_gemma":[0.49434823,0.012322929,0.48794532,0.00058788515,0.0004596017,0.0013312015,0.0011149691,0.00037063757,0.0015192654],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.90913445,0.07858153,0.0018106627,0.003177751,0.006796839,0.00049870706],"domain_scores_gemma":[0.6633673,0.31326,0.0058193156,0.007020282,0.009922165,0.00061100215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08311731,0.0010076463,0.0013804401,0.0029199505,0.00048027755,0.0017811831,0.002479763,0.0017434792,0.0043755616],"category_scores_gemma":[0.23845574,0.0005092592,0.002361134,0.0025764238,0.0010027838,0.0049103894,0.0016408078,0.0021084736,0.00043152538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042204903,0.00072907924,0.05871463,0.0052800723,0.004452581,0.0003659557,0.0016780988,0.18823332,0.001628486,0.09745319,0.0055677276,0.6316763],"study_design_scores_gemma":[0.00067183614,0.002024501,0.023905613,0.0017251357,0.0014565984,0.0008546425,0.0009746065,0.8930491,0.0033272798,0.056746352,0.014963682,0.00030053174],"about_ca_topic_score_codex":0.0023319123,"about_ca_topic_score_gemma":0.0011432688,"teacher_disagreement_score":0.08311731,"about_ca_system_score_codex":0.0015967003,"about_ca_system_score_gemma":0.0019884526,"threshold_uncertainty_score":0.43957162},"labels":[],"label_agreement":null},{"id":"W4389743152","doi":"10.6000/1929-6029.2023.12.28","title":"The Role of Emotional Intelligence in the Rehabilitation of the Former Prisoners of War","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Emotional Intelligence and Performance","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Emotional intelligence; Psychology; Rehabilitation; Life satisfaction; Scale (ratio); Descriptive statistics; Test (biology); Clinical psychology; Social psychology; Statistics","score_opus":0.07340945340824763,"score_gpt":0.49419427722918335,"score_spread":0.42078482382093574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389743152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99751675,0.0008425702,0.00009372597,0.00016839363,0.000011151637,0.0000075277935,0.000015010376,0.0000030121469,0.0013419009],"genre_scores_gemma":[0.99954623,0.0002127792,0.000068831636,0.000015871079,0.0000045744723,0.0000024790531,0.000011983078,5.012596e-7,0.000136762],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99973553,0.000116065814,0.000024866165,0.000015608259,0.000040573064,0.000067299035],"domain_scores_gemma":[0.99940634,0.000094349016,0.00023027162,0.000015987524,0.00008533199,0.00016777047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046744544,0.00019748475,0.00016941964,0.00051753776,0.0004158187,0.00064833026,0.000116188035,0.00013711007,0.0007913893],"category_scores_gemma":[0.0022455908,0.000051714906,0.00019031476,0.00021120661,0.0003348738,0.00018738942,0.00046738057,0.00030268382,0.000060627117],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029899986,0.00044024808,0.8828775,0.00020002603,0.00014106267,0.0010534878,0.005431568,0.0002878749,0.0020695212,0.0004428713,0.00058137294,0.10617537],"study_design_scores_gemma":[0.0000024048163,0.00021484331,0.99638736,0.00005398418,0.00002632535,0.00027049863,0.002017237,0.0001704654,0.00014996207,0.00010265375,0.00059804815,0.000006262719],"about_ca_topic_score_codex":0.0012038115,"about_ca_topic_score_gemma":0.0017370709,"teacher_disagreement_score":0.0012038115,"about_ca_system_score_codex":0.00023495834,"about_ca_system_score_gemma":0.0002803061,"threshold_uncertainty_score":0.002647519},"labels":[],"label_agreement":null},{"id":"W4389939035","doi":"10.6000/1929-6029.2023.12.30","title":"Analysis of the Effectiveness of Periodontitis Treatment Using Antimicrobial Agents","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Antimicrobial; Periodontitis; Adjunct; Antibiotics; Intensive care medicine; Scaling and root planing; MEDLINE; Inclusion and exclusion criteria; Clinical trial; Chronic periodontitis; Randomized controlled trial; Dentistry; Surgery; Internal medicine; Alternative medicine; Pathology; Microbiology","score_opus":0.09467768814550302,"score_gpt":0.4838586702232326,"score_spread":0.38918098207772955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389939035","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004457515,0.9944095,0.00012123278,0.00011127714,0.00007768582,0.0001936707,0.00012233286,0.0000033603812,0.0005034039],"genre_scores_gemma":[0.061582044,0.93613034,0.0012022693,0.00021641613,0.00018033541,0.0003356314,0.00017845318,0.000006475636,0.00016797315],"study_design_codex":"systematic_review","study_design_gemma":"observational","domain_scores_codex":[0.9880998,0.0045384644,0.004018394,0.0005647771,0.0026154981,0.00016295577],"domain_scores_gemma":[0.96494496,0.027228527,0.005232493,0.00034263448,0.0020352423,0.00021614632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009688765,0.0012286181,0.0069999304,0.008471862,0.0003324841,0.0019893157,0.001317422,0.0012939305,0.003793989],"category_scores_gemma":[0.02286705,0.0006046624,0.00794659,0.0060251234,0.00072803267,0.0014282566,0.00070510013,0.00096104346,0.0002560999],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038116202,0.00023940224,0.0016091978,0.8168092,0.03227133,0.00025925974,0.00022144952,0.00024948132,0.0022477754,0.00043747766,0.000504142,0.14133972],"study_design_scores_gemma":[0.0032011375,0.013991403,0.04014031,0.5090441,0.37558377,0.00237888,0.0011339503,0.00048586153,0.0059265797,0.0011367182,0.046838827,0.00013843455],"about_ca_topic_score_codex":0.0008983454,"about_ca_topic_score_gemma":0.0020867714,"teacher_disagreement_score":0.009688765,"about_ca_system_score_codex":0.0015576116,"about_ca_system_score_gemma":0.0020588995,"threshold_uncertainty_score":0.05123967},"labels":[],"label_agreement":null},{"id":"W4389952016","doi":"10.6000/1929-6029.2023.12.29","title":"The Effect of Symptoms on the Survival Time of Coronavirus Patients in the Sudanese Population","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proportional hazards model; Survival analysis; Coronavirus; Coronavirus disease 2019 (COVID-19); Log-rank test; Medicine; Hazard ratio; Pandemic; Demography; Population; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Regression analysis; Internal medicine; Statistics; Disease; Environmental health; Infectious disease (medical specialty)","score_opus":0.041080605086838674,"score_gpt":0.46190086589085805,"score_spread":0.42082026080401935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389952016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987858,0.00046994686,0.000058475118,0.000153893,0.00000989996,0.0000021514472,0.00012632506,0.0000012589004,0.00039235636],"genre_scores_gemma":[0.9996171,0.00016572057,0.000018989205,0.000021229269,0.000009481514,0.0000015523308,0.000092427756,5.9056975e-7,0.00007281209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99933237,0.00028554353,0.000044339937,0.00006820419,0.00006545448,0.00020414527],"domain_scores_gemma":[0.9975522,0.00077982823,0.00085756247,0.00013974552,0.00021674429,0.00045394673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012102246,0.0002184817,0.0002307891,0.00048467205,0.00040168528,0.00053226156,0.00016248073,0.00028636632,0.00127229],"category_scores_gemma":[0.0048075207,0.00008800257,0.0005496937,0.0005515429,0.00029726073,0.00041530133,0.00055992114,0.00068425713,0.00012770509],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000302773,0.000058048536,0.99408334,0.000012549592,0.0000667462,0.00015237281,0.00016109916,0.0001393341,0.00022485142,0.00007610758,0.00014275017,0.0045800516],"study_design_scores_gemma":[0.0000048072984,0.00020124012,0.9984119,0.000012304653,0.000044153123,0.00015604557,0.00039473223,0.00040703526,0.00005935358,0.00008866974,0.00021504717,0.0000046736163],"about_ca_topic_score_codex":0.0057958625,"about_ca_topic_score_gemma":0.0071305986,"teacher_disagreement_score":0.0057958625,"about_ca_system_score_codex":0.00056158524,"about_ca_system_score_gemma":0.00078399404,"threshold_uncertainty_score":0.01152426},"labels":[],"label_agreement":null},{"id":"W4390266323","doi":"10.6000/1929-6029.2023.12.32","title":"The Relationship Between The Physical Environment and Quality of Life for Patients With Type 2 Diabetes Mellitus","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Type 2 Diabetes Mellitus; Quality of life (healthcare); Logistic regression; Gerontology; Medicine; Diabetes mellitus; Affect (linguistics); Nonprobability sampling; Life quality; Environmental health; Psychology; Physical therapy; Internal medicine; Endocrinology; Population; Nursing","score_opus":0.14261039499504533,"score_gpt":0.4711171197571234,"score_spread":0.3285067247620781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390266323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997997,0.00071173074,0.00008103913,0.00022809063,0.000013835999,0.000008384062,0.00024748192,0.0000025370375,0.0007099675],"genre_scores_gemma":[0.9995061,0.00017216254,0.00008202946,0.00002929761,0.000009351325,0.000006329005,0.00012118799,7.4434956e-7,0.000072860195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994412,0.00021981653,0.00006908679,0.000054312346,0.00013043475,0.00008503561],"domain_scores_gemma":[0.997357,0.0007470192,0.0011742275,0.00005643687,0.00020606554,0.00045931406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007535539,0.0001705744,0.00023238108,0.00061321765,0.00034154128,0.0005958507,0.000225077,0.00031151442,0.0029442892],"category_scores_gemma":[0.003845776,0.000089716035,0.0003987329,0.0007511685,0.00019307909,0.0002896219,0.00033915334,0.00064313895,0.00015957438],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034975223,0.00004518436,0.9980196,0.000014355389,0.00003219135,0.00003037121,0.000052042848,0.00002273124,0.00002758737,0.0000062266727,0.000072105795,0.0016428037],"study_design_scores_gemma":[0.0000031392906,0.00008756257,0.9993279,0.0000106555,0.00002304476,0.00012173078,0.00019523352,0.00007817787,0.000016620268,0.000015403166,0.00011811386,0.0000024813096],"about_ca_topic_score_codex":0.0015755405,"about_ca_topic_score_gemma":0.0020881959,"teacher_disagreement_score":0.0029442892,"about_ca_system_score_codex":0.00023926307,"about_ca_system_score_gemma":0.00025312984,"threshold_uncertainty_score":0.009849608},"labels":[],"label_agreement":null},{"id":"W4390267949","doi":"10.6000/1929-6029.2023.12.31","title":"The Influence of Emotional Demonstration on the Smoking Behavior of Junior High School (SMP) Adolescents in South Sinjai District, Sinjai Regency","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adolescent Health and Behaviors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accidental; Accidental sampling; Psychology; Test (biology); Intervention (counseling); Sample (material); Research design; Theory of planned behavior; Data collection; Multistage sampling; Developmental psychology; Control (management); Social psychology; Mathematics education; Applied psychology; Medicine; Environmental health; Mathematics; Statistics; Computer science; Psychiatry","score_opus":0.11817375469342549,"score_gpt":0.5194306028224178,"score_spread":0.4012568481289923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390267949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997607,0.000026248714,0.0000062687905,0.000024669345,0.0000015658919,0.000003538413,0.0000047432536,6.595679e-7,0.00017168315],"genre_scores_gemma":[0.9997358,0.00004650714,0.000032025036,0.000011342515,0.0000018776637,0.0000060089424,0.000012248355,2.9046387e-7,0.0001539721],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996,0.00015324602,0.000020964162,0.000036422352,0.00009266422,0.00009670277],"domain_scores_gemma":[0.99876726,0.00022499621,0.00039897606,0.0000451711,0.000098302,0.00046530575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062409276,0.00014666912,0.00018159462,0.000263001,0.0005551626,0.00041001063,0.00025533244,0.00022815792,0.0015330521],"category_scores_gemma":[0.0014428543,0.00012709756,0.000225377,0.0001539424,0.00044575686,0.000140757,0.0005245181,0.00041619185,0.00011392363],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018086603,0.0015373664,0.976824,0.000095606294,0.000038478094,0.00034088778,0.003826233,0.00006960649,0.0020482428,0.00008171403,0.00021748761,0.014739546],"study_design_scores_gemma":[0.000004257722,0.00029791205,0.997285,0.000010342422,0.000008985549,0.0000328007,0.0020223206,0.00003570163,0.00012392485,0.000006873499,0.00016972283,0.000001978135],"about_ca_topic_score_codex":0.008937988,"about_ca_topic_score_gemma":0.026595857,"teacher_disagreement_score":0.008937988,"about_ca_system_score_codex":0.00042482305,"about_ca_system_score_gemma":0.0008164466,"threshold_uncertainty_score":0.0177719},"labels":[],"label_agreement":null},{"id":"W4390345846","doi":"10.6000/1929-6029.2023.12.33","title":"Advancing Healthcare Service Efficacy by Optimizing Pharmaceutical Inventory Management: Leveraging ABC, VED Analysis for Trend Demand","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Operations Management Techniques","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"VIT University; Vellore Institute of Technology, Chennai","keywords":"Business; Health care; Inventory management; Healthcare service; Operations management; Service (business); Process management; Operations research; Marketing; Engineering; Economics","score_opus":0.2660898949723599,"score_gpt":0.5846512723034222,"score_spread":0.31856137733106227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390345846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6996062,0.001625286,0.27272516,0.0037342082,0.00017745829,0.0012191361,0.0032001599,0.0008883168,0.016824089],"genre_scores_gemma":[0.9257068,0.0003134006,0.07172059,0.00010550106,0.000050079296,0.0001583905,0.001187726,0.000034841356,0.0007225896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970079,0.0015883205,0.0003076634,0.00032636672,0.00050940044,0.00026027768],"domain_scores_gemma":[0.9812092,0.013073988,0.0022746306,0.00071256503,0.0023921344,0.0003375109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054946346,0.000951503,0.0010045756,0.0060058497,0.00049368734,0.0037942685,0.0009657265,0.0007013999,0.0029813815],"category_scores_gemma":[0.024485689,0.00044924265,0.001467685,0.004311475,0.0006064195,0.0027939954,0.0012493482,0.0011634135,0.00040611892],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048195597,0.0007786804,0.36246422,0.0008087814,0.00055455894,0.00048402324,0.0008687387,0.3990779,0.0010357591,0.016904721,0.0062526404,0.21028803],"study_design_scores_gemma":[0.000009636528,0.000113913804,0.015444562,0.00007079946,0.00004966209,0.000038715512,0.0006869351,0.97818595,0.0002484362,0.004121708,0.001003678,0.000026028307],"about_ca_topic_score_codex":0.021677412,"about_ca_topic_score_gemma":0.017288385,"teacher_disagreement_score":0.021677412,"about_ca_system_score_codex":0.0025877615,"about_ca_system_score_gemma":0.0030708117,"threshold_uncertainty_score":0.043102443},"labels":[],"label_agreement":null},{"id":"W4390510093","doi":"10.6000/1929-6029.2023.12.34","title":"Trend, Associated Factors and Concordance of Obesity by Body Mass Index, Waist Circumference and Waist-Height Ratio in Adolescents. An Analysis of a 4-Year National Survey","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health and Lifestyle Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Waist; Body mass index; Waist-to-height ratio; Obesity; Demography; Abdominal obesity; Medicine; Marital status; Anthropometry; Body Shape Index; Body volume index; Waist–hip ratio; Gerontology; Classification of obesity; Environmental health; Population; Fat mass; Internal medicine","score_opus":0.15523203631089005,"score_gpt":0.5396665594962413,"score_spread":0.38443452318535126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390510093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984541,0.00031109,0.00007658288,0.000033627723,0.0000048509405,0.000005964335,0.00077083637,0.000005329616,0.00033759142],"genre_scores_gemma":[0.99880207,0.00012615543,0.000093127164,0.00000976908,0.0000051732845,0.000013465103,0.0008021983,0.0000019230185,0.00014604925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996136,0.00011383969,0.000055069806,0.000092123664,0.00007332616,0.0000521043],"domain_scores_gemma":[0.99854124,0.0003176263,0.0006794438,0.00009621448,0.0002301897,0.00013531045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075994607,0.00012141389,0.00016844527,0.0008168813,0.00017228434,0.00032691227,0.00024051007,0.00023597603,0.0010912358],"category_scores_gemma":[0.0019092943,0.00019515186,0.00039406025,0.0009441782,0.00013247658,0.00028343688,0.00024728803,0.00039303757,0.0001962354],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016846594,0.0000062400914,0.99956006,0.0000027246801,0.00001743846,0.000007066097,0.000026791362,0.000008548787,0.000029572895,0.000004704935,0.00003554807,0.00028440912],"study_design_scores_gemma":[0.0000014727655,0.000028155499,0.9996025,0.0000031057664,0.000013667093,0.00004587625,0.00009324492,0.0001002148,0.000015350039,0.0000043840573,0.00009121842,7.994881e-7],"about_ca_topic_score_codex":0.007884469,"about_ca_topic_score_gemma":0.008623383,"teacher_disagreement_score":0.007884469,"about_ca_system_score_codex":0.0001780306,"about_ca_system_score_gemma":0.00031474867,"threshold_uncertainty_score":0.015677154},"labels":[],"label_agreement":null},{"id":"W4390528330","doi":"10.6000/1929-6029.2023.12.35","title":"The Impact of COVID-19 Pandemic on Distress Intolerance: Among Panic Buyers in Turkey","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Distress; Panic; Pandemic; Clinical psychology; Feeling; Psychology; Coronavirus disease 2019 (COVID-19); Psychiatry; Medicine; Anxiety; Social psychology; Disease; Internal medicine","score_opus":0.15460511531701698,"score_gpt":0.47979080950826974,"score_spread":0.32518569419125276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390528330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994885,0.00007550335,0.000029406347,0.00006321152,0.000002913067,0.0000021963956,0.00003082382,4.6021836e-7,0.0003069444],"genre_scores_gemma":[0.9997789,0.000055423043,0.000023625209,0.000012500696,0.0000029115245,9.251671e-7,0.000030197858,2.4709115e-7,0.000095228665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99980956,0.000043627148,0.000018419927,0.000024490542,0.000040171282,0.000063658146],"domain_scores_gemma":[0.9991411,0.00016099156,0.00042283602,0.000025511754,0.0000809054,0.00016862874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040726337,0.00015297635,0.0001476235,0.0005206113,0.00031939568,0.00068308396,0.00017055536,0.00028688935,0.0016817349],"category_scores_gemma":[0.0012694164,0.00011487597,0.00022942683,0.0004374374,0.0003078801,0.000376267,0.00041769104,0.0004551291,0.00010173931],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007799838,0.0000652365,0.9918469,0.000027169213,0.000020018118,0.00039499562,0.0013906485,0.00008949827,0.00026108613,0.000105059335,0.00016058167,0.005560807],"study_design_scores_gemma":[0.0000010803636,0.00005592151,0.9948708,0.0000069717644,0.0000061874125,0.00018472581,0.0044385637,0.00013890333,0.000038211063,0.000041940955,0.00021276514,0.0000040541986],"about_ca_topic_score_codex":0.004675052,"about_ca_topic_score_gemma":0.006458822,"teacher_disagreement_score":0.004675052,"about_ca_system_score_codex":0.0004960839,"about_ca_system_score_gemma":0.00029928374,"threshold_uncertainty_score":0.009295702},"labels":[],"label_agreement":null},{"id":"W4390976339","doi":"10.6000/1929-6029.2024.13.01","title":"A Double Truncated Binomial Model to Assess Psychiatric Health through Brief Psychiatric Rating Scale: When is Intervention Useful?","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kurtosis; Brief Psychiatric Rating Scale; Mathematics; Statistics; Confidence interval; Skewness; Rating scale; Negative binomial distribution; Psychology; Psychiatry; Poisson distribution; Psychosis","score_opus":0.26935983563864385,"score_gpt":0.5687714749139448,"score_spread":0.2994116392753009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390976339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087464936,0.0026281176,0.8927258,0.006082672,0.0008204969,0.0013360099,0.0018986157,0.00030967605,0.0067335786],"genre_scores_gemma":[0.63906395,0.003539425,0.3379739,0.00196875,0.00067752984,0.004835399,0.0021692517,0.00009309042,0.009678677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9878163,0.0094539365,0.00044335952,0.00091662235,0.0008590881,0.000510805],"domain_scores_gemma":[0.97057414,0.02545544,0.0014456973,0.0009767399,0.0010177161,0.00053027406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020836074,0.0008837707,0.0025464564,0.0012378602,0.00045846606,0.0018369218,0.0028927317,0.0025032584,0.00791996],"category_scores_gemma":[0.04267951,0.00043256552,0.0014096582,0.00140596,0.0012722022,0.0026291907,0.0017010269,0.0027131166,0.0010209968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007893378,0.0012840328,0.06594004,0.002499832,0.0011124006,0.0029004589,0.0027666504,0.1568099,0.0032047124,0.38031584,0.017498963,0.35777384],"study_design_scores_gemma":[0.0009125948,0.0026549806,0.00950601,0.00075900817,0.0004423187,0.0009778955,0.0008934086,0.77164906,0.00075960264,0.19912274,0.012112975,0.00020922787],"about_ca_topic_score_codex":0.0037344778,"about_ca_topic_score_gemma":0.0032188548,"teacher_disagreement_score":0.020836074,"about_ca_system_score_codex":0.0015338382,"about_ca_system_score_gemma":0.0017988089,"threshold_uncertainty_score":0.110193014},"labels":[],"label_agreement":null},{"id":"W4390976683","doi":"10.6000/1929-6029.2024.13.02","title":"Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Logistic regression; Covariate; Monte Carlo method; Markov chain; Machine learning; Bayesian probability; Artificial intelligence; Data mining; Statistics; Econometrics; Mathematics","score_opus":0.10806006502510578,"score_gpt":0.4874183684286137,"score_spread":0.3793583034035079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390976683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74392676,0.001226926,0.24088012,0.0015061153,0.00014485074,0.00034237825,0.0074230265,0.0011296032,0.003420302],"genre_scores_gemma":[0.92784464,0.0002668923,0.06303995,0.00014420558,0.000048287297,0.0002934223,0.0068778624,0.000087070366,0.0013976932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986669,0.0006723431,0.00007571777,0.00026680934,0.00017585629,0.00014246657],"domain_scores_gemma":[0.9772296,0.019371387,0.0009995106,0.001022163,0.001062714,0.0003146591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052045956,0.000557967,0.0009966813,0.0014082729,0.00066597835,0.0013461449,0.0016103113,0.0012491328,0.0027963626],"category_scores_gemma":[0.017508393,0.00028748353,0.0011541916,0.0012140012,0.0007840527,0.0008678995,0.00075556646,0.0014972179,0.00028811488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021588351,0.00014595113,0.025351731,0.00017265936,0.0001442773,0.00038211784,0.0001362333,0.94513327,0.0004939248,0.013716039,0.0033253192,0.010782575],"study_design_scores_gemma":[0.0000111636655,0.00002141159,0.002038393,0.000014176931,0.000010560015,0.000036590292,0.00003040845,0.992664,0.00017706645,0.0045314594,0.00045333462,0.000011514063],"about_ca_topic_score_codex":0.018495666,"about_ca_topic_score_gemma":0.019732747,"teacher_disagreement_score":0.018495666,"about_ca_system_score_codex":0.0009952888,"about_ca_system_score_gemma":0.0012553971,"threshold_uncertainty_score":0.036776066},"labels":[],"label_agreement":null},{"id":"W4391930990","doi":"10.6000/1929-6029.2024.13.03","title":"Triglyceridemic Waist Phenotypes as Risk Factors for Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis","year":2024,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Adipokines, Inflammation, and Metabolic Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Waist; Medicine; Type 2 Diabetes Mellitus; Diabetes mellitus; Internal medicine; Phenotype; Biology; Endocrinology; Genetics; Obesity","score_opus":0.16552085766507865,"score_gpt":0.506419023494526,"score_spread":0.34089816582944743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391930990","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042368774,0.9937122,0.00055013836,0.0002995435,0.0001249231,0.00026576172,0.00060709333,0.000025167012,0.00017841822],"genre_scores_gemma":[0.13405326,0.8591234,0.0027523567,0.0008296901,0.00029292805,0.0016168371,0.0010362368,0.000021821035,0.00027350258],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9903044,0.0043626106,0.0026774937,0.0011225861,0.0011905453,0.00034236227],"domain_scores_gemma":[0.98107374,0.014132762,0.0026617283,0.00059981574,0.0012887171,0.0002432751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014110216,0.0022898603,0.016749335,0.006563677,0.00065347826,0.0031878855,0.0020796878,0.0020008723,0.003176775],"category_scores_gemma":[0.030801667,0.0012670808,0.029064855,0.009754958,0.0006705668,0.0017161188,0.0013639911,0.001611214,0.000260927],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011722331,0.00002712346,0.011825413,0.45825705,0.51456374,0.00017507472,0.00009804266,0.000695094,0.00019902767,0.00016371258,0.0010338218,0.011789771],"study_design_scores_gemma":[0.00039182205,0.00014258582,0.008031665,0.03901204,0.9499741,0.00011935291,0.00005095864,0.00031213448,0.00009424131,0.00021301908,0.0016317677,0.000026237038],"about_ca_topic_score_codex":0.008309112,"about_ca_topic_score_gemma":0.016671527,"teacher_disagreement_score":0.016749335,"about_ca_system_score_codex":0.0024857929,"about_ca_system_score_gemma":0.0045564272,"threshold_uncertainty_score":0.07462287},"labels":[],"label_agreement":null},{"id":"W4393317705","doi":"10.6000/1929-6029.2024.13.04","title":"Adaptive Elastic Net on High-Dimensional Sparse Data with Multicollinearity: Application to Lipomatous Tumor Classification","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Siriraj Foundation","keywords":"Multicollinearity; Elastic net regularization; Net (polyhedron); Pattern recognition (psychology); Artificial intelligence; Computer science; Mathematics; Statistics; Feature selection; Regression analysis; Geometry","score_opus":0.09876984620822425,"score_gpt":0.43053498212556957,"score_spread":0.3317651359173453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393317705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17306218,0.0007017308,0.8232465,0.0009443887,0.00007240893,0.000067089466,0.00015911282,0.00042821592,0.0013185426],"genre_scores_gemma":[0.8701496,0.00048626575,0.12622742,0.00019317503,0.000080935315,0.00011799762,0.0003625941,0.00007397663,0.0023079852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993125,0.0002960331,0.000046877576,0.00012328896,0.00015712906,0.000064126325],"domain_scores_gemma":[0.99707806,0.00213653,0.00024537995,0.00015094918,0.00032224765,0.00006697691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043605478,0.0006819964,0.0008621649,0.0012515188,0.0004957299,0.00078305247,0.0008885565,0.00092912285,0.00082988886],"category_scores_gemma":[0.0056064515,0.00033657413,0.000751802,0.0012601231,0.00072122284,0.0010191032,0.0012136648,0.001253107,0.00013255126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002333307,0.0001202741,0.0068686297,0.00009525658,0.00009967812,0.00022502389,0.000081902326,0.8717428,0.001662956,0.0041210656,0.00095269707,0.11379644],"study_design_scores_gemma":[0.0000039125716,0.000016513313,0.00035987012,0.0000038753883,0.000005418497,0.000014841238,0.000009190473,0.997678,0.00033328726,0.0014455279,0.00012519759,0.0000043163864],"about_ca_topic_score_codex":0.004005902,"about_ca_topic_score_gemma":0.0035117802,"teacher_disagreement_score":0.0043605478,"about_ca_system_score_codex":0.0005552441,"about_ca_system_score_gemma":0.0009587844,"threshold_uncertainty_score":0.023061037},"labels":[],"label_agreement":null},{"id":"W4398171400","doi":"10.6000/1929-6029.2024.13.05","title":"The Impact of the Risk Perception of COVID-19 PANDEMIC on College Students' Occupational Anxiety: The Moderating Effect of Career Adaptability","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Grit, Self-Efficacy, and Motivation","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptability; Anxiety; Pandemic; Coronavirus disease 2019 (COVID-19); Psychology; Perception; Risk perception; Clinical psychology; Medicine; Economics; Management; Psychiatry","score_opus":0.10219097913469904,"score_gpt":0.5347454043031903,"score_spread":0.43255442516849124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398171400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99919325,0.00007351676,0.000051199142,0.00015499716,0.0000071271297,0.000008986813,0.000017412689,0.0000017063157,0.0004918648],"genre_scores_gemma":[0.9997609,0.000044827513,0.00003289854,0.000028179544,0.0000050337303,0.000005105277,0.000016302538,3.4288334e-7,0.00010645879],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999283,0.00022601093,0.000055545315,0.000066044275,0.00014646849,0.00022296578],"domain_scores_gemma":[0.99638724,0.0009197049,0.0009660506,0.0001470973,0.00021618973,0.0013637876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077607145,0.00026714106,0.00022772413,0.00038986595,0.0005732668,0.0008357872,0.00020627493,0.00034398944,0.0028759595],"category_scores_gemma":[0.0041739945,0.00015538381,0.00055945036,0.00027420095,0.00044684068,0.00028722791,0.0009352793,0.00097021676,0.00012087338],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087534194,0.00036744864,0.9896919,0.00002941256,0.00007299582,0.00010914043,0.0011191268,0.000053632673,0.00060562056,0.00008039463,0.00010117808,0.0076815262],"study_design_scores_gemma":[0.0000026694481,0.00008286736,0.99864143,0.000008202507,0.000017476383,0.000021882346,0.0009604888,0.00007848141,0.000056220142,0.000030133857,0.00009692131,0.00000328354],"about_ca_topic_score_codex":0.0032096289,"about_ca_topic_score_gemma":0.006379794,"teacher_disagreement_score":0.0032096289,"about_ca_system_score_codex":0.00035073538,"about_ca_system_score_gemma":0.0010710756,"threshold_uncertainty_score":0.009621024},"labels":[],"label_agreement":null},{"id":"W4399320983","doi":"10.6000/1929-6029.2024.13.06","title":"Competing Risks Model to Evaluate Dropout Dynamics Among the Type 1 Diabetes Patients Registered with the Changing Diabetes in Children (CDiC) Program","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diabetes Management and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dropout (neural networks); Type 2 diabetes; Diabetes mellitus; Type 1 diabetes; Medicine; Computer science; Endocrinology; Machine learning","score_opus":0.06697737605967859,"score_gpt":0.44848758748667933,"score_spread":0.38151021142700076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399320983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74953943,0.0027380674,0.23719758,0.0030174216,0.000603171,0.0010345032,0.003207619,0.00047063883,0.002191572],"genre_scores_gemma":[0.96974045,0.0006040185,0.023060255,0.0002042134,0.0001645938,0.00077511993,0.002117543,0.000045473837,0.0032883447],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99254906,0.005366239,0.00030456952,0.0007415406,0.00029674056,0.0007418334],"domain_scores_gemma":[0.96352524,0.0317355,0.0020067391,0.0008078606,0.0010827879,0.0008417855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020261448,0.0014740279,0.0021680149,0.0023156616,0.00082411105,0.0017870697,0.0023989552,0.0018176654,0.006804434],"category_scores_gemma":[0.028321723,0.00051428337,0.0037943313,0.0013675835,0.0007485653,0.0010583663,0.0014521948,0.003272816,0.00041243056],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050983564,0.0017397774,0.507499,0.0008776759,0.0045515685,0.0013058733,0.0015899973,0.36478758,0.00069704844,0.033632323,0.0075462586,0.07067457],"study_design_scores_gemma":[0.00013956429,0.0007896788,0.01637691,0.000087721084,0.0005233272,0.00016283887,0.00040814563,0.974381,0.00013937581,0.005687214,0.0012629825,0.000041216823],"about_ca_topic_score_codex":0.018119141,"about_ca_topic_score_gemma":0.0068208496,"teacher_disagreement_score":0.020261448,"about_ca_system_score_codex":0.0014687966,"about_ca_system_score_gemma":0.002822327,"threshold_uncertainty_score":0.10715407},"labels":[],"label_agreement":null},{"id":"W4399497650","doi":"10.6000/1929-6029.2024.13.07","title":"Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Computational Techniques in Science and Engineering","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Correctness; Reliability (semiconductor); Computer science; Bayesian probability; Basis (linear algebra); Reliability engineering; Simplicity; Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.02260742139172137,"score_gpt":0.40139450823376954,"score_spread":0.37878708684204815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399497650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13793509,0.0007076909,0.85837114,0.0002241054,0.000043648062,0.00011362917,0.00026637604,0.0010333674,0.00130503],"genre_scores_gemma":[0.69789785,0.00030219936,0.3007956,0.000052256917,0.00005172012,0.00011931682,0.0003091318,0.000044021985,0.00042777252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868435,0.000424169,0.0001210454,0.0003188714,0.00036942065,0.00008216354],"domain_scores_gemma":[0.9959644,0.0026086138,0.0004912217,0.00030110238,0.0005388976,0.000095801945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019761333,0.000604699,0.0007856771,0.002117095,0.00030165593,0.0008800544,0.0006784119,0.00078754994,0.0008670552],"category_scores_gemma":[0.009975481,0.00029892358,0.0008234262,0.00076206005,0.0005367425,0.00067643216,0.0005384853,0.00055146666,0.00033999214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016027559,0.00026451616,0.06431567,0.00061295595,0.000340389,0.00062042114,0.0005528142,0.09983027,0.055632446,0.0056596952,0.0025155086,0.7680526],"study_design_scores_gemma":[0.000064505264,0.00045664504,0.051794555,0.000072557734,0.00024697676,0.0015776401,0.00021744425,0.89966404,0.030575572,0.01337754,0.0018551172,0.000097431155],"about_ca_topic_score_codex":0.0010509253,"about_ca_topic_score_gemma":0.0012756045,"teacher_disagreement_score":0.002117095,"about_ca_system_score_codex":0.00037551558,"about_ca_system_score_gemma":0.0006736847,"threshold_uncertainty_score":0.0104509},"labels":[],"label_agreement":null},{"id":"W4400654325","doi":"10.6000/1929-6029.2024.13.08","title":"Support of Characteristics, Physical Environmental and Psychological On Quality Of Life Of Patients With DM Type II","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Public Health and Nutrition","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Feeling; Quality of life (healthcare); Bivariate analysis; Psychology; Simple random sample; Marital status; Population; Logistic regression; Affect (linguistics); Observational study; Demography; Sample size determination; Gerontology; Clinical psychology; Medicine; Statistics; Social psychology; Mathematics; Environmental health","score_opus":0.10818806634203666,"score_gpt":0.5140419248824845,"score_spread":0.4058538585404478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400654325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989446,0.00028570072,0.000023159388,0.00012545305,0.000005590431,0.0000049513123,0.00009949322,8.3807095e-7,0.0005102737],"genre_scores_gemma":[0.9997009,0.00009576665,0.000035534238,0.000025564052,0.00000488404,0.000004155829,0.00006191592,2.969087e-7,0.00007098403],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99965763,0.000120957855,0.000040184797,0.00003499012,0.000072068986,0.00007412348],"domain_scores_gemma":[0.998738,0.0002248635,0.00056127354,0.000029778821,0.00011728389,0.0003289164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043702268,0.00013127028,0.00020939637,0.00042081042,0.00036736394,0.0004547559,0.0001496769,0.00022268776,0.0023744702],"category_scores_gemma":[0.0024988235,0.00008687047,0.00037488333,0.0004958877,0.00015313295,0.00020501157,0.00036679563,0.00052856654,0.00011641272],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041284988,0.0000724181,0.9973686,0.000015847969,0.000026839325,0.000043441516,0.000149289,0.000018608529,0.000056614237,0.000008283608,0.00007740994,0.0021213845],"study_design_scores_gemma":[0.0000040856066,0.00009195443,0.99917907,0.000012992008,0.000017441625,0.00008695583,0.00038469132,0.00006775285,0.000017308732,0.000013866239,0.00012172089,0.0000021449973],"about_ca_topic_score_codex":0.0018707669,"about_ca_topic_score_gemma":0.0032019718,"teacher_disagreement_score":0.0023744702,"about_ca_system_score_codex":0.00022747564,"about_ca_system_score_gemma":0.00033286947,"threshold_uncertainty_score":0.007943392},"labels":[],"label_agreement":null},{"id":"W4401216167","doi":"10.6000/1929-6029.2024.13.10","title":"Development of New Methods and Materials for the Restoration of Tooth Pulp","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Endodontics and Root Canal Treatments","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pulpitis; Dentistry; Pulp (tooth); Candida albicans; Medicine; Periodontitis; Biology; Microbiology","score_opus":0.18624756792443115,"score_gpt":0.5698387194494311,"score_spread":0.38359115152499995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401216167","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18988547,0.5987978,0.18835585,0.0025432694,0.0014688149,0.0004883953,0.00045208866,0.0005205693,0.017487738],"genre_scores_gemma":[0.3460639,0.2657654,0.3715401,0.001154732,0.0012289467,0.0006588073,0.0005518849,0.00008979602,0.012946372],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994393,0.000118294105,0.00004724394,0.000090084,0.00027166016,0.00003336623],"domain_scores_gemma":[0.99952304,0.00019800954,0.00010686654,0.0000480711,0.00009818762,0.000025786981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122097,0.00034596026,0.0004413101,0.0013752445,0.0001724898,0.0007045577,0.00048155285,0.00055853155,0.003955576],"category_scores_gemma":[0.00078597706,0.00023269904,0.000424501,0.00049159135,0.00049266766,0.00079356006,0.00043927605,0.00051473675,0.0010411489],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023742681,0.00046912342,0.0068387734,0.0045435885,0.000107236716,0.0003692897,0.00016277478,0.0010858501,0.263643,0.008840511,0.0018326122,0.7118698],"study_design_scores_gemma":[0.00018451927,0.0046881023,0.061416693,0.002198135,0.00042235234,0.009587993,0.0007458392,0.0087761665,0.2404777,0.023399383,0.64793193,0.00017125631],"about_ca_topic_score_codex":0.00014496311,"about_ca_topic_score_gemma":0.00022203411,"teacher_disagreement_score":0.003955576,"about_ca_system_score_codex":0.00021965703,"about_ca_system_score_gemma":0.00049265387,"threshold_uncertainty_score":0.013232708},"labels":[],"label_agreement":null},{"id":"W4401216249","doi":"10.6000/1929-6029.2024.13.09","title":"Body Mass Index and Metabolic Phenotypes in Breast Cancer Risk: A Meta-Analysis and Systematic Review","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Obesity; Medicine; Body mass index; Hazard ratio; Breast cancer; Internal medicine; Confidence interval; Oncology; Endocrinology; Physiology; Cancer","score_opus":0.06666655585383394,"score_gpt":0.47764492606166736,"score_spread":0.41097837020783345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401216249","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030977651,0.99559855,0.00030884775,0.00017570029,0.00007684119,0.00012636391,0.00045578578,0.000014517007,0.0001455332],"genre_scores_gemma":[0.09182045,0.9045362,0.001453669,0.00050326023,0.00022673294,0.0006179669,0.00064872694,0.000015446403,0.00017757206],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9941181,0.0026688175,0.0014841498,0.000720943,0.0007650426,0.0002428676],"domain_scores_gemma":[0.9825575,0.013326832,0.0023703706,0.0004798213,0.0010605174,0.00020503245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009740362,0.0022786094,0.014407049,0.0072703897,0.00063666434,0.002693024,0.0018048923,0.0018781939,0.0033738664],"category_scores_gemma":[0.023962576,0.0012185003,0.028322373,0.009559769,0.0006191207,0.0012578873,0.0011263577,0.0014600424,0.00026480758],"study_design_candidate":"meta_analysis","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013458834,0.000038359904,0.013550456,0.5236637,0.44069028,0.00023152267,0.00008813767,0.0008507429,0.00025983088,0.00017618375,0.0010922151,0.018012641],"study_design_scores_gemma":[0.0003396904,0.00019464456,0.01087199,0.048052136,0.937403,0.00019264694,0.00004787954,0.000286002,0.00012333953,0.00022468047,0.0022344678,0.000029483865],"about_ca_topic_score_codex":0.009056882,"about_ca_topic_score_gemma":0.016107982,"teacher_disagreement_score":0.014407049,"about_ca_system_score_codex":0.0021443916,"about_ca_system_score_gemma":0.004183954,"threshold_uncertainty_score":0.0515126},"labels":[],"label_agreement":null},{"id":"W4401216818","doi":"10.6000/1929-6029.2024.13.11","title":"Body Mass Index as a Risk Factor for Diffuse Large B-Cell Lymphoma: A Systematic Review and Meta-Analysis","year":2024,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Diffuse large B-cell lymphoma; Medicine; Body mass index; Internal medicine; Observational study; Confidence interval; Obesity; Oncology; MEDLINE; Lymphoma; Biology","score_opus":0.14649569348871014,"score_gpt":0.5319923021335837,"score_spread":0.38549660864487356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401216818","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002944581,0.99564517,0.0004040789,0.00019763825,0.00010898083,0.00022066546,0.00031211102,0.000016806525,0.0001500364],"genre_scores_gemma":[0.111599796,0.8826034,0.0024068393,0.0008085898,0.00028698714,0.0013716817,0.0006637373,0.0000227456,0.00023624959],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99011445,0.004616839,0.0026773948,0.0010109601,0.0012369696,0.00034333096],"domain_scores_gemma":[0.97674054,0.017534066,0.0031174242,0.00063146057,0.0016965751,0.0002798828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014494184,0.0026600068,0.01901531,0.008463919,0.0007879531,0.0035511744,0.0022025597,0.0025177798,0.0032569356],"category_scores_gemma":[0.035908036,0.0015431392,0.03452454,0.009581497,0.0007794154,0.0019441452,0.0013913574,0.0019110971,0.00027010372],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012119062,0.000035766647,0.007152474,0.48206466,0.49514177,0.00018415878,0.00009034075,0.00065726775,0.00021781249,0.00015483341,0.00072975684,0.012359248],"study_design_scores_gemma":[0.00034044858,0.00018066772,0.005233446,0.042847585,0.9489548,0.00014717164,0.000049622115,0.00026770696,0.00010306504,0.00020177347,0.0016457869,0.000027878761],"about_ca_topic_score_codex":0.007868204,"about_ca_topic_score_gemma":0.015279435,"teacher_disagreement_score":0.01901531,"about_ca_system_score_codex":0.0031828966,"about_ca_system_score_gemma":0.005410201,"threshold_uncertainty_score":0.07665348},"labels":[],"label_agreement":null},{"id":"W4401545222","doi":"10.6000/1929-6029.2024.13.13","title":"Assessment of the Awareness and Oral Hygiene Practices among Patients with Gum and Periodontal Diseases","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Oral hygiene; Gingivitis; Dentistry; Hygiene; Periodontitis; Bleeding on probing; Pathology","score_opus":0.037215120857589025,"score_gpt":0.4604083025391912,"score_spread":0.42319318168160214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401545222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994622,0.00012478416,0.00002894096,0.000017289298,0.0000020040536,0.000008077676,0.00003342288,0.0000013640273,0.00032193566],"genre_scores_gemma":[0.99968016,0.00006979184,0.0000588134,0.000011295124,0.0000038577446,0.0000069810435,0.00004905058,3.016209e-7,0.00011976064],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99966097,0.000088888184,0.000042613683,0.000040693063,0.000097656506,0.00006913613],"domain_scores_gemma":[0.99923134,0.0001741841,0.00029710977,0.00003428979,0.00011006448,0.00015305368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005214369,0.0001825223,0.00029654853,0.0007556155,0.00030319966,0.00040857363,0.00013031856,0.00042426251,0.0015829011],"category_scores_gemma":[0.0020249083,0.00021351752,0.0002694337,0.00042008597,0.00020043498,0.00035658714,0.0003458193,0.0003775778,0.0002006239],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008716519,0.00011521836,0.9963554,0.00002170659,0.000019562955,0.000052022213,0.0003757042,0.000015043115,0.0003751342,0.000006059032,0.000037437083,0.0025395807],"study_design_scores_gemma":[0.0000057711477,0.00042111854,0.998456,0.0000071754635,0.00001699452,0.0002582738,0.00055523356,0.0000743957,0.00008193992,0.000011645217,0.00010797807,0.0000033823064],"about_ca_topic_score_codex":0.0011926589,"about_ca_topic_score_gemma":0.0010677523,"teacher_disagreement_score":0.0015829011,"about_ca_system_score_codex":0.00015386667,"about_ca_system_score_gemma":0.00017982026,"threshold_uncertainty_score":0.0052952766},"labels":[],"label_agreement":null},{"id":"W4401545432","doi":"10.6000/1929-6029.2024.13.12","title":"Compliance with the NATO Standards in the Field of Psychological Assistance for the Servicemen with Post-Traumatic Stress Disorder","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Posttraumatic Stress Disorder Research","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anxiety; Descriptive statistics; Depression (economics); Psychology; Intrusion; Clinical psychology; Psychiatry; Traumatic stress; Scale (ratio); Service member; Military personnel; Statistics; Political science","score_opus":0.13542281242461324,"score_gpt":0.5611596455585738,"score_spread":0.42573683313396055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401545432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98228353,0.0009796531,0.002147501,0.0036867855,0.0002079006,0.00064823375,0.00026759657,0.00005715958,0.00972169],"genre_scores_gemma":[0.9899852,0.0007561589,0.00517601,0.00047151552,0.00008002202,0.00049750454,0.0003103776,0.000009788718,0.0027134155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99615014,0.0018332585,0.00048158516,0.00016670793,0.0010503243,0.00031800856],"domain_scores_gemma":[0.98948497,0.001668643,0.0039693075,0.0008590085,0.0027457697,0.0012722581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046691983,0.00015200679,0.00014412141,0.0005533548,0.00072930526,0.00064124557,0.00055944396,0.0003810025,0.0014374871],"category_scores_gemma":[0.018909471,0.00010567638,0.00019313765,0.0005952252,0.00039846846,0.00033729928,0.0009254823,0.00059535314,0.00029790364],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032781274,0.001068684,0.6767775,0.0004807313,0.00005047121,0.00025125768,0.005081611,0.0006990455,0.005862415,0.001018517,0.004999956,0.3033821],"study_design_scores_gemma":[0.000019747955,0.001152182,0.97731197,0.00024279596,0.00002168477,0.00036139874,0.0044756588,0.0005716228,0.0016058909,0.0002584431,0.013949941,0.000028651237],"about_ca_topic_score_codex":0.0067054387,"about_ca_topic_score_gemma":0.010710736,"teacher_disagreement_score":0.0067054387,"about_ca_system_score_codex":0.00096227124,"about_ca_system_score_gemma":0.0049496526,"threshold_uncertainty_score":0.02469343},"labels":[],"label_agreement":null},{"id":"W4401821039","doi":"10.6000/1929-6029.2024.13.14","title":"A Study on the Effects of Biodiversity and Conservation Efforts on Community Health in the Sunderban Area of Eastern India","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biodiversity; Biodiversity conservation; Geography; Socioeconomics; Environmental resource management; Environmental planning; Environmental science; Ecology; Sociology; Biology","score_opus":0.12899873092588765,"score_gpt":0.4759028444356667,"score_spread":0.34690411350977907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401821039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99951303,0.000033591867,0.00001287953,0.00004545621,0.0000014643055,0.000011757181,0.000016295107,6.4021725e-7,0.0003650055],"genre_scores_gemma":[0.99958223,0.00007470997,0.000057118246,0.00004522327,0.0000030871677,0.000018172712,0.000025711155,3.8889718e-7,0.00019342915],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928623,0.0003390403,0.000023575987,0.000050364033,0.00007381609,0.00022693607],"domain_scores_gemma":[0.9978975,0.0007666205,0.0004456472,0.000102503305,0.00018787748,0.0005998961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067445403,0.00021903048,0.00019821878,0.0009968693,0.0014813169,0.000914578,0.0005037676,0.00031086025,0.0009773299],"category_scores_gemma":[0.0017270172,0.00020155188,0.00036067032,0.0011732884,0.0011822867,0.00045004566,0.0011489349,0.00066364865,0.0000939408],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002686257,0.0015151763,0.9213568,0.00018069125,0.000082852486,0.0013485512,0.051572546,0.000096971504,0.0017542407,0.00032769187,0.0004073684,0.021088464],"study_design_scores_gemma":[0.000009779002,0.0004950738,0.9683957,0.000020097863,0.00002176077,0.0001555882,0.03027346,0.00005951189,0.000094433926,0.000026665923,0.00044071974,0.0000071482236],"about_ca_topic_score_codex":0.053065516,"about_ca_topic_score_gemma":0.101640485,"teacher_disagreement_score":0.053065516,"about_ca_system_score_codex":0.0013952823,"about_ca_system_score_gemma":0.0018287968,"threshold_uncertainty_score":0.105513275},"labels":[],"label_agreement":null},{"id":"W4402365816","doi":"10.6000/1929-6029.2024.13.15","title":"Influence of Stress Factors on the Development of Post-Traumatic Stress Disorder in Children: Risk Factors","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Resilience and Mental Health","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Traumatic stress; Stress (linguistics); Psychology; Clinical psychology; Developmental psychology; Linguistics; Philosophy","score_opus":0.05395451352435786,"score_gpt":0.4930364945035464,"score_spread":0.43908198097918855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402365816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970003,0.001837596,0.00010399115,0.00016684606,0.000015288033,0.0000115079,0.00025193245,0.0000031991779,0.0006092433],"genre_scores_gemma":[0.9982881,0.0011248956,0.00015407529,0.000034894136,0.000024060204,0.000012031038,0.00015437473,0.0000012065433,0.00020638839],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999828,0.000041642434,0.0000147741075,0.000033327673,0.000027507276,0.00005466418],"domain_scores_gemma":[0.9996387,0.000058220987,0.00017225946,0.000010337835,0.000045368808,0.000075061274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024329455,0.00021072166,0.0001683674,0.00039262147,0.0002984398,0.000381251,0.00018470496,0.00022316386,0.0018318065],"category_scores_gemma":[0.0006977282,0.00011223271,0.00025707774,0.00040105186,0.00017582702,0.00028488276,0.00020879986,0.00032306236,0.00012514643],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005884504,0.00004248283,0.9950157,0.000038451584,0.000029018414,0.00027721366,0.00019773173,0.000021289568,0.00017052353,0.000020277796,0.00008089156,0.004047696],"study_design_scores_gemma":[9.941044e-7,0.000058246813,0.9988115,0.000017536999,0.00001662824,0.00034201346,0.00044974659,0.000020727583,0.00006639497,0.000028621389,0.00018565463,0.0000020243324],"about_ca_topic_score_codex":0.0058087576,"about_ca_topic_score_gemma":0.008113983,"teacher_disagreement_score":0.0058087576,"about_ca_system_score_codex":0.0003062983,"about_ca_system_score_gemma":0.00055539946,"threshold_uncertainty_score":0.01154989},"labels":[],"label_agreement":null},{"id":"W4402365859","doi":"10.6000/1929-6029.2024.13.16","title":"The Relationship between Traumatic Experiences, the Prevalence of Social Anxiety and Insecure Attachment among University Students","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Posttraumatic Stress Disorder Research","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Anxiety; Insecure attachment; Social anxiety; Clinical psychology; Attachment theory; Developmental psychology; Psychiatry","score_opus":0.1606313967093912,"score_gpt":0.5297478671738614,"score_spread":0.36911647046447016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402365859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99964345,0.00006838945,0.000015646448,0.000028580205,0.0000023068192,0.0000032154999,0.0000117229965,4.100044e-7,0.00022632307],"genre_scores_gemma":[0.9997967,0.00009050007,0.000020098096,0.000010298815,0.0000038910657,0.0000034990132,0.000012889453,2.3584363e-7,0.000061855506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992824,0.00021014913,0.000108874,0.000053407697,0.00018290592,0.00016236914],"domain_scores_gemma":[0.9975126,0.00046373552,0.0010713151,0.000110504516,0.00020837742,0.000633599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007401786,0.00021409486,0.00025518177,0.001068909,0.0007508379,0.001039988,0.0002503046,0.0004053158,0.0019573895],"category_scores_gemma":[0.0041539175,0.00019579357,0.0003098687,0.0006756001,0.00054657855,0.0004570978,0.0009776843,0.0006650498,0.00015351555],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003526746,0.00009641713,0.9938066,0.000023893197,0.000027620386,0.00014036035,0.0016610991,0.000017334547,0.00024804595,0.00005060728,0.000043771895,0.0038489818],"study_design_scores_gemma":[9.3116415e-7,0.0001287644,0.9934196,0.00001877463,0.000011448518,0.00024310187,0.0059082177,0.00004203663,0.000049647406,0.000037436857,0.0001351402,0.000004858822],"about_ca_topic_score_codex":0.001814619,"about_ca_topic_score_gemma":0.0030447505,"teacher_disagreement_score":0.0019573895,"about_ca_system_score_codex":0.00028475295,"about_ca_system_score_gemma":0.0003971777,"threshold_uncertainty_score":0.0065481067},"labels":[],"label_agreement":null},{"id":"W4402365889","doi":"10.6000/1929-6029.2024.13.17","title":"The Chronic Progressive Repeated Measures (CPRM) Model for Clinical Trials Comparing Change Over Time in Quantitative Trait Outcomes","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging","keywords":"Trait; Statistics; Psychology; Computer science; Mathematics","score_opus":0.9057898403375169,"score_gpt":0.71960631360349,"score_spread":0.18618352673402683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402365889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029082117,0.0019463414,0.96973,0.0021643303,0.001075527,0.015011014,0.0022642082,0.001771483,0.003128928],"genre_scores_gemma":[0.06499528,0.0017447899,0.7832758,0.0029063348,0.00057819684,0.13690521,0.0028622397,0.00036090758,0.00637117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.7028521,0.26345485,0.009035683,0.010291517,0.012557114,0.0018088099],"domain_scores_gemma":[0.84543824,0.1113672,0.013813361,0.02263211,0.0059222546,0.0008268698],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.26392585,0.004924873,0.0068593523,0.0038478023,0.0011895524,0.0031887526,0.006225238,0.006217715,0.019239275],"category_scores_gemma":[0.23507836,0.001971297,0.0126154125,0.0037060871,0.0037531897,0.0041664797,0.0036055243,0.011732973,0.0063860724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0104196,0.00094399025,0.011372545,0.010176154,0.0130246645,0.0012532654,0.0014127981,0.10124374,0.0020291992,0.5370241,0.06982736,0.24127261],"study_design_scores_gemma":[0.01028443,0.009558075,0.007253771,0.002485545,0.0054232934,0.0010653347,0.00017291743,0.4759241,0.003146647,0.39313647,0.0909797,0.00056970725],"about_ca_topic_score_codex":0.0031094784,"about_ca_topic_score_gemma":0.0023146889,"teacher_disagreement_score":0.73607415,"about_ca_system_score_codex":0.0035264536,"about_ca_system_score_gemma":0.0076721017,"threshold_uncertainty_score":0.9077105},"labels":[],"label_agreement":null},{"id":"W4402848922","doi":"10.6000/1929-6029.2024.13.18","title":"The Impact of Practical Skills on Improving the Servicemen’s Preparedness to Act in Case of Radiation Contamination of the Area","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare, Law, Governance, and Management Studies","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preparedness; Contamination; Radiation; Psychology; Environmental science; Political science; Law; Optics; Physics; Biology","score_opus":0.09548294129498289,"score_gpt":0.5861829254238945,"score_spread":0.4906999841289116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402848922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894047,0.00009177045,0.0007167304,0.0007179485,0.000017752433,0.000055600685,0.00002596119,0.00002218927,0.008947271],"genre_scores_gemma":[0.9974744,0.00016923166,0.0007909996,0.00007452334,0.0000053944955,0.000021115995,0.000027029722,0.0000025171641,0.0014347432],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99906355,0.00037669286,0.00003001017,0.000045655994,0.0001711939,0.00031299272],"domain_scores_gemma":[0.9969415,0.0010740743,0.00040905454,0.00012724967,0.00038885925,0.0010591935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009929152,0.0003169115,0.00012490775,0.00031027012,0.0005227851,0.0007414984,0.0003239256,0.00044835577,0.0065355985],"category_scores_gemma":[0.0041007255,0.00008990076,0.0002772062,0.00018445485,0.0005427646,0.00037131232,0.000967461,0.00066351047,0.0006875107],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000893465,0.008689065,0.3119703,0.0017494389,0.00012486482,0.0018699195,0.02540143,0.005918713,0.028208662,0.0029889953,0.006323634,0.60586154],"study_design_scores_gemma":[0.00009794012,0.008680051,0.900539,0.0005581783,0.00010806228,0.000750604,0.045114778,0.0052698916,0.013498321,0.0022942538,0.023004323,0.00008449748],"about_ca_topic_score_codex":0.002270111,"about_ca_topic_score_gemma":0.0034490726,"teacher_disagreement_score":0.0065355985,"about_ca_system_score_codex":0.00061662414,"about_ca_system_score_gemma":0.0021758191,"threshold_uncertainty_score":0.021863699},"labels":[],"label_agreement":null},{"id":"W4403255837","doi":"10.6000/1929-6029.2024.13.19","title":"Process Capability Indices for Processes when the Underlying Data are Interval-Valued","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interval (graph theory); Process (computing); Interval data; Upper and lower bounds; Mathematics; Computation; Algorithm; Data mining; Computer science; Statistics; Measure (data warehouse); Mathematical analysis; Combinatorics","score_opus":0.5978024727038904,"score_gpt":0.6589394964474958,"score_spread":0.06113702374360541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403255837","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05918194,0.0004857745,0.9349671,0.00024180942,0.00004341925,0.00016326124,0.0005195271,0.0003131351,0.00408412],"genre_scores_gemma":[0.72947514,0.0005350746,0.26717094,0.00009862269,0.0001318711,0.00053500244,0.0011170242,0.0000945016,0.00084191695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931647,0.0019461107,0.0009777412,0.0012485399,0.0023179925,0.0003448666],"domain_scores_gemma":[0.91325593,0.06212587,0.012924872,0.004961338,0.005736337,0.000995746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014617386,0.0009851692,0.0009712064,0.005257315,0.00056689704,0.0033397374,0.0014470051,0.0011460372,0.002156373],"category_scores_gemma":[0.095531344,0.00033457924,0.0011388102,0.0041185026,0.0020804696,0.006516492,0.0018959823,0.0029373346,0.0003841712],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043089243,0.00022064395,0.048696373,0.00090357737,0.00032005773,0.0008395963,0.0015965007,0.28898972,0.008785286,0.46070305,0.0023494214,0.1861648],"study_design_scores_gemma":[0.000021660118,0.0003157565,0.01689509,0.00025863785,0.000073208255,0.0004968353,0.00042588572,0.74819094,0.0049638986,0.22354352,0.0046859984,0.00012866498],"about_ca_topic_score_codex":0.0013655059,"about_ca_topic_score_gemma":0.00080282416,"teacher_disagreement_score":0.014617386,"about_ca_system_score_codex":0.001557338,"about_ca_system_score_gemma":0.0010529391,"threshold_uncertainty_score":0.07730502},"labels":[],"label_agreement":null},{"id":"W4403282198","doi":"10.6000/1929-6029.2024.13.20","title":"Estimating Optimum Length of Stay in a Hospital to Control the Infection Spread during an Epidemic Using Left-Right Truncated Poisson Model","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Poisson distribution; Epidemic model; Mathematics; Statistics; Poisson regression; Applied mathematics; Demography","score_opus":0.11562617245549857,"score_gpt":0.5641770184976034,"score_spread":0.4485508460421048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403282198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3866642,0.00079522905,0.60801595,0.0009053541,0.00007982664,0.0001942949,0.0006854499,0.00021726072,0.0024424312],"genre_scores_gemma":[0.9651985,0.00034293148,0.032265082,0.00005600579,0.000039540326,0.00015496378,0.0005380734,0.000021205627,0.0013835516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989365,0.0004382796,0.000059738075,0.00023853974,0.00011163394,0.00021539934],"domain_scores_gemma":[0.9952415,0.0035413434,0.00060884224,0.000087798,0.00035379053,0.00016675041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002840194,0.0007060873,0.0011933532,0.00084217516,0.0003308134,0.00096346193,0.001375775,0.0010805498,0.0017868188],"category_scores_gemma":[0.0065821097,0.0005499261,0.0012240706,0.0006200939,0.0004165747,0.000880715,0.00070367364,0.0010756586,0.00020935645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078122925,0.00005925502,0.0069044307,0.00007272856,0.000049664915,0.000073267314,0.00005086451,0.9834509,0.00045137844,0.002057002,0.00035351017,0.006398823],"study_design_scores_gemma":[0.0000052726155,0.00004980816,0.0011114323,0.0000069877533,0.000011843727,0.000014692462,0.000029845205,0.99770314,0.00010062408,0.00088917214,0.0000708851,0.000006323893],"about_ca_topic_score_codex":0.01767891,"about_ca_topic_score_gemma":0.008257317,"teacher_disagreement_score":0.01767891,"about_ca_system_score_codex":0.0012910664,"about_ca_system_score_gemma":0.0023096136,"threshold_uncertainty_score":0.035152018},"labels":[],"label_agreement":null},{"id":"W4403380110","doi":"10.6000/1929-6029.2024.13.21","title":"Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Feature (linguistics); Computer science; Selection (genetic algorithm); Database; Health care; Data mining; Artificial intelligence; Pattern recognition (psychology); Economics","score_opus":0.09428672363252123,"score_gpt":0.4837787701549981,"score_spread":0.3894920465224769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403380110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63195956,0.005576985,0.3491093,0.0022369297,0.00042617513,0.0005215222,0.0039819647,0.0022444697,0.003943136],"genre_scores_gemma":[0.8854127,0.001019074,0.10609616,0.00036754878,0.000103750426,0.00050848455,0.0051410613,0.00012333106,0.0012278934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929367,0.003803495,0.0006628013,0.0012883905,0.0009828272,0.0003257701],"domain_scores_gemma":[0.9812758,0.014681541,0.0005770295,0.0016407694,0.0016190223,0.00020582596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016761307,0.001283712,0.0015081234,0.0018348119,0.0007779451,0.0018995762,0.0015156103,0.0010579051,0.0017912724],"category_scores_gemma":[0.037895214,0.00034588427,0.0021028016,0.0022532458,0.0005485386,0.0021577,0.0014170975,0.0013386027,0.00066152844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003616667,0.00097336073,0.13302872,0.0009559539,0.0019668194,0.0006281966,0.0005258294,0.3980001,0.0029651194,0.0049725566,0.0140126925,0.43835402],"study_design_scores_gemma":[0.000105803,0.00044532196,0.022808574,0.00011100904,0.00023475348,0.00026378746,0.00025898722,0.96615,0.001981221,0.005314234,0.002268316,0.000057989804],"about_ca_topic_score_codex":0.011072771,"about_ca_topic_score_gemma":0.0061774785,"teacher_disagreement_score":0.016761307,"about_ca_system_score_codex":0.0008979209,"about_ca_system_score_gemma":0.001888865,"threshold_uncertainty_score":0.08864331},"labels":[],"label_agreement":null},{"id":"W4403491292","doi":"10.6000/1929-6029.2024.13.22","title":"The Effect of Educational Media Website and Surveillance on Risk Behavior for Prevention of Premarital Sex and Sexual Violence in Adolescents in Gorontalo Regency High School","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adolescent Health and Behaviors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Premarital sex; Sexual behavior; Risky sexual behavior; Social psychology; Demography; Sexually active; Sociology","score_opus":0.042641329762284574,"score_gpt":0.519201306239877,"score_spread":0.47655997647759246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403491292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994646,0.00015909963,0.000009025434,0.000047499936,0.0000060418424,0.000033747318,0.000015880107,0.0000013460965,0.00026273832],"genre_scores_gemma":[0.99910295,0.00026415446,0.00013438065,0.000033591445,0.000010096836,0.00007304031,0.000029979457,5.436406e-7,0.00035122415],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99926454,0.00030994773,0.000052153508,0.00006483043,0.00014311071,0.00016547517],"domain_scores_gemma":[0.9981729,0.0005512007,0.00047742156,0.000056189263,0.00011254879,0.0006297747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011214691,0.00028748132,0.00038815785,0.0005034997,0.00047482352,0.00050612475,0.00032843236,0.000410836,0.0021866227],"category_scores_gemma":[0.0029111344,0.00022774801,0.0005855162,0.0002524184,0.0003058842,0.00026018595,0.000456787,0.00061063044,0.00014780012],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002220513,0.025407763,0.8982991,0.0005899095,0.0002403028,0.00027625152,0.0042370707,0.00013036473,0.0019685323,0.0001368105,0.00038111585,0.0661123],"study_design_scores_gemma":[0.00011981662,0.006460067,0.990054,0.0000857744,0.00012212365,0.00004674534,0.0019718567,0.0001300988,0.00038564592,0.000018938119,0.0005980542,0.0000069614584],"about_ca_topic_score_codex":0.005348989,"about_ca_topic_score_gemma":0.010374838,"teacher_disagreement_score":0.005348989,"about_ca_system_score_codex":0.0004625941,"about_ca_system_score_gemma":0.0010139573,"threshold_uncertainty_score":0.010635674},"labels":[],"label_agreement":null},{"id":"W4404668027","doi":"10.6000/1929-6029.2024.13.24","title":"Sample Size and Statistical Power Calculation in Multivariable Analyses: Development and Implementation of \"SampleSizeMulti\" Packages in R","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Sample size determination; Sample (material); Power (physics); Statistical power; Statistics; Computer science; Econometrics; Mathematics; Engineering; Control engineering; Physics; Chemistry; Chromatography","score_opus":0.16341242151178634,"score_gpt":0.5796363033632727,"score_spread":0.4162238818514863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404668027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014279948,0.0002805821,0.97981334,0.0007395541,0.00021527756,0.0019035547,0.0027401529,0.010850515,0.0020289898],"genre_scores_gemma":[0.010952667,0.000255826,0.96998626,0.00043744117,0.00010312901,0.011167385,0.0012087229,0.0052131326,0.0006754139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92662865,0.054406606,0.005891144,0.0040138727,0.008186216,0.0008735027],"domain_scores_gemma":[0.7681616,0.18660997,0.012593636,0.01677735,0.014574568,0.001282838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07359876,0.002773345,0.0022968047,0.0042396924,0.0008547295,0.003534173,0.004113356,0.0016419815,0.022848012],"category_scores_gemma":[0.31123218,0.0020664965,0.0034819185,0.004228942,0.0022599667,0.0024848,0.00531219,0.0052829776,0.01055615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011061013,0.0002807707,0.010773943,0.006510678,0.0018150382,0.0005213395,0.0029327795,0.030726751,0.003962329,0.09001481,0.19088374,0.66047174],"study_design_scores_gemma":[0.0018852638,0.0010623705,0.016489541,0.0038683123,0.0012729069,0.0014051251,0.00063860946,0.20432423,0.028628428,0.24783891,0.4918477,0.00073858694],"about_ca_topic_score_codex":0.001904895,"about_ca_topic_score_gemma":0.0020567176,"teacher_disagreement_score":0.07359876,"about_ca_system_score_codex":0.0013596573,"about_ca_system_score_gemma":0.0067736446,"threshold_uncertainty_score":0.3892321},"labels":[],"label_agreement":null},{"id":"W4404668065","doi":"10.6000/1929-6029.2024.13.23","title":"The Impact of Group Psychotherapy on the Mental Health of Servicemen with Post-Traumatic Stress Disorder","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Posttraumatic Stress Disorder Research","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mental health; Psychology; Traumatic stress; Clinical psychology; Psychotherapist; Psychiatry; Group psychotherapy","score_opus":0.08308685217334537,"score_gpt":0.5355177021290888,"score_spread":0.4524308499557434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404668065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978855,0.0004279098,0.00006689308,0.00034197673,0.000045871675,0.00004248027,0.000010700955,0.000006913715,0.0011717876],"genre_scores_gemma":[0.9987478,0.0004988112,0.00025672218,0.00010218317,0.000050884613,0.000043000644,0.000017684308,0.0000012169958,0.00028172974],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996524,0.00018547756,0.000009627613,0.000018631139,0.000037602465,0.00009614735],"domain_scores_gemma":[0.99949026,0.00016770202,0.0000569363,0.000029200828,0.000027046508,0.0002287742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042098478,0.00020613591,0.00031281437,0.0002777692,0.0007663366,0.00022519508,0.00024183937,0.00037016737,0.0024146584],"category_scores_gemma":[0.001482856,0.000082656385,0.00027605417,0.00020917547,0.00034043114,0.00015454748,0.0004520654,0.0004491741,0.00015483421],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014563951,0.05704444,0.07105355,0.0009476578,0.000666935,0.0012571636,0.008340387,0.0012692162,0.017288737,0.00060293946,0.0046261214,0.82233894],"study_design_scores_gemma":[0.0036157595,0.09666685,0.8696793,0.0003351927,0.00073154084,0.00088694325,0.014211637,0.0016680371,0.0041581336,0.0010841425,0.00692258,0.000039917562],"about_ca_topic_score_codex":0.0014926512,"about_ca_topic_score_gemma":0.0035152393,"teacher_disagreement_score":0.0024146584,"about_ca_system_score_codex":0.0003064592,"about_ca_system_score_gemma":0.0004722254,"threshold_uncertainty_score":0.0080778},"labels":[],"label_agreement":null},{"id":"W4404718523","doi":"10.6000/1929-6029.2024.13.25","title":"A Novel RPCA Method Using Log-Weighted Nuclear and L_(2,1) Norms Combined with Contrast-Limited Adaptive Histogram Equalization (CLAHE) for High Dimensional Natural and Medical Image Data","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Department of Science and Technology, Ministry of Science and Technology, India; South African Medical Research Council; National Research Foundation","keywords":"Adaptive histogram equalization; Histogram equalization; Contrast (vision); Artificial intelligence; Histogram; Pattern recognition (psychology); Computer science; Image (mathematics); Histogram matching; Mathematics","score_opus":0.05129275047470889,"score_gpt":0.4004171838678205,"score_spread":0.3491244333931116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404718523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013458895,0.00015828476,0.9978186,0.000091365386,0.00004248585,0.000023364955,0.000014175185,0.00023332903,0.000272503],"genre_scores_gemma":[0.064525016,0.000574254,0.93041235,0.00026997906,0.0001712498,0.00020772175,0.0002847732,0.00025406247,0.0033005155],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990024,0.00027089735,0.0000742673,0.00027893763,0.00030635335,0.00006718231],"domain_scores_gemma":[0.99885976,0.00043467857,0.0001163239,0.00015021594,0.00037019484,0.00006885709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016294221,0.0011475387,0.0011783179,0.001104192,0.00062332023,0.0013446093,0.0017864871,0.0015928335,0.0018163975],"category_scores_gemma":[0.0043525617,0.0005594021,0.0016745237,0.0009539007,0.0011817805,0.0018001066,0.0013862718,0.002252141,0.0008019361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018795732,0.00013538264,0.0008065081,0.000291537,0.00022769073,0.00019717924,0.00016526385,0.27961972,0.028607912,0.020797668,0.009456998,0.65950614],"study_design_scores_gemma":[0.0000101560945,0.000037068334,0.00014656982,0.000012694538,0.000018618772,0.00010695711,0.000011084681,0.98964185,0.0036601417,0.0034051167,0.0029256963,0.000024001483],"about_ca_topic_score_codex":0.004659374,"about_ca_topic_score_gemma":0.004789086,"teacher_disagreement_score":0.004659374,"about_ca_system_score_codex":0.0005024267,"about_ca_system_score_gemma":0.002289845,"threshold_uncertainty_score":0.009264529},"labels":[],"label_agreement":null},{"id":"W4404852814","doi":"10.6000/1929-6029.2024.13.26","title":"Elimination Diet Guided by Food-Specific IgG Antibodies Measurements in Chronic Adult Acne in Thailand: A Prospective RCT Study","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Acne and Rosacea Treatments and Effects","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Acne; Randomized controlled trial; Medicine; Antibody; Food science; Environmental health; Immunology; Internal medicine; Biology; Dermatology","score_opus":0.09266199461967888,"score_gpt":0.47075417493191896,"score_spread":0.37809218031224007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404852814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97799426,0.0114112515,0.00055883627,0.00030664104,0.00021743008,0.007628566,0.0007269152,0.000021034899,0.0011351033],"genre_scores_gemma":[0.987323,0.0030268587,0.001259061,0.0004676788,0.00015875237,0.0070826006,0.00030933754,0.000003565941,0.00036910272],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9943645,0.0035917622,0.000842455,0.0005138418,0.00032475108,0.00036271155],"domain_scores_gemma":[0.9942736,0.0018299377,0.0019874074,0.00040636357,0.0006106238,0.00089201477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059716254,0.00095611444,0.0024981308,0.00041422172,0.0008084668,0.001106293,0.0005344009,0.0015419238,0.004503561],"category_scores_gemma":[0.0061062113,0.0006773553,0.0032546967,0.00097080716,0.0009851162,0.001062566,0.0006809871,0.0014658448,0.0003453039],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.8183041,0.034144647,0.078681365,0.014179061,0.013383587,0.00030519097,0.0008397732,0.00045078542,0.0016105218,0.000264118,0.0011050054,0.03673182],"study_design_scores_gemma":[0.57961047,0.29501095,0.09719702,0.002161228,0.0204669,0.00028577563,0.000931401,0.00096989895,0.0005339603,0.0003671259,0.0023720178,0.00009329967],"about_ca_topic_score_codex":0.002989895,"about_ca_topic_score_gemma":0.004995857,"teacher_disagreement_score":0.0059716254,"about_ca_system_score_codex":0.0009952276,"about_ca_system_score_gemma":0.0036232015,"threshold_uncertainty_score":0.031581342},"labels":[],"label_agreement":null},{"id":"W4404852818","doi":"10.6000/1929-6029.2024.13.28","title":"The Association between Sweet Sugar Beverage Intakes and the Quality of Sleep in Working Age Adults","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Sleep and related disorders","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sugar; Association (psychology); Sleep quality; Food science; Quality (philosophy); Environmental health; Medicine; Psychology; Biology; Insomnia; Psychiatry; Physics","score_opus":0.06096982212054353,"score_gpt":0.4691849876295167,"score_spread":0.4082151655089732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404852818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990397,0.000380426,0.000035432284,0.000032436288,0.0000046945906,0.000007012131,0.00023763046,0.0000012438595,0.0002614725],"genre_scores_gemma":[0.9992907,0.0002721046,0.00006789911,0.000026232497,0.000008517046,0.0000091702095,0.00020892014,6.7024337e-7,0.00011574894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997739,0.000046792804,0.000052776,0.00003549801,0.00005259983,0.00003840429],"domain_scores_gemma":[0.9985122,0.0002196916,0.000885668,0.000057580284,0.00013630156,0.00018849228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054862665,0.00018603072,0.00019209339,0.0005949504,0.00031529198,0.00041840842,0.00021345909,0.0002542633,0.0014462508],"category_scores_gemma":[0.0014448108,0.00018992917,0.00033901935,0.00074875576,0.00017726717,0.00033310187,0.00028573742,0.00033140878,0.00012566961],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046719084,0.000030770134,0.999035,0.000015961416,0.00004016283,0.000018971688,0.0000663736,0.0000074531927,0.000086818094,0.0000031021298,0.000026837626,0.0006218237],"study_design_scores_gemma":[0.0000015766553,0.000060952025,0.9996815,0.0000057132224,0.000014003344,0.000039285038,0.00010345778,0.00002734559,0.000013613692,0.0000041863177,0.000047579437,8.659543e-7],"about_ca_topic_score_codex":0.005657918,"about_ca_topic_score_gemma":0.009479662,"teacher_disagreement_score":0.005657918,"about_ca_system_score_codex":0.00014954351,"about_ca_system_score_gemma":0.0002062179,"threshold_uncertainty_score":0.0112499595},"labels":[],"label_agreement":null},{"id":"W4404852962","doi":"10.6000/1929-6029.2024.13.27","title":"Evaluating the Psychological Impact of Forest Bathing: A Meta-Analysis of Emotional State Outcomes","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bathing; Meta-analysis; Psychology; Clinical psychology; Applied psychology; Geography; Medicine","score_opus":0.39021775424736277,"score_gpt":0.6121654533170204,"score_spread":0.22194769906965767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404852962","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01841034,0.97916335,0.0008247126,0.00025115965,0.00015231917,0.00038393558,0.0004720703,0.000029587982,0.00031259438],"genre_scores_gemma":[0.42609236,0.56566143,0.0042186137,0.00084690546,0.00026857486,0.0015665676,0.0010089603,0.00004034938,0.00029619996],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99008346,0.005348696,0.0025599613,0.000750414,0.0010177991,0.0002396629],"domain_scores_gemma":[0.9739319,0.01946222,0.0040902374,0.00085670524,0.001361372,0.00029745323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017390259,0.0026017297,0.013239855,0.0057940246,0.00062780106,0.003230546,0.0016955636,0.0017826485,0.0023937046],"category_scores_gemma":[0.032333005,0.0010490518,0.042544197,0.005052667,0.0008093297,0.0013717231,0.0013407021,0.0018230025,0.00019189641],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036149102,0.00008230644,0.0049730954,0.20226108,0.77404976,0.000084891915,0.0001084076,0.0005228885,0.00036262977,0.00009077579,0.0002530352,0.013596251],"study_design_scores_gemma":[0.00080051814,0.000526843,0.0055406108,0.015498417,0.97631776,0.000071891984,0.00004844345,0.00014056667,0.00017466398,0.0001174222,0.0007461934,0.000016637263],"about_ca_topic_score_codex":0.004787759,"about_ca_topic_score_gemma":0.010071771,"teacher_disagreement_score":0.017390259,"about_ca_system_score_codex":0.0022907788,"about_ca_system_score_gemma":0.002661014,"threshold_uncertainty_score":0.09196961},"labels":[],"label_agreement":null},{"id":"W4405005480","doi":"10.6000/1929-6029.2024.13.30","title":"The Interaction between Self-Esteem, Perceived Gender Discrimination and Employment Motivation: A Log-Linear Analysis","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Self-esteem; Psychology; Social psychology; Welfare; Personal development; Political science","score_opus":0.27885979211386125,"score_gpt":0.5029842865103996,"score_spread":0.22412449439653837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405005480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952075,0.00025663147,0.0014836644,0.00023134894,0.00004344464,0.000046307443,0.0007916107,0.000100291036,0.0018392358],"genre_scores_gemma":[0.99661934,0.00008809177,0.00062025985,0.00003670667,0.000023293138,0.000121140976,0.00048124348,0.00001801163,0.001991893],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99780756,0.0011444812,0.000110221605,0.00025220666,0.00036679252,0.00031866922],"domain_scores_gemma":[0.9865107,0.010433713,0.0010957987,0.0005597865,0.0005474669,0.00085254665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003294949,0.0005809064,0.0006516867,0.0012419748,0.0005229548,0.0013073307,0.00092699716,0.00076169195,0.012907253],"category_scores_gemma":[0.009377553,0.0002817019,0.0019480851,0.0011110631,0.00058491935,0.00087821105,0.0008186895,0.0021817426,0.0016383718],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009327066,0.00084842945,0.98072374,0.00006466954,0.00070208736,0.00021361973,0.0007378489,0.0007523091,0.0007009186,0.00036268766,0.0008816206,0.013079377],"study_design_scores_gemma":[0.000028758326,0.001696549,0.97947115,0.000041359377,0.00024090675,0.0003320158,0.0016459299,0.013888509,0.00037502404,0.00035041262,0.0018903264,0.000039098457],"about_ca_topic_score_codex":0.004282991,"about_ca_topic_score_gemma":0.0021504264,"teacher_disagreement_score":0.012907253,"about_ca_system_score_codex":0.0004661766,"about_ca_system_score_gemma":0.0012346379,"threshold_uncertainty_score":0.043179095},"labels":[],"label_agreement":null},{"id":"W4405005507","doi":"10.6000/1929-6029.2024.13.29","title":"Building Professional Competence of Prison Staff: Psychological, Pedagogical, and Legal Aspects","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Educational Methods and Teacher Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prison; Professional development; Competence (human resources); Psychology; Coping (psychology); Professional studies; Personality; Medical education; Applied psychology; Pedagogy; Social psychology; Medicine; Clinical psychology; Criminology","score_opus":0.2322692845298456,"score_gpt":0.5791540724326167,"score_spread":0.3468847879027711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405005507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99676275,0.00037666553,0.0001692258,0.0003325931,0.000009327323,0.00001384164,0.000020412555,0.0000046836767,0.0023104683],"genre_scores_gemma":[0.99926573,0.00016898401,0.00017513221,0.000028035905,0.0000033862502,0.0000051917277,0.000014740994,7.284954e-7,0.00033812487],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.999423,0.00020853114,0.000041802152,0.000030910283,0.00015329386,0.00014240877],"domain_scores_gemma":[0.9983713,0.00026761348,0.00039641492,0.00004527889,0.00031374107,0.0006056117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010814273,0.00013364895,0.00015950289,0.0006831384,0.00074462447,0.00082513405,0.00016287876,0.00014677265,0.0011447868],"category_scores_gemma":[0.003574923,0.00009085246,0.00014026863,0.00020134445,0.0005869463,0.0003010015,0.0009620512,0.00031758205,0.00012565739],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066276625,0.00034009767,0.8573874,0.00030452144,0.000050507948,0.00056724827,0.036222827,0.00029608296,0.0032029117,0.0009275776,0.0016554375,0.09897909],"study_design_scores_gemma":[0.0000022816623,0.00016462528,0.97340924,0.00014978086,0.000009135097,0.00033200113,0.022356896,0.0002252877,0.00049880386,0.0002253627,0.0026157207,0.000010871902],"about_ca_topic_score_codex":0.0058977157,"about_ca_topic_score_gemma":0.008983573,"teacher_disagreement_score":0.0058977157,"about_ca_system_score_codex":0.0007905313,"about_ca_system_score_gemma":0.0026883946,"threshold_uncertainty_score":0.011726737},"labels":[],"label_agreement":null},{"id":"W4405449871","doi":"10.6000/1929-6029.2024.13.31","title":"The Effect of Regulation and Organizational Commitment on the Successful Handling of Covid-19 with Job Satisfaction Mediation","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Employee Performance and Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Job satisfaction; Mediation; Organizational commitment; Government (linguistics); Psychology; Coronavirus disease 2019 (COVID-19); Work (physics); Business; Social psychology; Medicine; Political science; Engineering","score_opus":0.03570434928428455,"score_gpt":0.4392669549586549,"score_spread":0.40356260567437036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405449871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9806954,0.00034112085,0.0018382592,0.00062431087,0.000056081197,0.000130105,0.00015263245,0.000036464135,0.016125493],"genre_scores_gemma":[0.99797827,0.000121741454,0.0006177496,0.000077787714,0.000014190075,0.0000978877,0.00006125299,0.000010441285,0.0010207631],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928837,0.003336099,0.00036311796,0.0008206067,0.0013777358,0.0012188178],"domain_scores_gemma":[0.9773959,0.013887104,0.003332606,0.0015103116,0.0016178511,0.002256292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043640723,0.00064119266,0.00058803515,0.00078518735,0.0014018667,0.0024272385,0.0009673817,0.000572894,0.009782993],"category_scores_gemma":[0.0215244,0.00035628845,0.00081012794,0.00075028714,0.0013030575,0.000797314,0.0032362693,0.0022083456,0.0005204942],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005091574,0.0017610419,0.92343414,0.0003576515,0.00075636304,0.000793107,0.010754598,0.00054582744,0.0043905023,0.008681542,0.0009239444,0.047092203],"study_design_scores_gemma":[0.000051879015,0.00048230664,0.983772,0.00015664208,0.00033265812,0.00028239144,0.0066897594,0.0018390305,0.0019013374,0.0021075902,0.0023486668,0.000035759822],"about_ca_topic_score_codex":0.004298049,"about_ca_topic_score_gemma":0.0029034228,"teacher_disagreement_score":0.009782993,"about_ca_system_score_codex":0.0009009874,"about_ca_system_score_gemma":0.004248642,"threshold_uncertainty_score":0.03272742},"labels":[],"label_agreement":null},{"id":"W4405578450","doi":"10.6000/1929-6029.2024.13.33","title":"Pharmaceutical Analytics: Methods of Analysis of Medicinal Products and their Quality Control","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Pharmaceutical Quality and Counterfeiting","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Population; Medicine; Business; Risk analysis (engineering); Traditional medicine; Environmental health","score_opus":0.2986388761040612,"score_gpt":0.6530571275702409,"score_spread":0.3544182514661797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405578450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033504155,0.07734277,0.829942,0.003445048,0.0012946285,0.002520225,0.004819151,0.0025702824,0.044561766],"genre_scores_gemma":[0.28691098,0.053108424,0.63496524,0.0017104936,0.0012095857,0.0026523257,0.003918284,0.0005726839,0.014952049],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97867143,0.0063230563,0.001543024,0.0022305662,0.010893806,0.00033809268],"domain_scores_gemma":[0.9858031,0.0058592837,0.0022926333,0.0018395045,0.0040193456,0.00018617054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011061458,0.0014605696,0.0017397542,0.0076690232,0.0009868109,0.0049753906,0.0014270466,0.0012222817,0.0038251122],"category_scores_gemma":[0.01698605,0.00057671976,0.0017242138,0.007100546,0.0028322157,0.0034787166,0.0022893974,0.0022834907,0.0023377913],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005353516,0.00047989518,0.021618724,0.0101523055,0.0007005925,0.00035152698,0.0018844806,0.0048301555,0.051988967,0.05313975,0.015176658,0.83914167],"study_design_scores_gemma":[0.00020687474,0.0024268639,0.06970455,0.004727935,0.00095505314,0.0031275025,0.0033596398,0.048769057,0.2016235,0.1464575,0.5179762,0.0006653632],"about_ca_topic_score_codex":0.0023464186,"about_ca_topic_score_gemma":0.001669049,"teacher_disagreement_score":0.011061458,"about_ca_system_score_codex":0.0016996801,"about_ca_system_score_gemma":0.003854915,"threshold_uncertainty_score":0.058499277},"labels":[],"label_agreement":null},{"id":"W4405589813","doi":"10.6000/1929-6029.2024.13.32","title":"Changes in Quality of Alimentation, Anthropometric Measurements, Emotional and Appetite Status of Bariatric Surgery Patients: A Retrospective Cohort Study","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Bariatric Surgery and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Anthropometry; Appetite; Retrospective cohort study; Weight loss; Meal; Obesity; Cohort; Quality of life (healthcare); Weight management; Cohort study; Surgery; Internal medicine","score_opus":0.1143783087135656,"score_gpt":0.47085009582366627,"score_spread":0.3564717871101007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405589813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995326,0.00014540475,0.000052763156,0.000010881514,0.00000455143,0.00001902679,0.00012476403,9.170501e-7,0.0001091229],"genre_scores_gemma":[0.9993487,0.000107817956,0.00007144011,0.000023769613,0.000009114192,0.000031292722,0.00029255744,0.0000011279253,0.00011407912],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99944514,0.00008948139,0.00008663311,0.0001477398,0.00013204178,0.00009900381],"domain_scores_gemma":[0.99918705,0.00011779903,0.00034653352,0.000092861555,0.00010224605,0.00015358409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008050623,0.00029231774,0.0004485428,0.00077878585,0.00059227936,0.0005355349,0.00029404458,0.00039099,0.0014324725],"category_scores_gemma":[0.0011268889,0.00035777074,0.00068022235,0.0009806677,0.00029228374,0.0006042954,0.0004227736,0.00049227785,0.00024326485],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013381915,0.00008415274,0.99881953,0.000009829398,0.000040106752,0.00011651917,0.000077582765,0.0000082660445,0.00011407549,0.000005259672,0.000036834892,0.00055396714],"study_design_scores_gemma":[0.000017099874,0.0005819654,0.9979175,0.000008420325,0.000052704985,0.00067540625,0.00038991836,0.00006773187,0.00006082875,0.000009973173,0.00021270942,0.000005796965],"about_ca_topic_score_codex":0.0016573297,"about_ca_topic_score_gemma":0.0018828139,"teacher_disagreement_score":0.0016573297,"about_ca_system_score_codex":0.00023698363,"about_ca_system_score_gemma":0.0003445819,"threshold_uncertainty_score":0.004792094},"labels":[],"label_agreement":null},{"id":"W4405770208","doi":"10.6000/1929-6029.2024.13.34","title":"Early Detection Model of Drug Abuse Relapse in the City of Padang","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Public Health and Nutrition","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rehabilitation; Addiction; Christian ministry; Relapse prevention; Substance abuse; Medicine; Psychiatry; Drug; Mental health; Physical therapy","score_opus":0.09123757301500404,"score_gpt":0.4793087499479864,"score_spread":0.3880711769329823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405770208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98579454,0.00086537504,0.0024525437,0.0026348187,0.000066231085,0.00027504642,0.00077763264,0.00011266363,0.0070210793],"genre_scores_gemma":[0.99522024,0.00045841327,0.0012031458,0.00011964596,0.000011679174,0.00009113442,0.00040231334,0.0000054936613,0.002488024],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936014,0.00019785082,0.000032870394,0.00012244425,0.000082798746,0.00020389663],"domain_scores_gemma":[0.99920577,0.00015180762,0.0001531698,0.000032122185,0.00025564054,0.00020147275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007636175,0.0006067979,0.00058007095,0.0016574308,0.0013260557,0.0020606676,0.0011479432,0.0006651446,0.0066478797],"category_scores_gemma":[0.0019041254,0.00040701087,0.0011102024,0.00081125303,0.00039020396,0.0008804455,0.0014698452,0.0013005183,0.0008379235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030075677,0.0013745227,0.9536604,0.00014680826,0.00013327406,0.0014612961,0.0019468652,0.0012444233,0.0004103408,0.0016731641,0.0037690653,0.033879023],"study_design_scores_gemma":[0.00008650649,0.000849615,0.9049046,0.0003117377,0.00044225025,0.0022012827,0.01638604,0.06508573,0.0007293763,0.0036040605,0.005307368,0.000091416645],"about_ca_topic_score_codex":0.08442456,"about_ca_topic_score_gemma":0.07330293,"teacher_disagreement_score":0.08442456,"about_ca_system_score_codex":0.0023868403,"about_ca_system_score_gemma":0.0037724553,"threshold_uncertainty_score":0.16786629},"labels":[],"label_agreement":null},{"id":"W4405848729","doi":"10.6000/1929-6029.2024.13.38","title":"Statistical Analysis of Microarray Data to Identify Key Gene Expression Patterns in Primary Hyperoxaluria","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Microarray analysis techniques; Key (lock); Computational biology; Expression (computer science); Data mining; Computer science; Gene expression; Biology; Gene; Genetics","score_opus":0.10980008824431038,"score_gpt":0.508055380442517,"score_spread":0.3982552921982066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405848729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73179317,0.0032546257,0.21043144,0.0010767535,0.00072990527,0.002298858,0.039074585,0.0045640185,0.006776611],"genre_scores_gemma":[0.87104565,0.0005707933,0.10540189,0.00040161514,0.00014100761,0.0065084486,0.013463511,0.0005358892,0.0019311641],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9903453,0.0025364528,0.001075016,0.002571195,0.0028288756,0.0006430494],"domain_scores_gemma":[0.9852506,0.010296839,0.0015312849,0.0014972391,0.0011684388,0.00025556545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076982146,0.0008026926,0.0020577298,0.0039440393,0.0008375068,0.0018186952,0.0008957842,0.00059965986,0.005941323],"category_scores_gemma":[0.0142943235,0.0003242222,0.0014841732,0.0053688213,0.00094966893,0.0005766079,0.00089405495,0.0017120618,0.0006936474],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0074326065,0.0013276454,0.42537257,0.00420854,0.005640582,0.0016735459,0.002016322,0.011171494,0.29130083,0.00372909,0.013416844,0.23270997],"study_design_scores_gemma":[0.000175797,0.0028394791,0.8808578,0.00017630792,0.0011477264,0.0014804589,0.0016456281,0.04466612,0.041542377,0.0053702495,0.01995734,0.0001406199],"about_ca_topic_score_codex":0.0009811186,"about_ca_topic_score_gemma":0.001176808,"teacher_disagreement_score":0.0076982146,"about_ca_system_score_codex":0.00082840037,"about_ca_system_score_gemma":0.0009086589,"threshold_uncertainty_score":0.040712476},"labels":[],"label_agreement":null},{"id":"W4405848756","doi":"10.6000/1929-6029.2024.13.37","title":"Biostatistical Analysis of Microarray Data to Decipher Viral Pathogenesis","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Animal Virus Infections Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"DECIPHER; Computational biology; Microarray analysis techniques; Biology; Virology; Bioinformatics; Genetics; Gene; Gene expression","score_opus":0.1579552651107291,"score_gpt":0.470855741462262,"score_spread":0.3129004763515329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405848756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2415433,0.003255353,0.529719,0.0024804752,0.0038595817,0.0086621605,0.17656814,0.025295986,0.008616002],"genre_scores_gemma":[0.58794945,0.0006054012,0.3486457,0.0010447305,0.0005170272,0.02063373,0.03507977,0.0020547856,0.0034694073],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9826315,0.006140635,0.0021114,0.0045558293,0.003446145,0.0011145168],"domain_scores_gemma":[0.96872663,0.023107115,0.0019569735,0.003561469,0.0022227587,0.00042502597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01343233,0.0016697884,0.0037908938,0.008177519,0.0013069268,0.003188637,0.0019830198,0.0014140429,0.025768844],"category_scores_gemma":[0.041670587,0.0006365408,0.0041659693,0.008226787,0.0013572618,0.0009900574,0.001796362,0.0030428316,0.0028129192],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009496048,0.003064254,0.31042263,0.018330647,0.024344875,0.0037196933,0.00364482,0.03177579,0.087188706,0.01581841,0.124610506,0.3675836],"study_design_scores_gemma":[0.0009823167,0.004230907,0.4289164,0.001632546,0.0051299054,0.0021696824,0.0050691566,0.3273694,0.036931984,0.034277003,0.15270194,0.0005887095],"about_ca_topic_score_codex":0.002960776,"about_ca_topic_score_gemma":0.0027025498,"teacher_disagreement_score":0.025768844,"about_ca_system_score_codex":0.0010842336,"about_ca_system_score_gemma":0.0030265152,"threshold_uncertainty_score":0.08620536},"labels":[],"label_agreement":null},{"id":"W4405848769","doi":"10.6000/1929-6029.2024.13.36","title":"Statistical Analysis of Gene Variants for Homologous Recombination Pathways of DNA Repair leading to Cancer Susceptibility","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"DNA Repair Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Homologous recombination; Gene; DNA repair; Homologous chromosome; Genetics; Recombination; Biology; DNA; Cancer","score_opus":0.05252278792496062,"score_gpt":0.4411419716862995,"score_spread":0.38861918376133886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405848769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97132695,0.001569386,0.018820215,0.00022839234,0.00012522771,0.00015354417,0.0056433175,0.00045835148,0.0016745507],"genre_scores_gemma":[0.99479693,0.000104905266,0.0027750102,0.00002475527,0.00002739218,0.00012273455,0.0018646242,0.000046739286,0.00023697763],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945937,0.0014998362,0.00053087465,0.0019879371,0.0009903193,0.00039720107],"domain_scores_gemma":[0.9762005,0.017316986,0.002894399,0.0016400492,0.0010010038,0.0009470121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056858934,0.00076421007,0.0011150371,0.004608146,0.0007172577,0.0010490173,0.0010362866,0.0007671308,0.0056696846],"category_scores_gemma":[0.016125184,0.00020916769,0.0027230575,0.0047911797,0.0008353111,0.00059136614,0.00088345027,0.0013299095,0.00049373583],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017696499,0.00008904267,0.96890444,0.0002678774,0.004822387,0.00067393266,0.0001355437,0.0047601513,0.0027215828,0.00061112683,0.0015201137,0.0137240235],"study_design_scores_gemma":[0.00010930458,0.0006653806,0.92810035,0.000066447225,0.002256624,0.0017058568,0.0004398701,0.059144642,0.0014296761,0.0033005222,0.0027307575,0.000050554383],"about_ca_topic_score_codex":0.0016945291,"about_ca_topic_score_gemma":0.0013358657,"teacher_disagreement_score":0.0056858934,"about_ca_system_score_codex":0.00041557042,"about_ca_system_score_gemma":0.0008561442,"threshold_uncertainty_score":0.030070245},"labels":[],"label_agreement":null},{"id":"W4405848788","doi":"10.6000/1929-6029.2024.13.35","title":"Leptin Signaling: Decoding of Genetic Pathways using Bioinformatics; Shaping Bariatric Surgery Outcomes","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diet and metabolism studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Leptin; Bioinformatics; Decoding methods; Medicine; Obesity; Biology; Computational biology; Genetics; Computer science; Internal medicine; Algorithm","score_opus":0.21223159105911552,"score_gpt":0.4664808763354401,"score_spread":0.2542492852763246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405848788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71247804,0.003886975,0.22237828,0.0057860385,0.00020141921,0.00032898522,0.041766442,0.007067912,0.006105918],"genre_scores_gemma":[0.89716053,0.001457464,0.08323838,0.00046134778,0.000058318456,0.00025698842,0.01596884,0.00020216336,0.0011960154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970347,0.0001050279,0.000020117315,0.00008284986,0.000053743057,0.000034766268],"domain_scores_gemma":[0.9992305,0.00044518302,0.00011698485,0.00003376658,0.00010829632,0.000065313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010226858,0.0006575133,0.00047542359,0.0018674941,0.0004227722,0.0010105522,0.0005807882,0.00037909552,0.0032324737],"category_scores_gemma":[0.0031712565,0.0001846935,0.0011128278,0.0014795949,0.00019280463,0.00026455068,0.00052037765,0.00057787384,0.0008733697],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020734777,0.0005380649,0.601532,0.002045692,0.0023991824,0.002433902,0.0005464616,0.11049828,0.055186175,0.011066948,0.022986267,0.18869351],"study_design_scores_gemma":[0.00018385916,0.00047470495,0.22872402,0.0003792994,0.0014897299,0.0013200229,0.0005866843,0.70023674,0.018471714,0.028219752,0.019793956,0.0001194323],"about_ca_topic_score_codex":0.006611552,"about_ca_topic_score_gemma":0.00838354,"teacher_disagreement_score":0.006611552,"about_ca_system_score_codex":0.00055163464,"about_ca_system_score_gemma":0.0014299654,"threshold_uncertainty_score":0.013146162},"labels":[],"label_agreement":null},{"id":"W4405891059","doi":"10.6000/1929-6029.2024.13.39","title":"Optimizing Quality of Hospital Services and Inpatient Satisfaction through Lean Principles","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Quality and Management","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Patient satisfaction; Operations management; Health care; Lean manufacturing; Quality (philosophy); Service quality; Service delivery framework; Logistic regression; Service (business); Business; Nursing; Medicine; Psychology; Marketing; Engineering","score_opus":0.3071653906818992,"score_gpt":0.6195581930661382,"score_spread":0.31239280238423905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405891059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892902,0.00037480684,0.0034858745,0.0012109365,0.000015748863,0.000054346256,0.00021345465,0.000043828946,0.0053107534],"genre_scores_gemma":[0.9975358,0.00015579315,0.0018273544,0.00010741217,0.000010134528,0.0000176533,0.000076932294,0.0000028834659,0.00026605997],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971969,0.0012767679,0.00027294268,0.000100570425,0.0008489107,0.00030388616],"domain_scores_gemma":[0.9941064,0.0018092655,0.0025011653,0.00013463666,0.00091865694,0.00052982045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023343796,0.00019163768,0.00022738044,0.00093225215,0.00037691498,0.0017462018,0.00029314135,0.00023222537,0.0019743089],"category_scores_gemma":[0.0050400957,0.00007985206,0.00034338038,0.0014008605,0.00048186167,0.0005283114,0.0008111016,0.00041099387,0.00016714548],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038378895,0.00086913334,0.8064135,0.0006062826,0.00018009213,0.00018967356,0.0016054269,0.0032760312,0.0024326842,0.0016799059,0.0020688465,0.18029472],"study_design_scores_gemma":[0.00004585223,0.0019394625,0.97528505,0.000247469,0.00011356764,0.00042487198,0.0067987624,0.006066557,0.0034594713,0.001977254,0.0036053776,0.00003628829],"about_ca_topic_score_codex":0.0013334348,"about_ca_topic_score_gemma":0.002414857,"teacher_disagreement_score":0.0023343796,"about_ca_system_score_codex":0.0012600409,"about_ca_system_score_gemma":0.0024447122,"threshold_uncertainty_score":0.012345552},"labels":[],"label_agreement":null},{"id":"W4405948794","doi":"10.6000/1929-6029.2024.13.40","title":"Patients’ Perception on Clinical Training and Informed Consent Towards Medical Students in Jazan Hospitals: A Cross-Sectional Study","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perception; Cross-sectional study; Informed consent; Psychology; Family medicine; Medical education; Medicine; Alternative medicine","score_opus":0.5301531454062701,"score_gpt":0.6786992647202436,"score_spread":0.14854611931397343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405948794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99958557,0.00010893954,0.000021886872,0.000063960586,0.0000028155248,0.000009516644,0.000020665395,4.4519618e-7,0.00018615038],"genre_scores_gemma":[0.9996743,0.00012188777,0.00003844066,0.00006309148,0.00000476941,0.000010314991,0.000022224569,3.9238512e-7,0.00006458359],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982064,0.0007732251,0.00022646977,0.0001375242,0.00038475668,0.0002716756],"domain_scores_gemma":[0.99473023,0.0016864035,0.002257979,0.000120805315,0.00042719572,0.0007773115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021027215,0.00016305296,0.00039051636,0.0006389356,0.0008160562,0.0009217541,0.00024780043,0.0006377991,0.002154078],"category_scores_gemma":[0.005500748,0.00028639063,0.00033036273,0.0007152627,0.0006282892,0.00089452363,0.0007576932,0.00076091895,0.00019076532],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001617541,0.00039313408,0.9804954,0.00011099621,0.0000294079,0.000562689,0.013638798,0.000029784558,0.0004917526,0.000066274755,0.00023090967,0.0037890375],"study_design_scores_gemma":[0.000019131596,0.0014581956,0.94235516,0.00009112084,0.00003401455,0.0023151024,0.052315775,0.00015610814,0.00017195675,0.000042836175,0.0010115004,0.000029088858],"about_ca_topic_score_codex":0.0012604215,"about_ca_topic_score_gemma":0.0019071656,"teacher_disagreement_score":0.002154078,"about_ca_system_score_codex":0.00044284892,"about_ca_system_score_gemma":0.0005078865,"threshold_uncertainty_score":0.011120379},"labels":[],"label_agreement":null},{"id":"W4405949056","doi":"10.6000/1929-6029.2024.13.41","title":"Bacterial Infection Among Covid-19-Infected Patients Admitted to the Intensive Care Unit at King Abdullah Hospital in Bisha: A Single-Centre Retrospective Observational Study","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intensive care unit; Observational study; Medicine; Coronavirus disease 2019 (COVID-19); Retrospective cohort study; Emergency medicine; University hospital; Intensive care; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pediatrics; Intensive care medicine; Internal medicine; Infectious disease (medical specialty)","score_opus":0.1565678924749122,"score_gpt":0.513771225693377,"score_spread":0.35720333321846476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405949056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99946433,0.0001960515,0.000036368365,0.000020860709,0.0000034869524,0.000033307922,0.0001398211,8.013774e-7,0.00010497234],"genre_scores_gemma":[0.99933475,0.0001996622,0.000066118664,0.000054921875,0.000012985733,0.00002851087,0.00026159865,7.323569e-7,0.000040603816],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99920803,0.00013636779,0.00018175134,0.00018569187,0.00014759056,0.00014058928],"domain_scores_gemma":[0.9979171,0.00030532383,0.001010257,0.00013752734,0.0002332324,0.0003965249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000688504,0.00040833693,0.00073842716,0.0010618687,0.0008617485,0.00074539863,0.000566883,0.00058751693,0.001211921],"category_scores_gemma":[0.0017721613,0.00057183485,0.00049255945,0.0010953805,0.000577092,0.000676098,0.0010754152,0.0006228066,0.00021686184],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006615265,0.00005552446,0.998642,0.000040216546,0.000023745511,0.00039559478,0.0002666641,0.000010418757,0.00015237652,0.000008452258,0.000048662536,0.00029018836],"study_design_scores_gemma":[0.00001795073,0.00049895607,0.9939564,0.00005509244,0.000045626435,0.0021870772,0.0027150458,0.00013611281,0.00008794248,0.000014667049,0.0002720905,0.000013128852],"about_ca_topic_score_codex":0.006693558,"about_ca_topic_score_gemma":0.0072975187,"teacher_disagreement_score":0.006693558,"about_ca_system_score_codex":0.00086472975,"about_ca_system_score_gemma":0.0010207148,"threshold_uncertainty_score":0.01330924},"labels":[],"label_agreement":null},{"id":"W4406687472","doi":"10.6000/1929-6029.2025.14.01","title":"Multiple Mean Comparison for Clusters of Gene Expression Data through the t-SNE Plot and PCA Dimension Reduction","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong Baptist University","keywords":"Dimensionality reduction; Dimension (graph theory); Plot (graphics); Reduction (mathematics); Expression (computer science); Mathematics; Pattern recognition (psychology); Statistics; Biology; Artificial intelligence; Computer science; Combinatorics; Geometry","score_opus":0.11361778035302182,"score_gpt":0.47861796691020303,"score_spread":0.3650001865571812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406687472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009510652,0.00019102452,0.9879574,0.0001672397,0.00008196509,0.00010193864,0.00016960996,0.0011641808,0.0006560744],"genre_scores_gemma":[0.097579844,0.0002545306,0.8999123,0.00007199056,0.000056991707,0.0004847671,0.0004678696,0.00046541257,0.0007063215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995622,0.0017121605,0.00029936226,0.0009793438,0.0012385403,0.00014868942],"domain_scores_gemma":[0.991211,0.0048771496,0.0010174682,0.00091517094,0.0017793818,0.0001997146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063451026,0.0016854103,0.0012782252,0.0053862305,0.0011314071,0.0026497873,0.0012649682,0.00092612137,0.0042715594],"category_scores_gemma":[0.020630632,0.00051422283,0.0021274216,0.0033300156,0.0016668355,0.0026783338,0.0018756345,0.0026627355,0.0010486615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069994497,0.00038862362,0.012893267,0.0014094017,0.000731077,0.0007961623,0.003246405,0.059852615,0.09317347,0.11177034,0.011192136,0.7038466],"study_design_scores_gemma":[0.00011216714,0.000588933,0.019826742,0.00024570548,0.00021695781,0.0010910411,0.0015326827,0.74292403,0.06563386,0.13349836,0.033858612,0.0004709112],"about_ca_topic_score_codex":0.0012385265,"about_ca_topic_score_gemma":0.0014958021,"teacher_disagreement_score":0.0063451026,"about_ca_system_score_codex":0.00074803084,"about_ca_system_score_gemma":0.0016129375,"threshold_uncertainty_score":0.03355652},"labels":[],"label_agreement":null},{"id":"W4406847430","doi":"10.6000/1929-6029.2025.14.02","title":"The Effect of Interpersonal Communication on Prevention Behavior of Early Hypertension among Student at SMAN 6 and SMAN 19 Bone","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Public Health and Nutrition","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universitas Hasanuddin","keywords":"Psychology; Interpersonal communication; Mathematics education; Social psychology","score_opus":0.05101177152754408,"score_gpt":0.5022613343357444,"score_spread":0.4512495628082003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406847430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9995858,0.000031788204,0.000012340886,0.000045099085,0.0000051173624,0.000010567866,0.0000059089753,0.0000010091649,0.00030246683],"genre_scores_gemma":[0.9993648,0.00010148245,0.00011325883,0.00003073638,0.000008837737,0.000032234988,0.000014029568,4.2002674e-7,0.0003343217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937016,0.0002393252,0.000037156657,0.000044077176,0.00014513635,0.0001641929],"domain_scores_gemma":[0.9974349,0.0008385456,0.00055858144,0.00005564833,0.00015670584,0.0009556284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008115163,0.00029242842,0.00027179832,0.00029683227,0.00069747353,0.00043839542,0.00025918396,0.00038006474,0.002386511],"category_scores_gemma":[0.0033245904,0.00016421551,0.0003571102,0.00016751516,0.00021492729,0.00020554906,0.0005357805,0.00077390467,0.00019704086],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022331686,0.044569653,0.8301356,0.00041365338,0.00023607258,0.0005059389,0.018522575,0.0002135343,0.007559406,0.0001439152,0.00063334376,0.09483311],"study_design_scores_gemma":[0.00009072957,0.008453991,0.98431474,0.00006678574,0.000104602615,0.00008370931,0.0052412907,0.00025933926,0.00080425036,0.000032365406,0.0005365845,0.000011489572],"about_ca_topic_score_codex":0.002366662,"about_ca_topic_score_gemma":0.0041928184,"teacher_disagreement_score":0.002386511,"about_ca_system_score_codex":0.000392986,"about_ca_system_score_gemma":0.0007902477,"threshold_uncertainty_score":0.007983685},"labels":[],"label_agreement":null},{"id":"W4407176551","doi":"10.6000/1929-6029.2025.14.04","title":"Comparative Analysis of Kolmogorov-Inspired CNN and Traditional CNN Models for Pneumonia Detection: A Study on Chest CT Images","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pneumonia; Computer science; Artificial intelligence; Medicine; Pattern recognition (psychology); Internal medicine","score_opus":0.19464887881089027,"score_gpt":0.5191152031387196,"score_spread":0.32446632432782935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407176551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8979929,0.011286944,0.07982149,0.00074345357,0.0003710351,0.0001498086,0.0012831046,0.001357522,0.0069937347],"genre_scores_gemma":[0.9828027,0.001577054,0.012946958,0.00011295364,0.000055930774,0.000035144578,0.0010676159,0.000041134517,0.0013605931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991768,0.0001836318,0.000077250676,0.00018923428,0.0002642327,0.00010881184],"domain_scores_gemma":[0.997988,0.0010293698,0.00015834818,0.00015243031,0.0005952047,0.00007663464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023068772,0.0010902145,0.0005443108,0.001382359,0.00024115483,0.0006973011,0.0006299501,0.00074994727,0.000751682],"category_scores_gemma":[0.00624233,0.00020535162,0.00066445686,0.00067165797,0.00030416637,0.0013408555,0.00042887597,0.00047395777,0.00029002808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025731463,0.000476315,0.12879317,0.0012554957,0.00122657,0.00074085937,0.00019839476,0.36745557,0.016312314,0.002386371,0.008378936,0.47020274],"study_design_scores_gemma":[0.000021034837,0.00053320074,0.026827656,0.000076261815,0.00022173644,0.00036251216,0.00011039024,0.9627748,0.007036512,0.0008087128,0.0011934094,0.00003372091],"about_ca_topic_score_codex":0.010495997,"about_ca_topic_score_gemma":0.013127844,"teacher_disagreement_score":0.010495997,"about_ca_system_score_codex":0.0010823234,"about_ca_system_score_gemma":0.00069745356,"threshold_uncertainty_score":0.020869792},"labels":[],"label_agreement":null},{"id":"W4407178907","doi":"10.6000/1929-6029.2025.14.03","title":"Assessing the Impact of Human and Technological Factors on Hospital Management Information System Utilization: A Case Study at Hospital X In Padang City Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hospital information system; Business; Operations management; Information system; Engineering","score_opus":0.16001651180637777,"score_gpt":0.5961320892293351,"score_spread":0.43611557742295737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407178907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99948657,0.000020529074,0.000061482795,0.000057474488,7.105997e-7,0.000012940378,0.00001193698,9.652375e-7,0.0003474441],"genre_scores_gemma":[0.99957615,0.00005967608,0.00017119094,0.000022481849,0.0000013795594,0.000011375192,0.000010074594,8.581897e-7,0.00014678291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99842536,0.0008253399,0.00009829479,0.00009031898,0.00023509414,0.00032563196],"domain_scores_gemma":[0.996601,0.0017058288,0.00078995095,0.0001291674,0.00028642887,0.00048763494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016165151,0.00022393845,0.0002497356,0.0012120347,0.0016040585,0.0016641144,0.0005343928,0.0005139597,0.001383514],"category_scores_gemma":[0.0036432822,0.00024747365,0.0003217496,0.0013994863,0.00092113606,0.0008162899,0.0012390002,0.0005580056,0.00012813947],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018250044,0.0014489988,0.9318644,0.00020927211,0.000079096164,0.00916038,0.03194391,0.0010713929,0.0016910377,0.0005174955,0.00037035003,0.02146106],"study_design_scores_gemma":[0.000020181602,0.0011519496,0.8263457,0.000088545436,0.00007234425,0.0027020124,0.16269344,0.0034647887,0.0015015698,0.00018485702,0.0017337638,0.00004093919],"about_ca_topic_score_codex":0.015130899,"about_ca_topic_score_gemma":0.027904874,"teacher_disagreement_score":0.015130899,"about_ca_system_score_codex":0.0027444542,"about_ca_system_score_gemma":0.0025961972,"threshold_uncertainty_score":0.030085683},"labels":[],"label_agreement":null},{"id":"W4407553706","doi":"10.6000/1929-6029.2025.14.05","title":"Predictors of Type-2 Diabetes Self-Screening: The Impact of Health Beliefs Model, Knowledge, and Demographics","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Diabetes Management and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Odds; Demographics; Checklist; Family history; Health belief model; Affect (linguistics); Medicine; Odds ratio; Diabetes mellitus; Disease; Demography; Clinical psychology; Gerontology; Family medicine; Internal medicine; Psychology; Health education; Public health; Logistic regression; Pathology","score_opus":0.05390463227216665,"score_gpt":0.47733434317752627,"score_spread":0.42342971090535964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407553706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986951,0.00022027316,0.00007900379,0.00018283853,0.000007460156,0.000016934271,0.0001473895,0.0000037622008,0.00064719043],"genre_scores_gemma":[0.9995459,0.00008541417,0.000092616174,0.000021007396,0.000007410314,0.000007647337,0.00011734374,0.0000011176,0.00012154607],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916637,0.00036322468,0.00008166148,0.00008329482,0.00017839349,0.00012702009],"domain_scores_gemma":[0.99371606,0.0028734014,0.0017877088,0.0002550829,0.00038827152,0.0009794378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024557516,0.00032280743,0.00034303454,0.0007135944,0.00041091122,0.001240793,0.00046526425,0.000598292,0.0037194588],"category_scores_gemma":[0.008836966,0.0002306206,0.0011008248,0.0006961228,0.000310514,0.00059791974,0.00053467735,0.0012870266,0.00027828157],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031707423,0.00015804726,0.9986368,0.0000073693463,0.000044579152,0.000017253766,0.00005133398,0.0000430343,0.000012616023,0.000011063279,0.00003938203,0.00094681117],"study_design_scores_gemma":[0.000010058855,0.00018087568,0.997556,0.000027028917,0.000082067265,0.000106247506,0.00037874322,0.0014669367,0.000030203673,0.00005774186,0.000099077864,0.000004985368],"about_ca_topic_score_codex":0.007917944,"about_ca_topic_score_gemma":0.007428232,"teacher_disagreement_score":0.007917944,"about_ca_system_score_codex":0.0003708885,"about_ca_system_score_gemma":0.00086436654,"threshold_uncertainty_score":0.015743732},"labels":[],"label_agreement":null},{"id":"W4407652101","doi":"10.6000/1929-6029.2025.14.06","title":"Prevalence of Depression among Women Using Hormonal Contraceptive Use: Insights from a Hospital-Based Cross-Sectional Study","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Maternal Mental Health During Pregnancy and Postpartum","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cross-sectional study; Depression (economics); Hormonal contraception; Medicine; Demography; Psychology; Clinical psychology; Psychiatry; Obstetrics; Environmental health; Population; Family planning; Research methodology; Sociology","score_opus":0.055804040684328184,"score_gpt":0.4675351265665818,"score_spread":0.4117310858822536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407652101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9992945,0.000113135364,0.000029980401,0.00002712143,0.0000027264202,0.00001561148,0.00037334027,0.0000011371407,0.00014234199],"genre_scores_gemma":[0.9992512,0.00015572671,0.00006650209,0.00003881686,0.000004998814,0.000018230143,0.00039319563,5.4128344e-7,0.000070702794],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997004,0.00010253564,0.00005163704,0.00004750199,0.000052418396,0.000045483084],"domain_scores_gemma":[0.99927443,0.000099702105,0.00031644013,0.00003468523,0.00010966197,0.00016512416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045445908,0.00017282434,0.00028475974,0.0007613316,0.00039721344,0.0004254642,0.00028538593,0.0003960946,0.00081717747],"category_scores_gemma":[0.0010849109,0.00030776448,0.00028378647,0.0009835374,0.00017631946,0.00034279015,0.00034825067,0.00034549754,0.0001962631],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020355748,0.000030935764,0.9992887,0.0000073520378,0.000018769844,0.000050059312,0.00010787299,0.000005374159,0.00009401314,0.0000019173688,0.00003205929,0.00034245726],"study_design_scores_gemma":[0.0000031619059,0.00007673852,0.9992142,0.0000040810305,0.000011009098,0.00021139989,0.00037642856,0.00003298139,0.000016423466,0.0000021255073,0.000049725713,0.0000017571152],"about_ca_topic_score_codex":0.0071352585,"about_ca_topic_score_gemma":0.011413825,"teacher_disagreement_score":0.0071352585,"about_ca_system_score_codex":0.00027512456,"about_ca_system_score_gemma":0.00018440808,"threshold_uncertainty_score":0.014187455},"labels":[],"label_agreement":null},{"id":"W4407711125","doi":"10.6000/1929-6029.2025.14.07","title":"Understanding Thrombocytopenia in the Obstetric Population: A Study from a Tertiary Care Center","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Platelet Disorders and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tertiary care; Center (category theory); Population; Medicine; Family medicine; Environmental health; Chemistry","score_opus":0.1369113957026293,"score_gpt":0.4789430407891329,"score_spread":0.34203164508650363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407711125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989575,0.00018441855,0.000035016037,0.00015698608,0.00000381789,0.000016233484,0.00022072726,0.0000017172674,0.00042345858],"genre_scores_gemma":[0.9991559,0.0002746011,0.00007155252,0.00020270284,0.00001103647,0.000019327925,0.00019979579,0.0000014123245,0.00006373707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992698,0.00016961132,0.00011523601,0.00013558516,0.00015165513,0.00015815152],"domain_scores_gemma":[0.99805486,0.0003011201,0.0009621894,0.00008744211,0.00020634684,0.0003880725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007625711,0.00022339592,0.00040639154,0.0011568328,0.0010098375,0.0009790891,0.0005713489,0.0005462955,0.0019599372],"category_scores_gemma":[0.0030245234,0.00039244504,0.00032498752,0.002029281,0.00037647452,0.0006952543,0.0008227277,0.00074025197,0.00030208536],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008564918,0.00002604028,0.9987404,0.00000862811,0.0000073565297,0.0002092117,0.0003438389,0.0000048810884,0.00004751824,0.000009450675,0.0001014948,0.00049265305],"study_design_scores_gemma":[0.000004930565,0.000078263925,0.99638414,0.000024850091,0.000013361125,0.0009119342,0.0023033726,0.000056030996,0.000018549801,0.000014702187,0.00018619714,0.0000036787062],"about_ca_topic_score_codex":0.013978039,"about_ca_topic_score_gemma":0.01590043,"teacher_disagreement_score":0.013978039,"about_ca_system_score_codex":0.0009434376,"about_ca_system_score_gemma":0.0012607516,"threshold_uncertainty_score":0.027793348},"labels":[],"label_agreement":null},{"id":"W4408086341","doi":"10.6000/1929-6029.2025.14.09","title":"Optimizing Sample Size for Accelerated Failure Time Model in Progressive Type-II Censoring through Rank Set Sampling","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Statistics; Sample (material); Sample size determination; Rank (graph theory); Sampling (signal processing); Mathematics; Set (abstract data type); Computer science; Econometrics; Combinatorics","score_opus":0.2693501445789356,"score_gpt":0.5615043793006131,"score_spread":0.29215423472167756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408086341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023509214,0.00021573636,0.9749128,0.00022014584,0.000027824755,0.00029470626,0.00011467914,0.00019733276,0.00050758326],"genre_scores_gemma":[0.51060194,0.0006946254,0.48201016,0.00030884932,0.00017107416,0.0020457124,0.00094864937,0.00017049724,0.0030484502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9900378,0.0075671417,0.000262905,0.00072896545,0.00096509303,0.00043808308],"domain_scores_gemma":[0.9370996,0.055472005,0.0019052287,0.0019295835,0.00274238,0.0008511748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02531154,0.0010108689,0.0022830388,0.0011769936,0.0006879249,0.0010863133,0.002888004,0.0012593991,0.0034841294],"category_scores_gemma":[0.04880564,0.00070708955,0.0013907148,0.0010906897,0.0015721994,0.0018822781,0.0018507353,0.0023414386,0.00042478196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012332392,0.00036128538,0.010314762,0.00052866474,0.00024527044,0.00051254896,0.0005371255,0.77325284,0.0029516954,0.10545045,0.003643189,0.100968964],"study_design_scores_gemma":[0.00010362562,0.0001562483,0.00056690176,0.000020688274,0.000026805192,0.0000444822,0.000028183505,0.98388886,0.00046882758,0.014259699,0.0004206603,0.0000150812],"about_ca_topic_score_codex":0.005266357,"about_ca_topic_score_gemma":0.003873326,"teacher_disagreement_score":0.02531154,"about_ca_system_score_codex":0.0011633082,"about_ca_system_score_gemma":0.0027869418,"threshold_uncertainty_score":0.13386184},"labels":[],"label_agreement":null},{"id":"W4408086389","doi":"10.6000/1929-6029.2025.14.08","title":"A Conceptual Model of Sustainable Technology Use: The Role of Confirmation and Perceived Usefulness in the Hospital X Management Information System in Padang","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Conceptual model; Business; Knowledge management; Psychology; Process management; Computer science; Database","score_opus":0.07110138836614756,"score_gpt":0.4280098169977412,"score_spread":0.35690842863159367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408086389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93813396,0.00089031766,0.017249376,0.007557163,0.000043635344,0.00031978523,0.00019328335,0.00007352633,0.03553895],"genre_scores_gemma":[0.9958418,0.00022385121,0.0029588882,0.000105720464,0.00000622569,0.00012223015,0.000047017264,0.000003655691,0.00069054274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980421,0.0011419981,0.0000984362,0.00022802874,0.00023584493,0.00025361366],"domain_scores_gemma":[0.99576557,0.0023138388,0.00082073204,0.0001251866,0.00042986983,0.00054480246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021258218,0.00071379496,0.00023991367,0.003175702,0.0020860953,0.005974587,0.0016215815,0.0017531001,0.0034817497],"category_scores_gemma":[0.00383934,0.00046229543,0.00055189576,0.0025439432,0.0046484643,0.00580541,0.0032776995,0.0015196812,0.00029322042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022253432,0.001718072,0.33874568,0.00077403535,0.00015543935,0.0027586413,0.18169834,0.008353811,0.0018734783,0.3995308,0.003202171,0.060967032],"study_design_scores_gemma":[0.00020891456,0.001848522,0.2415119,0.0018821157,0.00029272182,0.0030669398,0.45735356,0.11673279,0.0011490971,0.1262651,0.049367234,0.0003211801],"about_ca_topic_score_codex":0.008796379,"about_ca_topic_score_gemma":0.0063688275,"teacher_disagreement_score":0.008796379,"about_ca_system_score_codex":0.004512257,"about_ca_system_score_gemma":0.006222321,"threshold_uncertainty_score":0.032738805},"labels":[],"label_agreement":null},{"id":"W4408086432","doi":"10.6000/1929-6029.2025.14.10","title":"Risk Factors of Physical Condition of House and Clean and Healthy Living Behavior (PHBS) to Tuberculosis in Kaluku Bodoa Health Center Area, Makassar City","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health and Well-being Studies","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Center (category theory); Environmental health; Tuberculosis; Health risk; Gerontology; Psychology; Geography; Medicine","score_opus":0.0653757530940474,"score_gpt":0.5102134910149181,"score_spread":0.44483773792087067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408086432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991166,0.00024648718,0.000016390084,0.000104711085,0.000006228098,0.0000088535335,0.000106867104,0.0000011581213,0.00039269283],"genre_scores_gemma":[0.99961984,0.00013489416,0.00002458806,0.000021262194,0.00000820411,0.000008807586,0.000095284915,5.846231e-7,0.000086542386],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996264,0.00007665014,0.000043925975,0.000059080347,0.00007450444,0.000119402386],"domain_scores_gemma":[0.998917,0.00011462023,0.0005214569,0.000027274873,0.000100958765,0.00031864864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000305298,0.0002485868,0.00019526895,0.0006839373,0.00087210204,0.0008611862,0.00040034423,0.0003389169,0.0025314172],"category_scores_gemma":[0.0012216734,0.00020662983,0.00030946688,0.0010311464,0.00035346893,0.00030021367,0.0006630158,0.00048721483,0.00017708994],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012022244,0.00004214793,0.9989617,0.000012910771,0.000011434547,0.00009062909,0.00023459873,0.000013806331,0.000057668258,0.00000975446,0.0000675634,0.0004858985],"study_design_scores_gemma":[8.2476697e-7,0.000022735518,0.9986834,0.000011031746,0.000006945046,0.00008013922,0.0010406572,0.000036485555,0.000011684628,0.000005531452,0.00009880757,0.0000018233454],"about_ca_topic_score_codex":0.036816537,"about_ca_topic_score_gemma":0.044867467,"teacher_disagreement_score":0.036816537,"about_ca_system_score_codex":0.0006704879,"about_ca_system_score_gemma":0.0010040475,"threshold_uncertainty_score":0.07320446},"labels":[],"label_agreement":null},{"id":"W4408121597","doi":"10.6000/1929-6029.2025.14.11","title":"Chronic kidney Disease Classification through Hybrid Feature Selection and Ensemble Deep Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Feature selection; Ensemble learning; Computer science; Feature (linguistics); Kidney disease; Pattern recognition (psychology); Selection (genetic algorithm); Machine learning; Medicine; Internal medicine","score_opus":0.15498784566678234,"score_gpt":0.5754985133114356,"score_spread":0.4205106676446533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408121597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29537153,0.0016192069,0.6986564,0.0005016834,0.00014415338,0.00011694868,0.00051463407,0.0019568137,0.0011186071],"genre_scores_gemma":[0.92928576,0.00031183104,0.06796352,0.00018170528,0.00007241522,0.00010980069,0.0009542474,0.00003214773,0.0010884699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931264,0.00016101352,0.000049938157,0.00016639149,0.00017477585,0.0001353321],"domain_scores_gemma":[0.99930346,0.000249411,0.000058258054,0.000076778764,0.00026730858,0.000044800247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015019687,0.0009429627,0.0016122628,0.0014001156,0.00039961174,0.00067132484,0.0009734667,0.0006763425,0.0004573789],"category_scores_gemma":[0.0017980635,0.00032109534,0.00123831,0.0009869034,0.00018602735,0.0010209491,0.00081091066,0.00087180664,0.00018834094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051278406,0.0005681648,0.025252985,0.00005969898,0.0005149486,0.0002459139,0.00010289876,0.30208156,0.0071170377,0.0008290971,0.0048236423,0.6578912],"study_design_scores_gemma":[0.000009726183,0.0000732552,0.0017107907,0.000005794386,0.000041093543,0.00004237984,0.0000098662695,0.99610656,0.0011073671,0.00067166745,0.00021227563,0.000009176014],"about_ca_topic_score_codex":0.0094529465,"about_ca_topic_score_gemma":0.009997879,"teacher_disagreement_score":0.0094529465,"about_ca_system_score_codex":0.00056590966,"about_ca_system_score_gemma":0.00082514074,"threshold_uncertainty_score":0.018795848},"labels":[],"label_agreement":null},{"id":"W4408295849","doi":"10.6000/1929-6029.2025.14.12","title":"The Effectiveness of the SOBUMIL mHealth App in Enhancing Early Detection of Pregnancy Complications in Bogor Regency, Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Quality and Satisfaction","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"mHealth; Pregnancy; Obstetrics; Computer science; Medicine; Internet privacy; Nursing; Biology; Psychological intervention","score_opus":0.11237965957889538,"score_gpt":0.5679856191300371,"score_spread":0.4556059595511417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408295849","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99734193,0.0002946755,0.00011597702,0.00022340896,0.000013549657,0.00016264559,0.000043190725,0.000019316327,0.0017852377],"genre_scores_gemma":[0.9966426,0.00052175176,0.0012456253,0.00014737358,0.000009053246,0.00027873504,0.000037358197,0.000003669316,0.0011138662],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994431,0.00032143865,0.00004023346,0.000045973735,0.0000766937,0.000072506045],"domain_scores_gemma":[0.9989201,0.00063067186,0.00013755875,0.00003137347,0.00006873271,0.00021160732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014412681,0.00021744362,0.00025632564,0.00023037753,0.0003683067,0.0005988985,0.00029678302,0.00020004685,0.0016030768],"category_scores_gemma":[0.004004469,0.00011856967,0.00030745217,0.00011196591,0.00026801607,0.00035938853,0.00065079075,0.00039201896,0.00016246492],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055062566,0.021247236,0.22172852,0.0027319,0.00020100406,0.0014013925,0.020401668,0.00047115138,0.009540661,0.0002536054,0.0028638216,0.7136528],"study_design_scores_gemma":[0.0007824952,0.015332039,0.9551245,0.0008623165,0.00049214164,0.0005371936,0.013443079,0.0020518794,0.004294941,0.00015386894,0.0068584355,0.000067158326],"about_ca_topic_score_codex":0.0049122325,"about_ca_topic_score_gemma":0.009472922,"teacher_disagreement_score":0.0049122325,"about_ca_system_score_codex":0.00041739296,"about_ca_system_score_gemma":0.0011511281,"threshold_uncertainty_score":0.009767294},"labels":[],"label_agreement":null},{"id":"W4408808178","doi":"10.6000/1929-6029.2025.14.14","title":"Policy Innovation in Healthcare: Exploring the Adoption and Implementation of Telemedicine","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Telemedicine; Health care; Business; Knowledge management; Computer science; Economics; Economic growth","score_opus":0.13921119634843857,"score_gpt":0.5617443797499995,"score_spread":0.4225331834015609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408808178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95927143,0.0014876773,0.005374575,0.016892517,0.000040727366,0.00029150693,0.00009654275,0.000014889519,0.016530097],"genre_scores_gemma":[0.99753857,0.00062189467,0.0011874378,0.00036604397,0.00001342928,0.000101434365,0.000026838805,0.0000027547355,0.0001416273],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.96190226,0.02825751,0.0018199957,0.0013326043,0.004017929,0.0026697153],"domain_scores_gemma":[0.85416293,0.11828767,0.016689748,0.0022103717,0.0059909425,0.00265824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036741637,0.000310841,0.00040573368,0.0038189504,0.0019951502,0.0061991587,0.0014160082,0.0020626816,0.0021004626],"category_scores_gemma":[0.08909236,0.0003274416,0.0007667113,0.004573133,0.005424432,0.006453126,0.00405481,0.002736873,0.00012304091],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002112806,0.0015611218,0.5159865,0.0014693086,0.0002761285,0.00094626565,0.18030763,0.0051239617,0.0009053434,0.16292807,0.0015483933,0.12873593],"study_design_scores_gemma":[0.00010356288,0.0011084058,0.5230504,0.0038542752,0.00020994226,0.00067598297,0.36001554,0.022007495,0.0019223697,0.050601877,0.03631877,0.0001313254],"about_ca_topic_score_codex":0.008281997,"about_ca_topic_score_gemma":0.004443578,"teacher_disagreement_score":0.036741637,"about_ca_system_score_codex":0.014807749,"about_ca_system_score_gemma":0.019027948,"threshold_uncertainty_score":0.1943106},"labels":[],"label_agreement":null},{"id":"W4408808274","doi":"10.6000/1929-6029.2025.14.15","title":"Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Artificial intelligence; Selection (genetic algorithm); Feature (linguistics); Pattern recognition (psychology); Computer science; Linguistics; Philosophy","score_opus":0.03650406747325213,"score_gpt":0.43746930739303963,"score_spread":0.4009652399197875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408808274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27891603,0.0035352707,0.70930564,0.00078716327,0.0002309368,0.0002650566,0.0013847093,0.0031068712,0.0024684123],"genre_scores_gemma":[0.8359627,0.0005890109,0.15833384,0.00020431583,0.0001121793,0.00027187463,0.0021257715,0.00007654445,0.002323701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940777,0.00020001509,0.000058696023,0.00010974314,0.00011953611,0.00010423927],"domain_scores_gemma":[0.9991984,0.0004083384,0.00006024037,0.000074589676,0.00022183196,0.000036586654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001928675,0.0009456051,0.0010957672,0.0020389967,0.00044300559,0.0008148479,0.000579205,0.0005856301,0.0011986775],"category_scores_gemma":[0.0029593376,0.00022424311,0.0010180731,0.001582525,0.00027069973,0.0005947248,0.0006415185,0.0007970511,0.00045712767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007255544,0.00039516235,0.020985916,0.00019005519,0.00029335477,0.00024155421,0.000117740856,0.09687082,0.014102001,0.0034507737,0.008846129,0.85378087],"study_design_scores_gemma":[0.000041581196,0.00016101223,0.005038614,0.000030056684,0.000054963813,0.00009282564,0.000038183225,0.9839676,0.004592984,0.0041412734,0.0018220672,0.00001883028],"about_ca_topic_score_codex":0.00569609,"about_ca_topic_score_gemma":0.00529351,"teacher_disagreement_score":0.00569609,"about_ca_system_score_codex":0.00053493807,"about_ca_system_score_gemma":0.0010420509,"threshold_uncertainty_score":0.011325896},"labels":[],"label_agreement":null},{"id":"W4408808277","doi":"10.6000/1929-6029.2025.14.16","title":"A Choice of Performance Metrics for Evaluating Predictive Accuracy of Survival Models","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Statistics; Econometrics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.6491725716129512,"score_gpt":0.6344758245705483,"score_spread":0.014696747042402869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408808277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33214307,0.013231742,0.63145244,0.0035077976,0.0008713315,0.0006416529,0.005271184,0.002438525,0.010442287],"genre_scores_gemma":[0.8945378,0.0016938543,0.09795432,0.0003117512,0.00025688382,0.00029496566,0.003967587,0.0002026763,0.00078019133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98057705,0.01096162,0.0019806337,0.0018778166,0.0039036027,0.0006992749],"domain_scores_gemma":[0.8713137,0.10366091,0.0076860604,0.00756296,0.008573095,0.0012033188],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.051660124,0.002264112,0.0021013012,0.008838721,0.0011776866,0.004180542,0.001850727,0.002471291,0.0013472107],"category_scores_gemma":[0.16316348,0.00042939038,0.0021498012,0.0064957705,0.0018537474,0.0038222896,0.0026597031,0.0031431871,0.0005123932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009310615,0.00046332483,0.21353532,0.0012530881,0.002082701,0.0004639299,0.0008846045,0.49978808,0.0022334512,0.022924544,0.011714731,0.2437252],"study_design_scores_gemma":[0.000055436714,0.0007538648,0.032986183,0.0005052273,0.00029757599,0.0004951266,0.00089162233,0.93783915,0.0030493466,0.018971346,0.003979818,0.00017531551],"about_ca_topic_score_codex":0.006349529,"about_ca_topic_score_gemma":0.004718349,"teacher_disagreement_score":0.9483399,"about_ca_system_score_codex":0.00145393,"about_ca_system_score_gemma":0.0026910782,"threshold_uncertainty_score":0.27320808},"labels":[],"label_agreement":null},{"id":"W4408833595","doi":"10.6000/1929-6029.2025.14.13","title":"The Effect of Emotional Regulation for the Successful Treatment of Emotional Dependence in Young People","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Emotional regulation; Developmental psychology","score_opus":0.05172527749820594,"score_gpt":0.5256649705582774,"score_spread":0.4739396930600715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408833595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.978112,0.0044182586,0.0013166044,0.0008329509,0.00012980065,0.00028692128,0.00004057702,0.000024950165,0.014837893],"genre_scores_gemma":[0.99383426,0.0022916833,0.0028479798,0.0001863352,0.000055100383,0.00017292735,0.000021666823,0.0000040927703,0.00058595935],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.99747235,0.0019261263,0.00009837559,0.000053155305,0.000329585,0.000120417746],"domain_scores_gemma":[0.99232274,0.00639386,0.00035459962,0.00015358403,0.00015395299,0.00062123867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030132,0.00017552251,0.00025537185,0.00041915334,0.0004313771,0.00038732376,0.00021619168,0.00033655585,0.002398969],"category_scores_gemma":[0.007429452,0.000085340806,0.00051892956,0.00021342348,0.00035042723,0.00025573003,0.00060251664,0.0005263062,0.0001261697],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034154675,0.011839232,0.047288805,0.0024040407,0.00034584556,0.00033745912,0.0050120973,0.0004633311,0.007456823,0.0020850238,0.0017930276,0.9175589],"study_design_scores_gemma":[0.0012965254,0.027622784,0.9372298,0.0016986266,0.0012032115,0.0009994255,0.0045154807,0.0013752487,0.007048155,0.0016679857,0.015292006,0.00005074732],"about_ca_topic_score_codex":0.00048066548,"about_ca_topic_score_gemma":0.0009366803,"teacher_disagreement_score":0.0030132,"about_ca_system_score_codex":0.00035783253,"about_ca_system_score_gemma":0.0008889079,"threshold_uncertainty_score":0.01593548},"labels":[],"label_agreement":null},{"id":"W4408972584","doi":"10.6000/1929-6029.2025.14.17","title":"Adapting and Validating a Motor Intelligence Assessment Tool for Children with Intellectual Disabilities: Prioritizing Movement and Sensory-Motor Integration","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Children's Physical and Motor Development","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Sensory system; Psychology; Movement assessment; Physical medicine and rehabilitation; Cognitive psychology; Motor skill; Neuroscience; Medicine","score_opus":0.05309117053073927,"score_gpt":0.44442144835406616,"score_spread":0.3913302778233269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408972584","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885117,0.00036673405,0.0035078328,0.00016028914,0.00005996563,0.002468796,0.00188062,0.000104413,0.0029396014],"genre_scores_gemma":[0.9350538,0.000938065,0.050376404,0.00017216172,0.000028649325,0.0069582053,0.0048386836,0.000030689225,0.0016033133],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974867,0.00053347956,0.000744951,0.00025278886,0.0007597728,0.00022228307],"domain_scores_gemma":[0.9958495,0.0010202335,0.0009660745,0.00026865528,0.0016758498,0.00021960595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045918515,0.0008505637,0.0006101941,0.0020436205,0.00044615328,0.0007273275,0.0010226289,0.0006732877,0.0012041109],"category_scores_gemma":[0.009159963,0.00035579866,0.001291222,0.0012393662,0.00048500486,0.00073557254,0.0012738857,0.00096802023,0.0004648861],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030048925,0.0007542554,0.89445007,0.0004443642,0.00012220678,0.00053807185,0.0019456221,0.0011066823,0.0033677858,0.00021668253,0.0024514694,0.09430233],"study_design_scores_gemma":[0.00008021891,0.0007127058,0.99096924,0.00016394774,0.000051801613,0.0005837753,0.0010576809,0.0014398529,0.0018567544,0.00012531156,0.0029300463,0.000028718194],"about_ca_topic_score_codex":0.0063623134,"about_ca_topic_score_gemma":0.016421683,"teacher_disagreement_score":0.0063623134,"about_ca_system_score_codex":0.00094996893,"about_ca_system_score_gemma":0.0023293954,"threshold_uncertainty_score":0.024284303},"labels":[],"label_agreement":null},{"id":"W4409585537","doi":"10.6000/1929-6029.2025.14.20","title":"Innovative SiKaRen Smartphone Application Model: A Breakthrough in Enhancing IMP Cadres’ Knowledge and Attitudes Toward Family Planning","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Business; Knowledge management; Psychology; Computer science","score_opus":0.06711255088428571,"score_gpt":0.4925689061276852,"score_spread":0.42545635524339953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409585537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85430396,0.005963541,0.048872504,0.0066720564,0.0005288405,0.0028406922,0.0010509244,0.0018100124,0.077957466],"genre_scores_gemma":[0.8589175,0.006167528,0.101741925,0.0012470249,0.00016581947,0.0021906453,0.0006308517,0.000067160756,0.028871603],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99959236,0.00017673244,0.000026135545,0.000047067497,0.000108945795,0.00004885655],"domain_scores_gemma":[0.9995253,0.00022008696,0.00004175734,0.000036313537,0.00010223786,0.00007439026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063971983,0.00040924764,0.00025841064,0.0005423245,0.0002661483,0.00064452324,0.00064761116,0.0005405791,0.0073595215],"category_scores_gemma":[0.0013349582,0.00013076793,0.00044624304,0.0003009361,0.0002362072,0.00087121,0.00086587993,0.00054314884,0.00097264035],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007126604,0.0030295795,0.015789177,0.0029044063,0.00007685711,0.0010445835,0.005326385,0.0006047163,0.020917894,0.0029699453,0.010400182,0.93622357],"study_design_scores_gemma":[0.0016389885,0.03595553,0.32597947,0.005064346,0.0016380736,0.0109183965,0.021984123,0.026569461,0.047611404,0.006972055,0.51518553,0.00048257798],"about_ca_topic_score_codex":0.0007459256,"about_ca_topic_score_gemma":0.0020210547,"teacher_disagreement_score":0.0073595215,"about_ca_system_score_codex":0.00019658972,"about_ca_system_score_gemma":0.00062527513,"threshold_uncertainty_score":0.024620116},"labels":[],"label_agreement":null},{"id":"W4409585592","doi":"10.6000/1929-6029.2025.14.18","title":"Scoring System Model for Early Detection of Maternity Blues in Bukittinggi, West Sumatera, Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Infant Health and Development","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Blues; History; Art history","score_opus":0.11217247543985943,"score_gpt":0.5443106763518469,"score_spread":0.4321382009119875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409585592","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9166551,0.0010863193,0.06616493,0.0012898927,0.00021354899,0.001975479,0.0030993125,0.00064181257,0.008873576],"genre_scores_gemma":[0.9618334,0.00038548582,0.033749193,0.0000682226,0.000023860182,0.0007475607,0.0017529454,0.000018799006,0.0014205021],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981,0.0009917279,0.00020465972,0.00019185265,0.00035748832,0.00015423146],"domain_scores_gemma":[0.9961563,0.0016088985,0.0006442106,0.00012822285,0.0012970443,0.000165449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00476129,0.00082257885,0.00068225997,0.0020133925,0.00042377345,0.0014431119,0.0009164237,0.00046786448,0.0030206665],"category_scores_gemma":[0.012355013,0.00024444575,0.001013686,0.0009379994,0.00024566398,0.0005434862,0.00075313356,0.0006333177,0.0007482179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047310136,0.0004999513,0.8788557,0.00023254786,0.00023915368,0.0003400087,0.00042952338,0.018398952,0.0006272248,0.0008647751,0.0055588516,0.09348021],"study_design_scores_gemma":[0.00011916668,0.0012121685,0.5025397,0.0003517493,0.00035020593,0.00066918146,0.0015478919,0.48523992,0.0010955584,0.0023716693,0.0044122995,0.00009039245],"about_ca_topic_score_codex":0.01082049,"about_ca_topic_score_gemma":0.008481902,"teacher_disagreement_score":0.01082049,"about_ca_system_score_codex":0.0011074049,"about_ca_system_score_gemma":0.00179172,"threshold_uncertainty_score":0.0251804},"labels":[],"label_agreement":null},{"id":"W4409585667","doi":"10.6000/1929-6029.2025.14.19","title":"Analysis of Occupational Health and Safety Risk Management: Hazard Identification, Risk Assessment, and Risk Control-HIRARC for Workers at Health Quarantine Offices in Makassar, Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quarantine; Identification (biology); Hazard analysis; Environmental health; Hazard; Business; Risk assessment; Risk analysis (engineering); Medicine; Engineering; Computer science; Computer security; Reliability engineering; Biology","score_opus":0.075726913603491,"score_gpt":0.574032459853643,"score_spread":0.49830554625015205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409585667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984366,0.00013964802,0.0007387211,0.000060965715,0.0000034764241,0.00009594052,0.0000624861,0.0000074018435,0.0004546394],"genre_scores_gemma":[0.9982906,0.0001209641,0.001231141,0.000014165683,0.0000035700307,0.000053570304,0.000074840114,0.0000013047381,0.00020976744],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976533,0.0010460156,0.00033164426,0.00015371556,0.00067730894,0.0001380212],"domain_scores_gemma":[0.9960062,0.001518696,0.0014278084,0.00018613912,0.00066488894,0.00019632853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032711537,0.00030809594,0.0002836872,0.0008197219,0.0003487999,0.00058013346,0.00038778447,0.00020965388,0.0009623271],"category_scores_gemma":[0.0060262373,0.00018116987,0.0005378285,0.0005893116,0.00017951633,0.00038745304,0.00046786715,0.00032176706,0.00012366656],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020250957,0.0005064862,0.9536893,0.00017787275,0.00008279293,0.0002465885,0.0012648802,0.00044130237,0.0005438942,0.00003980917,0.00014301462,0.042661514],"study_design_scores_gemma":[0.0000097620605,0.0006160995,0.9943183,0.00003892339,0.000059609294,0.00016048513,0.0022544165,0.001925571,0.0003721164,0.00002957227,0.00020579244,0.0000094290235],"about_ca_topic_score_codex":0.0046888585,"about_ca_topic_score_gemma":0.0059250365,"teacher_disagreement_score":0.0046888585,"about_ca_system_score_codex":0.0005563937,"about_ca_system_score_gemma":0.0012574136,"threshold_uncertainty_score":0.017299712},"labels":[],"label_agreement":null},{"id":"W4409681230","doi":"10.6000/1929-6029.2025.14.21","title":"Spatial Analysis Risk Factors of Pneumonia Incidence in Toddlers Gowa Regency","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Quality and Satisfaction","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Incidence (geometry); Pneumonia; Environmental health; Geography; Medicine; Mathematics; Internal medicine","score_opus":0.156245192134287,"score_gpt":0.6102221531057344,"score_spread":0.45397696097144735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409681230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985037,0.00020736229,0.00016142862,0.00009101757,0.0000049207015,0.000014531956,0.0004928319,0.000005746259,0.0005184809],"genre_scores_gemma":[0.9988972,0.00012925922,0.00029797052,0.0000151185,0.0000052718824,0.000021207752,0.00036907624,0.0000013646663,0.0002635005],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996573,0.00010441545,0.000032761134,0.000066536166,0.000065230946,0.00007385419],"domain_scores_gemma":[0.99933016,0.000094944575,0.00028752763,0.000047277223,0.00014558481,0.000094484996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034805186,0.00030850683,0.00023395818,0.0012009643,0.0005905894,0.00040594355,0.00040590586,0.00019810995,0.0015027312],"category_scores_gemma":[0.0016227731,0.00012998124,0.0005559911,0.0011757335,0.00030184092,0.00019331084,0.0006367159,0.00024255824,0.00014616316],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017135857,0.000010529304,0.99784255,0.000016126796,0.00003525505,0.00009059882,0.00017486226,0.000107457396,0.00008271622,0.00002972464,0.00014313948,0.0014499356],"study_design_scores_gemma":[0.0000010222218,0.000017400167,0.9982729,0.000015923419,0.00002193843,0.000094941504,0.0009734506,0.00033923826,0.000029555298,0.000029526169,0.00020183402,0.0000022761537],"about_ca_topic_score_codex":0.14364192,"about_ca_topic_score_gemma":0.18325765,"teacher_disagreement_score":0.14364192,"about_ca_system_score_codex":0.000724097,"about_ca_system_score_gemma":0.0008275869,"threshold_uncertainty_score":0.2856117},"labels":[],"label_agreement":null},{"id":"W4409771786","doi":"10.6000/1929-6029.2025.14.22","title":"Raking Method as a Tool for Improving Representativeness in Non-Probability Studies","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Statistics; Econometrics; Computer science; Data science; Psychology; Mathematics","score_opus":0.22934198842755865,"score_gpt":0.6295877522867993,"score_spread":0.40024576385924066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409771786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014480167,0.025358211,0.96702987,0.0016448552,0.00057878584,0.0013568399,0.00021815952,0.0004163411,0.00194889],"genre_scores_gemma":[0.040574756,0.015507815,0.93348646,0.0013828246,0.0006570199,0.0069518997,0.00029414476,0.00033815164,0.00080705027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.67775047,0.26520216,0.023473792,0.012155462,0.02058054,0.0008376138],"domain_scores_gemma":[0.44262153,0.48759332,0.022993267,0.026869802,0.019105839,0.00081622007],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.22946557,0.0023594396,0.006641802,0.011159031,0.0021074393,0.0066042435,0.006112364,0.0037159068,0.0064289705],"category_scores_gemma":[0.51396894,0.0019038336,0.00786736,0.011287025,0.0060788933,0.005676038,0.0051272865,0.0057162237,0.0014435758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038099667,0.00008891082,0.009110756,0.027069602,0.0074883704,0.0003170015,0.0039730286,0.019618647,0.0008056328,0.15542668,0.0104828775,0.76523745],"study_design_scores_gemma":[0.0007390296,0.0011058729,0.012878363,0.028238192,0.010433305,0.00210033,0.0015190333,0.08810694,0.00502462,0.6673264,0.18173215,0.0007957924],"about_ca_topic_score_codex":0.0031830932,"about_ca_topic_score_gemma":0.0031800899,"teacher_disagreement_score":0.22946557,"about_ca_system_score_codex":0.0031356674,"about_ca_system_score_gemma":0.009515054,"threshold_uncertainty_score":0.9502061},"labels":[],"label_agreement":null},{"id":"W4409772545","doi":"10.6000/1929-6029.2025.14.24","title":"Management of Antipsychotic Therapy in Patients with Schizophrenia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Schizophrenia research and treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Schizophrenia (object-oriented programming); Antipsychotic; Psychotherapist; Psychology; Psychiatry; Medicine","score_opus":0.03015544630936237,"score_gpt":0.42506667917920865,"score_spread":0.3949112328698463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409772545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99558854,0.001028012,0.00013067655,0.0011597087,0.000011533765,0.000026901851,0.0000572362,0.000005741828,0.0019917502],"genre_scores_gemma":[0.99873155,0.0006359403,0.0002574005,0.00020518704,0.00001057509,0.000012654299,0.000044097433,7.4597847e-7,0.00010191631],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986394,0.0006496457,0.00022308137,0.00006110407,0.00032921782,0.00009760473],"domain_scores_gemma":[0.99726427,0.00064357114,0.0015951149,0.00006836788,0.00017065839,0.0002578689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079296477,0.0000918208,0.00020407779,0.00034211433,0.00063360203,0.00041089812,0.00017502748,0.00028482897,0.00082475523],"category_scores_gemma":[0.0073670074,0.00010119061,0.00018243077,0.0004779746,0.0002645764,0.00037989777,0.00035977885,0.00041767457,0.000107424945],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074081145,0.00014854492,0.9724949,0.000099924546,0.00002293485,0.00081678905,0.0031310692,0.0001351863,0.0004629323,0.00019010728,0.00058890105,0.021834554],"study_design_scores_gemma":[0.00001644557,0.00029929113,0.98951447,0.00015088118,0.000027187492,0.0019931707,0.0052273767,0.00054499996,0.00013589949,0.00042090364,0.0016508168,0.000018501225],"about_ca_topic_score_codex":0.0039780224,"about_ca_topic_score_gemma":0.0059253676,"teacher_disagreement_score":0.0039780224,"about_ca_system_score_codex":0.0005752548,"about_ca_system_score_gemma":0.001412491,"threshold_uncertainty_score":0.007909715},"labels":[],"label_agreement":null},{"id":"W4409772988","doi":"10.6000/1929-6029.2025.14.23","title":"Strengthening the Health System to Address the COVID-19 Surge: An Empirical Study in South Kalimantan Province, Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Geography; Socioeconomics; Economics; Medicine; Disease","score_opus":0.11988948401036749,"score_gpt":0.532921622464042,"score_spread":0.4130321384536745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409772988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967078,0.00007790949,0.000036275775,0.0006492192,0.0000034608265,0.00003375671,0.00003973418,0.0000011068304,0.0024506955],"genre_scores_gemma":[0.9996018,0.00007617212,0.000029317018,0.0000670768,0.0000015077535,0.000011026169,0.000015757458,4.5131773e-7,0.0001970061],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99896455,0.0003790233,0.000055881737,0.00004926009,0.00011962812,0.00043162113],"domain_scores_gemma":[0.9966882,0.001181221,0.0008578977,0.0000789174,0.00034174367,0.0008521177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016940442,0.00017687782,0.0001871887,0.00057186704,0.0018792651,0.0014205024,0.00056617183,0.00036868697,0.0031962711],"category_scores_gemma":[0.0041661495,0.00019878526,0.0001899042,0.0010968137,0.0016691572,0.0011484432,0.0011385444,0.001014209,0.00019920742],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007857602,0.00072159513,0.92486185,0.00030909295,0.000041846586,0.0024517898,0.053089455,0.0006101535,0.000590692,0.002319115,0.0014275335,0.013498289],"study_design_scores_gemma":[0.000019986723,0.00014933909,0.7605233,0.00018135998,0.000028389743,0.00027203944,0.23448597,0.0009471063,0.00018748277,0.0002002384,0.002992571,0.000012225984],"about_ca_topic_score_codex":0.1330758,"about_ca_topic_score_gemma":0.21865274,"teacher_disagreement_score":0.1330758,"about_ca_system_score_codex":0.0057429527,"about_ca_system_score_gemma":0.0108087575,"threshold_uncertainty_score":0.26460242},"labels":[],"label_agreement":null},{"id":"W4410057785","doi":"10.6000/1929-6029.2025.14.25","title":"The Influence of Emotional Intelligence on Coping Skills","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Emotional Intelligence and Performance","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Emotional intelligence; Psychology; Coping (psychology); Applied psychology; Cognitive psychology; Social psychology; Clinical psychology","score_opus":0.07061032960026911,"score_gpt":0.5335184200869036,"score_spread":0.4629080904866345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410057785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935539,0.0010780552,0.00034572894,0.00011675949,0.000019368064,0.000015541542,0.00012776669,0.00001200419,0.004730938],"genre_scores_gemma":[0.99939,0.00022638311,0.00011036599,0.000015447993,0.000012594224,0.000006426283,0.000051527157,0.0000026115868,0.00018457424],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999501,0.00021124832,0.000039495393,0.00005630498,0.00012447425,0.000067440764],"domain_scores_gemma":[0.99503917,0.002978153,0.00093239546,0.00019875201,0.00026529742,0.0005863017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006203147,0.00032814383,0.00025139668,0.0006004434,0.00015776236,0.000663507,0.0001582262,0.00015442522,0.003546773],"category_scores_gemma":[0.0050099418,0.00006776941,0.00031710856,0.00038891402,0.00035326125,0.00016849539,0.00039570945,0.00034353606,0.00021063648],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063848967,0.0004282578,0.92640626,0.00015322525,0.00034704315,0.0004183354,0.00069514196,0.000366411,0.002392564,0.00029117503,0.00028875814,0.06757421],"study_design_scores_gemma":[0.000003468504,0.00020373744,0.9985875,0.000016949962,0.000045177054,0.00019767768,0.00012448638,0.00014557918,0.00024463722,0.0001404773,0.0002865535,0.0000037520178],"about_ca_topic_score_codex":0.00045747028,"about_ca_topic_score_gemma":0.00031960828,"teacher_disagreement_score":0.003546773,"about_ca_system_score_codex":0.00013678164,"about_ca_system_score_gemma":0.00018816271,"threshold_uncertainty_score":0.011865139},"labels":[],"label_agreement":null},{"id":"W4410058380","doi":"10.6000/1929-6029.2025.14.26","title":"The Impact COVID-19 Pandemic on Coronary Heart Disease Deaths: Using Bayesian Lorenz Curve and Gini-Index Distribution","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"İstanbul Medipol Üniversitesi","keywords":"Lorenz curve; Pandemic; Coronavirus disease 2019 (COVID-19); Index (typography); Bayesian probability; Distribution (mathematics); 2019-20 coronavirus outbreak; Statistics; Medicine; Mathematics; Gini coefficient; Internal medicine; Disease; Virology; Inequality; Computer science; Economic inequality; Infectious disease (medical specialty); Mathematical analysis","score_opus":0.12448344250596154,"score_gpt":0.47326357819871584,"score_spread":0.3487801356927543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410058380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69091165,0.003049334,0.2906165,0.0028124098,0.00012239875,0.00033548658,0.0025093225,0.00045947003,0.009183523],"genre_scores_gemma":[0.9832817,0.0007905358,0.01354835,0.00010993824,0.00005751742,0.00009154167,0.0010939018,0.00004795281,0.0009784888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9974694,0.0015564329,0.00009219014,0.0004053029,0.00028781145,0.00018893635],"domain_scores_gemma":[0.99071395,0.006591406,0.0012400013,0.00047581206,0.00078737683,0.00019145523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00821545,0.00068907026,0.00082887296,0.0025592442,0.0004779529,0.0018727937,0.0007930721,0.0010770875,0.0020361051],"category_scores_gemma":[0.025550382,0.00034496942,0.001586317,0.0012302786,0.0010097274,0.0018346373,0.0011487647,0.001306498,0.0003201484],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038771122,0.00012839587,0.3467498,0.00030012245,0.00075456826,0.00058424467,0.0008385783,0.527189,0.0010813476,0.037712373,0.006026575,0.07824721],"study_design_scores_gemma":[0.000018024517,0.00014321343,0.06075854,0.00011811408,0.00009834199,0.0003486576,0.0003777065,0.9068542,0.00045490311,0.028249713,0.002508638,0.000069995294],"about_ca_topic_score_codex":0.014082941,"about_ca_topic_score_gemma":0.0061940462,"teacher_disagreement_score":0.014082941,"about_ca_system_score_codex":0.0015742606,"about_ca_system_score_gemma":0.00092134054,"threshold_uncertainty_score":0.04344791},"labels":[],"label_agreement":null},{"id":"W4410058466","doi":"10.6000/1929-6029.2025.14.27","title":"RPCA with Log-Schatten Norm and Adaptive Histogram Equalization for Medical Imaging","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptive histogram equalization; Histogram equalization; Norm (philosophy); Histogram; Computer vision; Computer science; Artificial intelligence; Mathematics; Image (mathematics); Philosophy; Epistemology","score_opus":0.0207594489877782,"score_gpt":0.3882877014501712,"score_spread":0.367528252462393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410058466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022614377,0.00045474202,0.9962406,0.00019664124,0.000034274188,0.000027803402,0.000040418774,0.00032449828,0.00041965191],"genre_scores_gemma":[0.1298271,0.0018189544,0.86353475,0.0003013444,0.00023102904,0.0003032808,0.00060957915,0.00023987005,0.0031340912],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988877,0.0004799665,0.00007746429,0.00018682117,0.00032354947,0.00004450377],"domain_scores_gemma":[0.9983473,0.00083170406,0.00014654124,0.000254792,0.00036852976,0.000051120584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021470462,0.00089838175,0.00077944406,0.0012291917,0.0003913924,0.0008956097,0.0010590693,0.0009887301,0.0018515959],"category_scores_gemma":[0.0069363057,0.00038861975,0.0011077626,0.0015889965,0.0010670115,0.0012045412,0.0010424108,0.0016226966,0.00079559645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022105606,0.00013204342,0.0010297797,0.0004108438,0.00025103195,0.00018516752,0.00009949932,0.30564153,0.023177577,0.037921138,0.010429319,0.620501],"study_design_scores_gemma":[0.00001479955,0.000048318558,0.00046070863,0.000019649176,0.000022976226,0.000108356966,0.000010361066,0.9775729,0.004133453,0.013960442,0.0036208313,0.00002724659],"about_ca_topic_score_codex":0.003891424,"about_ca_topic_score_gemma":0.0041087624,"teacher_disagreement_score":0.003891424,"about_ca_system_score_codex":0.00050287996,"about_ca_system_score_gemma":0.0022873583,"threshold_uncertainty_score":0.011354804},"labels":[],"label_agreement":null},{"id":"W4410602017","doi":"10.6000/1929-6029.2025.14.28","title":"Response Adaptive Randomization Using Biomarkers with Exponentially Decreasing Probability Sequence","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sequence (biology); Randomization; Exponential growth; Statistics; Mathematics; Computer science; Econometrics; Applied mathematics; Biology; Bioinformatics; Mathematical analysis; Clinical trial; Genetics","score_opus":0.2564553604632724,"score_gpt":0.5629981862205483,"score_spread":0.3065428257572759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410602017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014465331,0.00052855135,0.9828188,0.00050027255,0.00017410038,0.0005865665,0.00006955549,0.00018639826,0.0006703484],"genre_scores_gemma":[0.5171647,0.0009722644,0.47284874,0.0011249633,0.00033391884,0.0041190316,0.00020923285,0.000085562206,0.0031415746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9552725,0.03833979,0.0008560401,0.0029833647,0.0018876342,0.00066067284],"domain_scores_gemma":[0.946608,0.044285107,0.0033932403,0.0028231088,0.0021375278,0.00075306953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0327724,0.0015862234,0.0027423801,0.0013708782,0.0004917029,0.0013232972,0.0021557165,0.0022212542,0.0045505464],"category_scores_gemma":[0.06204612,0.0006454788,0.0013963624,0.0011150541,0.002114527,0.0025388133,0.0014071527,0.002331189,0.000741455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0080526015,0.0013351202,0.007893186,0.0018084231,0.0009967526,0.000531217,0.0006091386,0.30630934,0.00905471,0.4189053,0.0029915632,0.24151255],"study_design_scores_gemma":[0.0022744788,0.0058739185,0.0016426151,0.00020362208,0.0003842708,0.00041995256,0.000079387486,0.8319469,0.004299211,0.14684114,0.005892186,0.0001422846],"about_ca_topic_score_codex":0.00033359445,"about_ca_topic_score_gemma":0.00018808467,"teacher_disagreement_score":0.0327724,"about_ca_system_score_codex":0.00097506604,"about_ca_system_score_gemma":0.0021081546,"threshold_uncertainty_score":0.1733191},"labels":[],"label_agreement":null},{"id":"W4410830770","doi":"10.6000/1929-6029.2025.14.29","title":"A Hybrid Time Series–Regression Model for Tuberculosis Forecasting in Resource-Limited Settings","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Mean squared error; Linear regression; Time series; Computer science; Regression; Regression analysis; Statistics; Series (stratigraphy); Econometrics; Data mining; Machine learning; Mathematics","score_opus":0.16787631715770784,"score_gpt":0.5080204323417051,"score_spread":0.3401441151839973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410830770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8128203,0.00068351836,0.17998289,0.00081526913,0.00013867856,0.00009293114,0.0008322642,0.0006502248,0.0039839093],"genre_scores_gemma":[0.9760424,0.00015156185,0.021794228,0.00004284132,0.000025083984,0.00005953501,0.00036673684,0.00001178877,0.001505736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.00020105597,0.000027419375,0.0000817309,0.000047045374,0.000049444763],"domain_scores_gemma":[0.9993414,0.0004359963,0.00006232082,0.00002662546,0.000111141155,0.000022471062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014202684,0.00054155383,0.00067356083,0.0006607808,0.00027907625,0.00090599194,0.00068070774,0.0006329915,0.001274835],"category_scores_gemma":[0.0017737251,0.00021826412,0.00068541,0.0007167278,0.00012709622,0.0006152936,0.0004022085,0.000600922,0.00022281242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033939618,0.00024432552,0.013072024,0.00006163487,0.00016455335,0.00013122403,0.000078592595,0.93416166,0.0020900453,0.001888647,0.0008630099,0.046904877],"study_design_scores_gemma":[0.0000060994957,0.000041396626,0.0008943552,0.0000027336605,0.000012626919,0.0000069654707,0.00001236492,0.9984875,0.00014219014,0.000237351,0.00015127967,0.0000050892527],"about_ca_topic_score_codex":0.01877014,"about_ca_topic_score_gemma":0.0119796125,"teacher_disagreement_score":0.01877014,"about_ca_system_score_codex":0.0005943898,"about_ca_system_score_gemma":0.0006684857,"threshold_uncertainty_score":0.037321746},"labels":[],"label_agreement":null},{"id":"W4412032957","doi":"10.6000/1929-6029.2025.14.30","title":"Comparison of Heterogeneity Measures in Meta-Analysis","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta-analysis; Computer science; Econometrics; Mathematics; Medicine; Internal medicine","score_opus":0.9213311674461997,"score_gpt":0.7348622527882308,"score_spread":0.1864689146579689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412032957","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015552522,0.38041243,0.57432336,0.004549124,0.0042412165,0.011577979,0.004883296,0.0013089469,0.0031510815],"genre_scores_gemma":[0.480704,0.05488138,0.41440213,0.002204931,0.0014515773,0.04278494,0.0021693036,0.00067753246,0.00072422985],"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.41793674,0.49836168,0.046775516,0.012152345,0.02398541,0.0007883075],"domain_scores_gemma":[0.31921473,0.6324925,0.025340337,0.016081035,0.0063492763,0.0005221657],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.35099143,0.0034186367,0.015923964,0.014675144,0.000980712,0.0067983405,0.005384345,0.0045482637,0.0043658772],"category_scores_gemma":[0.54897434,0.0016636486,0.04717949,0.011338962,0.0029788977,0.004341772,0.003597723,0.005294587,0.00042909454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0056452956,0.00016717709,0.013654234,0.13630599,0.6781411,0.0004403749,0.00076927873,0.039383814,0.0008490371,0.016824564,0.00524495,0.10257419],"study_design_scores_gemma":[0.01007507,0.0044221855,0.020373369,0.06236912,0.6012037,0.001466151,0.00056109106,0.100439794,0.004659456,0.16285442,0.030551305,0.001024428],"about_ca_topic_score_codex":0.0017752977,"about_ca_topic_score_gemma":0.0012798053,"teacher_disagreement_score":0.6490086,"about_ca_system_score_codex":0.0055689667,"about_ca_system_score_gemma":0.0049988325,"threshold_uncertainty_score":0.80034316},"labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["metaresearch"],"domain":"methods","study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4412033058","doi":"10.6000/1929-6029.2025.14.31","title":"Overestimation of Cardiovascular Mortality Risk by Kaplan-Meier in Competing Risks Settings: A Web-Based Calculator and NHANES Analysis","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Calculator; Medicine; Demography; Internal medicine; Computer science; Sociology","score_opus":0.05825138047795735,"score_gpt":0.4482999031670607,"score_spread":0.39004852268910334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412033058","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6176552,0.0027164298,0.3498394,0.002850758,0.00023378183,0.0017918531,0.005268144,0.008339772,0.011304672],"genre_scores_gemma":[0.7800054,0.00045541298,0.21489623,0.00056600955,0.000058239235,0.0015041943,0.0014888401,0.00044654426,0.00057921937],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.971729,0.02135338,0.0014885993,0.0018500459,0.0033055607,0.00027334146],"domain_scores_gemma":[0.85060656,0.11963476,0.012711075,0.008434804,0.007768646,0.00084401143],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04873536,0.00079597486,0.0008266167,0.0030988853,0.00040607865,0.0025706526,0.0018051536,0.0011436631,0.0036094545],"category_scores_gemma":[0.18374068,0.0006272652,0.0013783342,0.0020003705,0.00061937066,0.0019240406,0.0016800114,0.0012172027,0.00095514895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002431321,0.000829452,0.5755848,0.0017050572,0.0016064955,0.00028847932,0.0029044857,0.06742153,0.0018172202,0.009026456,0.014818855,0.3215658],"study_design_scores_gemma":[0.00083004544,0.0018620809,0.3483819,0.0020516645,0.0011160435,0.0018193475,0.0013985494,0.58520895,0.009729929,0.022023682,0.025087718,0.0004901127],"about_ca_topic_score_codex":0.0039471164,"about_ca_topic_score_gemma":0.0034780651,"teacher_disagreement_score":0.9512646,"about_ca_system_score_codex":0.0013975039,"about_ca_system_score_gemma":0.001554786,"threshold_uncertainty_score":0.25774032},"labels":[],"label_agreement":null},{"id":"W4412175179","doi":"10.6000/1929-6029.2025.14.34","title":"Structural Equation Modeling of Oral Stomatitis and Its Determinants among the Sundanese Ethnic Group: Evidence from IFLS-5","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Oral Health Pathology and Treatment","field":"Dentistry","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ethnic group; Structural equation modeling; Psychology; Sociology; Mathematics; Anthropology; Statistics","score_opus":0.21854701293474935,"score_gpt":0.5405510440864871,"score_spread":0.3220040311517378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412175179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874291,0.0024685394,0.004667701,0.0023983952,0.0001376645,0.00016270387,0.0011786388,0.000052100284,0.0015051517],"genre_scores_gemma":[0.99246854,0.0016927621,0.003632473,0.00013592202,0.00004747514,0.00016967356,0.0013995726,0.000014861937,0.00043883108],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99226767,0.0060894894,0.00030234945,0.0004406187,0.0005507298,0.0003491676],"domain_scores_gemma":[0.9612183,0.029969847,0.0039480757,0.0016884686,0.0021291852,0.0010460431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013616582,0.0011075136,0.0010491352,0.0019662965,0.0010622097,0.0015252823,0.0015140611,0.00091417675,0.004193766],"category_scores_gemma":[0.030743467,0.00066950935,0.0036550746,0.0022099265,0.0008631628,0.0011630545,0.0016146547,0.001807043,0.00043146368],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031717969,0.00070502295,0.9671463,0.00032577736,0.0019211475,0.0001920771,0.0013439562,0.003381418,0.000068657224,0.0027505683,0.0016989853,0.020148883],"study_design_scores_gemma":[0.0002445819,0.0010067667,0.869075,0.0010946896,0.002604059,0.00034993934,0.004734591,0.109776184,0.00017612861,0.0069577754,0.003913486,0.00006685036],"about_ca_topic_score_codex":0.04184941,"about_ca_topic_score_gemma":0.03192414,"teacher_disagreement_score":0.04184941,"about_ca_system_score_codex":0.0016650035,"about_ca_system_score_gemma":0.0040613795,"threshold_uncertainty_score":0.0832116},"labels":[],"label_agreement":null},{"id":"W4412175184","doi":"10.6000/1929-6029.2025.14.33","title":"Association between Body Mass Index and Complete Blood Count Parameters","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adipokines, Inflammation, and Metabolic Diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Body mass index; Index (typography); Association (psychology); Statistics; Mathematics; Medicine; Internal medicine; Computer science; Psychology; World Wide Web","score_opus":0.04180263036720945,"score_gpt":0.41297282509541405,"score_spread":0.3711701947282046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412175184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977646,0.0007830621,0.00019604275,0.000056897108,0.000017720491,0.000005369376,0.00025584307,0.0000067701376,0.00091376033],"genre_scores_gemma":[0.99907196,0.00015619626,0.00012942667,0.000020856975,0.000025817437,0.00000625566,0.00021627336,0.0000023851633,0.00037073495],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996117,0.00012494288,0.000045005,0.000067389854,0.00009636631,0.000054582604],"domain_scores_gemma":[0.9979625,0.0008828383,0.000599944,0.000118207085,0.00018379613,0.00025270716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044708865,0.00023237709,0.00029713917,0.0008455697,0.00020747675,0.00039098458,0.00018240903,0.00029354126,0.0023753096],"category_scores_gemma":[0.0025194725,0.00015984531,0.00027976974,0.00069629634,0.00016873592,0.00022645922,0.00018853805,0.00059693214,0.00031275654],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022771991,0.000036388272,0.99782026,0.000007572024,0.000060244263,0.00006262686,0.000016664499,0.000025386735,0.0003034009,0.000010745249,0.00006148716,0.0013675129],"study_design_scores_gemma":[0.000003502087,0.0001148834,0.9990978,0.0000040037066,0.00003326242,0.00028522723,0.000034475244,0.00019540313,0.000075403404,0.000018237864,0.00013529952,0.0000025301638],"about_ca_topic_score_codex":0.001066319,"about_ca_topic_score_gemma":0.0013421407,"teacher_disagreement_score":0.0023753096,"about_ca_system_score_codex":0.00009050795,"about_ca_system_score_gemma":0.0001762924,"threshold_uncertainty_score":0.007946193},"labels":[],"label_agreement":null},{"id":"W4412175859","doi":"10.6000/1929-6029.2025.14.32","title":"Statistical Analysis of Logistics Management Impact on Medical Device Indicators in Indonesian Island Clinics","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Indonesian; Business; Statistical analysis; Statistics; Operations management; Economics; Mathematics","score_opus":0.05357503361674612,"score_gpt":0.47614956418256515,"score_spread":0.42257453056581906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412175859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99616563,0.00015054492,0.0008449595,0.00010303211,0.00002373581,0.000085696156,0.0014534021,0.000030202596,0.0011428252],"genre_scores_gemma":[0.9980046,0.000053412343,0.0005668306,0.000012193086,0.000009824591,0.00013791097,0.00086955645,0.000006589839,0.00033904007],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99519956,0.0020927014,0.0006616736,0.00071623677,0.0008982148,0.0004316798],"domain_scores_gemma":[0.9799448,0.0114479,0.00499787,0.0010141972,0.0016306225,0.0009646016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004455946,0.00043257108,0.0005991903,0.0023243197,0.00039756275,0.0011081869,0.00081643846,0.00032804746,0.0038865618],"category_scores_gemma":[0.012015499,0.00023672641,0.0017553589,0.003369098,0.00069648813,0.0006141502,0.0010904092,0.0011087154,0.0003089136],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015977619,0.00007774106,0.99520963,0.00005736379,0.00024448097,0.00010526584,0.00020218607,0.00046682512,0.0000903166,0.00006308434,0.00029825108,0.0030250368],"study_design_scores_gemma":[0.0000054835386,0.00020775446,0.9940858,0.000020376616,0.00009280949,0.00007129998,0.0010241097,0.0039915186,0.00011926752,0.000058170488,0.00031511273,0.000008244902],"about_ca_topic_score_codex":0.010217558,"about_ca_topic_score_gemma":0.00598221,"teacher_disagreement_score":0.010217558,"about_ca_system_score_codex":0.0009337559,"about_ca_system_score_gemma":0.0016659533,"threshold_uncertainty_score":0.02356553},"labels":[],"label_agreement":null},{"id":"W4412822068","doi":"10.6000/1929-6029.2025.14.38","title":"Childhood Tuberculosis: Epidemiology, Etiology, Pathophysiology, Pathogenesis, Risk Factors, Prevention and Diagnosis: A Narrative Review","year":2025,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Lembaga Pengelola Dana Pendidikan; Ministrstvo za visoko šolstvo, znanost in tehnologijo","keywords":"Etiology; Epidemiology; Tuberculosis; Pathophysiology; Medicine; Pathogenesis; Narrative review; Narrative; Intensive care medicine; Immunology; Pathology; Linguistics","score_opus":0.09206386428804429,"score_gpt":0.5166658053682395,"score_spread":0.42460194108019517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412822068","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033941268,0.99875236,0.000038394293,0.00032351795,0.00010198251,0.000009866095,0.000044431843,0.0000027439569,0.00038734838],"genre_scores_gemma":[0.0023886883,0.9969356,0.00010475389,0.00021838793,0.00019175571,0.000011984812,0.000056092005,9.3872217e-7,0.000091711314],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994413,0.00011503365,0.000200838,0.00008613026,0.00011281451,0.00004374287],"domain_scores_gemma":[0.9976482,0.0015591016,0.0004964828,0.000029505805,0.00020124715,0.00006545525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077649753,0.0007595157,0.0014242275,0.0042879432,0.0004174367,0.0016933767,0.00078648655,0.0012311478,0.003288483],"category_scores_gemma":[0.00336901,0.0003368505,0.0012467051,0.0041936347,0.0005233612,0.0017759282,0.0006946089,0.0010205971,0.0004627623],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025514644,0.00012239975,0.00681475,0.45401934,0.0010933958,0.002435509,0.0010244205,0.00034443918,0.0007343334,0.0042375047,0.03656724,0.49235162],"study_design_scores_gemma":[0.00007353434,0.00025513142,0.018896524,0.4112406,0.005564285,0.022326391,0.0017201938,0.0002875529,0.0004532971,0.0033439419,0.53575146,0.000087078544],"about_ca_topic_score_codex":0.0024878546,"about_ca_topic_score_gemma":0.0034623786,"teacher_disagreement_score":0.0042879432,"about_ca_system_score_codex":0.00093339157,"about_ca_system_score_gemma":0.0035830408,"threshold_uncertainty_score":0.0110010505},"labels":[],"label_agreement":null},{"id":"W4412822106","doi":"10.6000/1929-6029.2025.14.39","title":"Improving Alzheimer’s Disease Detection with Transfer Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Al-Imam Muhammad Ibn Saud Islamic University","keywords":"Artificial intelligence; CAD; Computer science; Transfer of learning; Recall; Feature (linguistics); Feature extraction; Deep learning; Pattern recognition (psychology); Diagnostic accuracy; Machine learning; Medicine; Radiology; Psychology","score_opus":0.07182219965464066,"score_gpt":0.41631279578379016,"score_spread":0.3444905961291495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412822106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51584876,0.0037830435,0.46715495,0.0010262403,0.00027265915,0.00021307259,0.0007642322,0.006143239,0.004793782],"genre_scores_gemma":[0.9415459,0.0005596028,0.053878363,0.00026007253,0.00007150084,0.00006990538,0.0008422621,0.000045741777,0.0027266974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997069,0.00006208172,0.000021564329,0.000091991606,0.000066845016,0.0000505348],"domain_scores_gemma":[0.99927443,0.00031811974,0.000063721964,0.00006993578,0.00023949383,0.000034318615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011354706,0.0008858044,0.00054682425,0.0012159338,0.00024234048,0.0007197758,0.0007705358,0.00072942406,0.0011449821],"category_scores_gemma":[0.002807872,0.00021916624,0.00054092833,0.0005257151,0.00025244823,0.0009494966,0.0007935302,0.0007746584,0.0007824607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004146275,0.00046332146,0.017938934,0.00011599297,0.00020879452,0.00022484796,0.000094744675,0.13209727,0.011858987,0.001246256,0.007390114,0.8279461],"study_design_scores_gemma":[0.000014076423,0.000150555,0.0029325467,0.000016138607,0.00003441123,0.00008653714,0.000023404758,0.9892133,0.004980111,0.0017841097,0.0007513661,0.000013414189],"about_ca_topic_score_codex":0.0070549175,"about_ca_topic_score_gemma":0.0059578116,"teacher_disagreement_score":0.0070549175,"about_ca_system_score_codex":0.00069790106,"about_ca_system_score_gemma":0.00078631827,"threshold_uncertainty_score":0.014027715},"labels":[],"label_agreement":null},{"id":"W4412822171","doi":"10.6000/1929-6029.2025.14.37","title":"The Role of Emotion Regulation in the Vicarious Trauma Risk Reduction among Psychotherapists","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Psychiatric care and mental health services","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Reduction (mathematics); Emotional trauma; Psychotherapist; Social psychology; Developmental psychology; Clinical psychology","score_opus":0.02466513522646269,"score_gpt":0.47818048405146923,"score_spread":0.45351534882500655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412822171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99147755,0.0020398316,0.00084543996,0.0018059951,0.000040847222,0.0000697499,0.000012430285,0.000009097218,0.0036989562],"genre_scores_gemma":[0.997465,0.001058294,0.0007076286,0.00019556923,0.000017429547,0.000060289705,0.0000057016478,0.0000022731883,0.00048772688],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990681,0.0006134635,0.000032092343,0.000059788865,0.000102422615,0.00012422862],"domain_scores_gemma":[0.99917114,0.00035099112,0.00019240615,0.00004675969,0.000069205336,0.0001696109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011395719,0.00013460344,0.00023071494,0.00023154552,0.00053555303,0.0008075793,0.00035304326,0.0003606789,0.0018214327],"category_scores_gemma":[0.0024634171,0.00009723067,0.00024144794,0.00019323602,0.00058836926,0.00029069913,0.0007701659,0.0006118468,0.00016784773],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001690725,0.006575602,0.21319488,0.0024521884,0.0002957644,0.00081500446,0.052310083,0.0007002012,0.027526202,0.0023164104,0.0019146454,0.69020844],"study_design_scores_gemma":[0.000188579,0.0036334442,0.9361032,0.0008242788,0.00020413987,0.00072013546,0.03710385,0.0009472274,0.0034694397,0.002053261,0.014719205,0.00003315134],"about_ca_topic_score_codex":0.0007031189,"about_ca_topic_score_gemma":0.0014934737,"teacher_disagreement_score":0.0018214327,"about_ca_system_score_codex":0.0005750525,"about_ca_system_score_gemma":0.0012755819,"threshold_uncertainty_score":0.0060932636},"labels":[],"label_agreement":null},{"id":"W4412887633","doi":"10.6000/1929-6029.2025.14.40","title":"Statistical Evaluation of Comorbidities and Environmental Factors in COVID-19 Outcomes: Risk Measures and Predictive Analysis Using Odds and Hazard Ratios","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Odds ratio; Odds; Confidence interval; Hazard ratio; Intensive care medicine; Environmental health; Heart failure; Pandemic; Psychological intervention; Coronavirus disease 2019 (COVID-19); Hazard; Disease; Emergency medicine; Internal medicine; Logistic regression; Infectious disease (medical specialty); Psychiatry","score_opus":0.1798168107303578,"score_gpt":0.568486840620649,"score_spread":0.3886700298902912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412887633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6593048,0.022519046,0.29194206,0.001957489,0.00096913223,0.002166593,0.014722279,0.0010203104,0.005398302],"genre_scores_gemma":[0.95770466,0.0009954027,0.03639631,0.0001478816,0.00020908017,0.0016653507,0.0021283317,0.00008812477,0.00066490355],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.93943936,0.041706275,0.0060734027,0.0048843157,0.0069836006,0.000913107],"domain_scores_gemma":[0.800531,0.17120844,0.015554988,0.00941675,0.0024490836,0.0008396659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06916269,0.0010052334,0.0016731428,0.0075996025,0.0004379646,0.001624438,0.0017525643,0.0009365469,0.0036810334],"category_scores_gemma":[0.12555638,0.00032790052,0.004596976,0.006797577,0.0015450859,0.0021677094,0.0017061139,0.0026823247,0.00033236938],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020348488,0.00015810781,0.93499345,0.001514601,0.009806625,0.00027996307,0.00045256116,0.004387398,0.0003970988,0.0038762211,0.0018576225,0.040241595],"study_design_scores_gemma":[0.00028256766,0.005788396,0.79622424,0.0011669359,0.009474342,0.0026859343,0.0040916316,0.1310713,0.0036522625,0.024311015,0.020988287,0.00026310913],"about_ca_topic_score_codex":0.0019972334,"about_ca_topic_score_gemma":0.0009969501,"teacher_disagreement_score":0.06916269,"about_ca_system_score_codex":0.0007467025,"about_ca_system_score_gemma":0.0011995668,"threshold_uncertainty_score":0.36577165},"labels":[],"label_agreement":null},{"id":"W4413077203","doi":"10.6000/1929-6029.2025.14.36","title":"A Refined Population Mean Estimator Using Median and Skewness: Applications to Breast Cancer and Brain Tumor Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Skewness; Statistics; Population; Mathematics; Sampling (signal processing); Simple random sample; Computer science; Medicine","score_opus":0.24914940791752882,"score_gpt":0.5780527026165511,"score_spread":0.32890329469902224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413077203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022591641,0.00048569302,0.9761763,0.00015269782,0.000026245425,0.00004072654,0.0000647902,0.00017024716,0.0002916779],"genre_scores_gemma":[0.36913288,0.00086261384,0.62873524,0.00010361009,0.00011473055,0.00017263088,0.00029308093,0.000052556436,0.0005327278],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955842,0.0030894463,0.00020276701,0.00044435426,0.0005810356,0.00009820613],"domain_scores_gemma":[0.98226273,0.012578651,0.0012879652,0.0015547847,0.0021034651,0.00021240456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011760811,0.0004305801,0.00089439494,0.0019025999,0.00030380845,0.00066225196,0.000814007,0.0006120828,0.0005864232],"category_scores_gemma":[0.039724275,0.00024865696,0.00084600167,0.001861613,0.0005867861,0.0013912646,0.0012140162,0.0009813439,0.00016906623],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040814164,0.00016300453,0.03992652,0.0004402185,0.000379139,0.00036358004,0.0005603979,0.31310794,0.010388355,0.085864015,0.003034586,0.54536414],"study_design_scores_gemma":[0.0000553433,0.00023950674,0.0074765454,0.000055878358,0.00006941479,0.00036655835,0.00012190113,0.9521072,0.00357374,0.032334335,0.003537451,0.000062148414],"about_ca_topic_score_codex":0.001494932,"about_ca_topic_score_gemma":0.0012387242,"teacher_disagreement_score":0.011760811,"about_ca_system_score_codex":0.00042054217,"about_ca_system_score_gemma":0.0010329596,"threshold_uncertainty_score":0.062197864},"labels":[],"label_agreement":null},{"id":"W4413082052","doi":"10.6000/1929-6029.2025.14.35","title":"A Retrospective Study on Postoperative Complications in Gynecological Surgeries: Identification of High-Risk Factors and Best Practices","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Uterine Myomas and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Retrospective cohort study; Surgery; Malignancy; Complication; Incidence (geometry); Laparoscopy; Odds ratio; Logistic regression; Internal medicine","score_opus":0.10658483216119917,"score_gpt":0.50499834193623,"score_spread":0.39841350977503087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413082052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976434,0.0005747307,0.00010912314,0.00004945375,0.000009082514,0.00003431391,0.001163205,0.0000033365193,0.00041335926],"genre_scores_gemma":[0.99840087,0.00035378864,0.00013319521,0.0000558428,0.0000103622715,0.0000458821,0.0009060245,0.0000028457116,0.00009111198],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998896,0.00018242886,0.0002932367,0.00023858488,0.00022885088,0.00016081322],"domain_scores_gemma":[0.99656254,0.00047815996,0.00208107,0.00026215537,0.00033712626,0.0002790305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009526078,0.00021618056,0.00033783205,0.0011536721,0.0005178524,0.00056787266,0.0003834875,0.00034425274,0.0015635123],"category_scores_gemma":[0.0026988767,0.00040800462,0.00055917026,0.0022015895,0.000250973,0.00060548936,0.0005439034,0.00051081146,0.0002554554],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002338364,0.000014171321,0.99931383,0.000016347327,0.00001538781,0.00006142787,0.000043377146,0.0000064534347,0.000032913915,0.000007343064,0.000090404435,0.00037491618],"study_design_scores_gemma":[0.000004546943,0.00006764076,0.9983663,0.000023612738,0.000024314935,0.00063333556,0.00045887692,0.00007381153,0.000037050824,0.000015178535,0.00029048114,0.0000047509025],"about_ca_topic_score_codex":0.0051852367,"about_ca_topic_score_gemma":0.0064984174,"teacher_disagreement_score":0.0051852367,"about_ca_system_score_codex":0.0004981616,"about_ca_system_score_gemma":0.00092906575,"threshold_uncertainty_score":0.010310113},"labels":[],"label_agreement":null},{"id":"W4413130041","doi":"10.6000/1929-6029.2025.14.42","title":"Safety and Efficacy of Flow Diversion for Intracranial Aneurysms in Small Parent Vessels: A Retrospective Cohort Study","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Occlusion; Digital subtraction angiography; Retrospective cohort study; Aneurysm; Cohort; Surgery; Thrombosis; Radiology; Stent; Angiography; Flow diverter; Internal medicine","score_opus":0.03967107951306127,"score_gpt":0.4039842313551822,"score_spread":0.36431315184212093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413130041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994754,0.00017660933,0.00006875172,0.000007680065,0.0000030059684,0.000010127019,0.00013703105,0.0000015120263,0.000119988974],"genre_scores_gemma":[0.99942976,0.00015674905,0.000065820925,0.000013173769,0.000010740505,0.000016838181,0.00025578125,0.0000023103282,0.000048790596],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992224,0.00014558605,0.0001260756,0.00022952638,0.00015269702,0.00012379882],"domain_scores_gemma":[0.99665654,0.0006569701,0.0017811813,0.00038814908,0.00023890809,0.00027821443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001235755,0.00027181208,0.0004322817,0.0008862367,0.00036029212,0.0005628016,0.000293805,0.000406194,0.0009956382],"category_scores_gemma":[0.0031457476,0.00030924717,0.00081628055,0.00084861915,0.00032241314,0.0006113549,0.00029808405,0.00042909823,0.00022263234],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001489645,0.000019920719,0.998831,0.0000060914654,0.000047722882,0.0001276764,0.000039626193,0.000015901416,0.00009108953,0.000005751454,0.000029034996,0.00063721294],"study_design_scores_gemma":[0.000016338818,0.0004743866,0.99792624,0.000008304514,0.000079748774,0.0009843856,0.00012188519,0.00013038977,0.00007214162,0.000010580861,0.0001683163,0.0000072518346],"about_ca_topic_score_codex":0.0013560937,"about_ca_topic_score_gemma":0.0011728604,"teacher_disagreement_score":0.0013560937,"about_ca_system_score_codex":0.00028835837,"about_ca_system_score_gemma":0.00036553666,"threshold_uncertainty_score":0.0065353513},"labels":[],"label_agreement":null},{"id":"W4413224086","doi":"10.6000/1929-6029.2025.14.43","title":"Exploring the Relationship between Perceived Multidimensional Social Support and Well-Being among Community Seniors Participating in Group Exercise Programs","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Physical Activity and Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social support; Psychology; Scale (ratio); Descriptive statistics; Context (archaeology); Social psychology; Well-being; Gerontology; Clinical psychology; Medicine","score_opus":0.3001978759249538,"score_gpt":0.5020120160426975,"score_spread":0.20181414011774373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413224086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997038,0.00007107888,0.000018616327,0.00003104972,0.0000015731699,0.000004067597,0.00001432488,3.2309518e-7,0.00015514738],"genre_scores_gemma":[0.9997851,0.00006532801,0.00004571468,0.000015894717,0.0000028185104,0.0000057989364,0.000021819167,1.7198221e-7,0.000057180914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99957496,0.00013653071,0.000052383133,0.000043632805,0.00009701691,0.00009545425],"domain_scores_gemma":[0.99861085,0.00028470732,0.00045688235,0.000037439688,0.00016478788,0.00044528005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093641615,0.00020585995,0.0003075351,0.000833378,0.00048108946,0.00067329354,0.00022468915,0.00044563497,0.0010209632],"category_scores_gemma":[0.0026298272,0.00014272932,0.00031125732,0.0005379474,0.00025036666,0.0004604369,0.00079677626,0.00046634526,0.00009029012],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000525868,0.00019777221,0.9882641,0.00005117461,0.000066577086,0.00008456593,0.0039692842,0.000016757018,0.00035255734,0.000022251157,0.000042555235,0.0068799364],"study_design_scores_gemma":[0.0000025148333,0.00023614628,0.99303955,0.00002163695,0.00001684255,0.000066149165,0.00637952,0.000058630685,0.000027790422,0.000022726506,0.00012473833,0.000003835947],"about_ca_topic_score_codex":0.0023779483,"about_ca_topic_score_gemma":0.007615355,"teacher_disagreement_score":0.0023779483,"about_ca_system_score_codex":0.00016611702,"about_ca_system_score_gemma":0.00038236153,"threshold_uncertainty_score":0.0049523115},"labels":[],"label_agreement":null},{"id":"W4413245049","doi":"10.6000/1929-6029.2025.14.41","title":"The Effect of Website “Remaja Cegah DBD” on the Prevention Behaviour of Dengue Haemorrhagic Fever Among Students at Junior High School in Makassar City, Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dengue and Mosquito Control Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Health of the Republic of Indonesia; Universitas Hasanuddin","keywords":"Test (biology); Intervention (counseling); Medicine; Population; Dengue hemorrhagic fever; Significant difference; Health education; Bivariate analysis; Family medicine; Dengue fever; Psychology; Environmental health; Demography; Public health; Dengue virus; Nursing; Mathematics; Internal medicine; Immunology","score_opus":0.03494642382367961,"score_gpt":0.4849078155565445,"score_spread":0.44996139173286487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413245049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993963,0.000095903415,0.000009013576,0.000071752,0.000010721969,0.000037856287,0.000027718186,0.0000036060405,0.00034713253],"genre_scores_gemma":[0.99889,0.00015503794,0.00019974491,0.000064511194,0.000009652159,0.0000876088,0.000041733903,9.277942e-7,0.0005508242],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99955624,0.00016954138,0.00004291876,0.000046757104,0.00007585433,0.00010867196],"domain_scores_gemma":[0.99848574,0.00030311348,0.00027128815,0.00004748669,0.000102596205,0.00078986224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007443275,0.00025749626,0.0004793308,0.00028314572,0.00049307925,0.00052937755,0.0003865829,0.00042568895,0.002607531],"category_scores_gemma":[0.0018158582,0.00020395503,0.0006224532,0.00023431156,0.00023064631,0.00028390123,0.0004402473,0.00080559525,0.0002717074],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009914462,0.11948694,0.63181263,0.002335654,0.00044385105,0.0007716581,0.008539114,0.00041416727,0.0067506707,0.0002244409,0.0022857934,0.21702057],"study_design_scores_gemma":[0.00043114377,0.020568836,0.97302794,0.00016185154,0.00022146547,0.00007165002,0.0029397372,0.0004148385,0.0009857682,0.000031087704,0.0011243947,0.000021179503],"about_ca_topic_score_codex":0.004313508,"about_ca_topic_score_gemma":0.006583223,"teacher_disagreement_score":0.004313508,"about_ca_system_score_codex":0.0003976867,"about_ca_system_score_gemma":0.00084336166,"threshold_uncertainty_score":0.008723021},"labels":[],"label_agreement":null},{"id":"W4413337221","doi":"10.6000/1929-6029.2025.14.44","title":"Guillain-Barré Syndrome in Egypt: Diagnostic Challenges and Subtype Evolution Over Time","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Peripheral Neuropathies and Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Mansoura University","keywords":"Guillain-Barre syndrome; Medicine; Pediatrics","score_opus":0.02950823550893343,"score_gpt":0.3972725081695165,"score_spread":0.36776427266058304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413337221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990965,0.0004159272,0.00003410606,0.00010769017,0.0000035237113,0.000005630128,0.00009413125,0.0000015036406,0.00024091692],"genre_scores_gemma":[0.99901927,0.00047427046,0.00009065965,0.0000523637,0.000012427735,0.000006246859,0.00027941918,0.0000012746259,0.00006418966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99950683,0.000082534076,0.000073533585,0.00009794933,0.000096098694,0.00014306857],"domain_scores_gemma":[0.9987404,0.00016933127,0.00060448045,0.00006249622,0.00030153012,0.00012180096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009333965,0.00021795655,0.00029020954,0.001225285,0.00036088013,0.0007436557,0.0005943213,0.00041426756,0.00059021293],"category_scores_gemma":[0.0026706757,0.0001655514,0.00020865159,0.001721695,0.00036286275,0.0007686163,0.00053771824,0.00034944792,0.00014771249],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050672395,0.000015695823,0.99467456,0.000013496779,0.0000088636,0.00077040587,0.0003363339,0.000040881194,0.00021836463,0.00002581883,0.000094324874,0.0037505876],"study_design_scores_gemma":[0.000005051388,0.000043344433,0.9940148,0.000041397074,0.000012258935,0.0032748645,0.0018050857,0.00023443068,0.00009434514,0.00006558529,0.0004034534,0.0000055122873],"about_ca_topic_score_codex":0.0106419735,"about_ca_topic_score_gemma":0.009366521,"teacher_disagreement_score":0.0106419735,"about_ca_system_score_codex":0.0007407417,"about_ca_system_score_gemma":0.00060239114,"threshold_uncertainty_score":0.021160066},"labels":[],"label_agreement":null},{"id":"W4413337250","doi":"10.6000/1929-6029.2025.14.45","title":"Bayesian Estimation of Spatiotemporal Immune-Viral Dynamics in COVID-19 Using Partial Differential Equations","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Dynamics (music); Bayesian probability; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Partial differential equation; Estimation; Immune system; Statistical physics; Mathematics; Applied mathematics; Virology; Physics; Statistics; Mathematical analysis; Medicine; Immunology; Economics","score_opus":0.2907134393098752,"score_gpt":0.5828402253936437,"score_spread":0.29212678608376846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413337250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034573767,0.00019068962,0.9644946,0.00014455528,0.0000072950743,0.000031853062,0.00012123594,0.00010369573,0.0003323638],"genre_scores_gemma":[0.7222332,0.00085305184,0.27327085,0.00016653672,0.00006723534,0.00030081536,0.0009555135,0.00008554355,0.0020673203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992248,0.00040246063,0.00003707045,0.00014745037,0.00013323559,0.000055014432],"domain_scores_gemma":[0.99682367,0.0024944425,0.00032472628,0.00009529407,0.00019786009,0.00006401867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030753068,0.0005203778,0.0010229616,0.0009954822,0.00031761324,0.0008834048,0.00104499,0.0009061611,0.00065792387],"category_scores_gemma":[0.009324973,0.0008028452,0.0009749454,0.0006038348,0.000723244,0.00080722774,0.0010621191,0.0012266875,0.00017398038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005001928,0.000035454676,0.0053396407,0.00005769826,0.000067141606,0.00006986672,0.000056650035,0.9571459,0.0019707133,0.015319087,0.00023704997,0.019650757],"study_design_scores_gemma":[0.0000034396178,0.000008448102,0.00048725688,0.0000047710932,0.0000044790495,0.000015563293,0.000004126305,0.99537635,0.0001409944,0.0037876721,0.0001607264,0.00000617506],"about_ca_topic_score_codex":0.0121663185,"about_ca_topic_score_gemma":0.009005995,"teacher_disagreement_score":0.0121663185,"about_ca_system_score_codex":0.00078288687,"about_ca_system_score_gemma":0.0015307224,"threshold_uncertainty_score":0.024191022},"labels":[],"label_agreement":null},{"id":"W4413811328","doi":"10.6000/1929-6029.2025.14.46","title":"Alpha Diversity Analysis of Microbiota Dysbiosis in Normal and Colorectal Cancer of Mice Feces","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dysbiosis; Feces; Colorectal cancer; Diversity (politics); Biology; Fecal bacteriotherapy; Cancer; Microbiology; Gut flora; Immunology; Genetics; Sociology; Antibiotics","score_opus":0.020945026180126957,"score_gpt":0.4254921930109545,"score_spread":0.40454716683082753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413811328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968612,0.00036564752,0.0012843309,0.00002189788,0.0000059466533,0.000020659902,0.00086230703,0.000031247357,0.00054675934],"genre_scores_gemma":[0.99466383,0.0003343891,0.0028980253,0.000059243146,0.0000062744703,0.00007155594,0.0010072843,0.000019532217,0.0009398458],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99972636,0.000039371793,0.000023530405,0.000067177614,0.00008203408,0.00006161683],"domain_scores_gemma":[0.9995202,0.00004082041,0.00016432421,0.000026643382,0.000088114924,0.00015986426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030989439,0.00027369714,0.00026798467,0.0014720227,0.00023190102,0.00027487602,0.00012167767,0.00021976723,0.0008213597],"category_scores_gemma":[0.00025502392,0.0001628873,0.00021209901,0.00062111515,0.00024237561,0.00017300487,0.0002725792,0.00042726452,0.00016564487],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012115521,0.00015015565,0.050330494,0.00005197244,0.00005198067,0.00005068752,0.00014719705,0.00011201632,0.94262475,0.00005872564,0.000072193754,0.005138226],"study_design_scores_gemma":[0.00003107806,0.0021309587,0.7309844,0.000022463724,0.00012514585,0.00048198513,0.00035761364,0.0018929917,0.26204723,0.00017991877,0.0017202098,0.000026001382],"about_ca_topic_score_codex":0.00063215435,"about_ca_topic_score_gemma":0.00079287787,"teacher_disagreement_score":0.0014720227,"about_ca_system_score_codex":0.00016132137,"about_ca_system_score_gemma":0.00015749226,"threshold_uncertainty_score":0.0027477145},"labels":[],"label_agreement":null},{"id":"W4413876610","doi":"10.6000/1929-6029.2025.14.48","title":"Predicting Sex from Hand Dimensions using Statistical Models: A Cross-Sectional Study of Medical Students","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Medical Education and Admissions","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cross-sectional study; Statistics; Psychology; Mathematics","score_opus":0.19497804670301658,"score_gpt":0.5976328432091818,"score_spread":0.4026547965061652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413876610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9994703,0.000048330476,0.00028045752,0.000035341665,0.0000028793643,0.000010839025,0.00006190128,0.0000028302588,0.000087173416],"genre_scores_gemma":[0.999198,0.00006826998,0.00047151683,0.000017843799,0.0000050264407,0.000013881162,0.00012549853,0.0000023589275,0.00009767097],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99807453,0.0010649964,0.00015086708,0.00022572039,0.0003364109,0.0001474864],"domain_scores_gemma":[0.9913748,0.005709087,0.0013543358,0.000600899,0.0005276116,0.00043331223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005103727,0.00038478512,0.0004345526,0.0010379134,0.00040367717,0.00089741027,0.0005161557,0.0006609758,0.0014048108],"category_scores_gemma":[0.0143533535,0.0005753395,0.0008476114,0.0008686102,0.00034536858,0.00089997746,0.0007766111,0.0010326918,0.0005186966],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034820576,0.000101117745,0.9983359,0.0000043667765,0.000033726963,0.000033057215,0.00016052635,0.00010292761,0.000041891217,0.000010410985,0.00003292287,0.0011082458],"study_design_scores_gemma":[0.000014600884,0.001173806,0.9845881,0.000028481558,0.000090360765,0.00044709735,0.0017508233,0.011423478,0.00013289129,0.00009197845,0.00024386008,0.000014528656],"about_ca_topic_score_codex":0.0022712057,"about_ca_topic_score_gemma":0.0024324814,"teacher_disagreement_score":0.005103727,"about_ca_system_score_codex":0.00017235696,"about_ca_system_score_gemma":0.00041712468,"threshold_uncertainty_score":0.026991367},"labels":[],"label_agreement":null},{"id":"W4413876633","doi":"10.6000/1929-6029.2025.14.49","title":"Sex Estimation in Terms of Inclination and Alsberg in Proximal Femur by using Machine Learning Algorithms","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Forensic Anthropology and Bioarchaeology Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimation; Computer science; Femur; Artificial intelligence; Algorithm; Computer vision; Geology; Economics; Management","score_opus":0.05368655592760875,"score_gpt":0.4385868392732011,"score_spread":0.38490028334559234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413876633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71672195,0.0037058145,0.27524087,0.00028060138,0.000109650544,0.000107919266,0.00088580634,0.0008441951,0.0021033136],"genre_scores_gemma":[0.94549143,0.0005984003,0.052505046,0.000034415305,0.000048546506,0.00006044153,0.0007392976,0.00004307492,0.0004792524],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851936,0.0006217562,0.00019544091,0.00028767515,0.00029610156,0.00007958171],"domain_scores_gemma":[0.9948337,0.0034634634,0.00074113044,0.00034232368,0.0005479308,0.00007147253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037889134,0.0006885002,0.0007143564,0.002547588,0.00020563591,0.0011947147,0.00036159487,0.0005144297,0.0007899224],"category_scores_gemma":[0.013512348,0.00019892254,0.0006462304,0.001254791,0.0003016171,0.0006703799,0.0005061301,0.00042454232,0.00043673514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078087975,0.000110522444,0.5828381,0.00018295903,0.00035453466,0.0002546975,0.00023430515,0.06115841,0.004820749,0.000959147,0.0012223143,0.34708354],"study_design_scores_gemma":[0.00004067659,0.0004533219,0.24636693,0.00012888944,0.0002652361,0.0009803489,0.00032851342,0.7375091,0.007437397,0.0040133,0.0023995712,0.000076769276],"about_ca_topic_score_codex":0.0016122198,"about_ca_topic_score_gemma":0.001255109,"teacher_disagreement_score":0.0037889134,"about_ca_system_score_codex":0.00020064761,"about_ca_system_score_gemma":0.0004994522,"threshold_uncertainty_score":0.02003795},"labels":[],"label_agreement":null},{"id":"W4413876639","doi":"10.6000/1929-6029.2025.14.50","title":"Determination of Alzheimer's Disease Stages by Artificial Learning Algorithms","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health","keywords":"Artificial intelligence; Computer science; Disease; Algorithm; Machine learning; Medicine; Internal medicine","score_opus":0.048574687753175645,"score_gpt":0.4788623794538804,"score_spread":0.43028769170070474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413876639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34222075,0.0031196205,0.64662987,0.00092583394,0.00014328278,0.00027125713,0.00091796386,0.0014713224,0.004300173],"genre_scores_gemma":[0.85405016,0.0006628685,0.14309934,0.00015571371,0.00007858159,0.00016125094,0.0007483402,0.000039758546,0.0010039314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990044,0.0004975353,0.00008277835,0.00018972348,0.00016461247,0.00006101702],"domain_scores_gemma":[0.9954939,0.0033105933,0.00046170354,0.00018444179,0.00047285046,0.000076388606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033394042,0.0008127464,0.000700993,0.0023523173,0.00027307236,0.0011823035,0.0005955069,0.00078755186,0.0010258367],"category_scores_gemma":[0.009292402,0.00022763641,0.00079645554,0.001115673,0.0003707266,0.0008213327,0.00043995932,0.0007966172,0.00042985354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005414634,0.00041436745,0.1329424,0.00030623144,0.00051087467,0.00013666794,0.00015551863,0.4972875,0.0016472875,0.00383577,0.003115366,0.35910657],"study_design_scores_gemma":[0.0000152514385,0.000073913056,0.00652347,0.000059568807,0.00003831554,0.00006838592,0.00002506746,0.9870657,0.0005952738,0.0050024455,0.0005187848,0.000013838207],"about_ca_topic_score_codex":0.0031218494,"about_ca_topic_score_gemma":0.0022064731,"teacher_disagreement_score":0.0033394042,"about_ca_system_score_codex":0.0006463924,"about_ca_system_score_gemma":0.00083930633,"threshold_uncertainty_score":0.017660677},"labels":[],"label_agreement":null},{"id":"W4413876715","doi":"10.6000/1929-6029.2025.14.47","title":"Bayesian Estimation for Factor Analysis Model in Geriatric Medicine","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimation; Bayesian probability; Factor (programming language); Computer science; Bayes estimator; Statistics; Artificial intelligence; Econometrics; Mathematics; Engineering","score_opus":0.19521356409554833,"score_gpt":0.590364230087773,"score_spread":0.3951506659922246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413876715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002601585,0.00048335557,0.9959039,0.00022700659,0.000030488325,0.00006992631,0.00007854451,0.00010424735,0.00050085183],"genre_scores_gemma":[0.15894301,0.0027021938,0.83392113,0.0002615246,0.00021946474,0.0012701142,0.0006644655,0.0001368415,0.0018811804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98977214,0.007657196,0.00036918843,0.0011041957,0.0008955914,0.00020173908],"domain_scores_gemma":[0.9837217,0.013334055,0.0010326479,0.0006545874,0.001110945,0.00014613106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015311142,0.0016122458,0.0022577506,0.00264365,0.0009485987,0.002046804,0.0020622998,0.0018273775,0.0041100644],"category_scores_gemma":[0.05693446,0.001197348,0.001976374,0.0033678191,0.001913281,0.002816739,0.0019873532,0.0034522698,0.0012854738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018130481,0.00013238448,0.0076939943,0.0007947671,0.0005855405,0.00023937601,0.0008706575,0.38401148,0.0009500975,0.34848437,0.00591863,0.25013745],"study_design_scores_gemma":[0.000049874325,0.000069018366,0.001605179,0.00017949441,0.000074205585,0.00009585633,0.00010281382,0.66868055,0.0002927603,0.32381287,0.0049780975,0.000059345504],"about_ca_topic_score_codex":0.013779143,"about_ca_topic_score_gemma":0.010840449,"teacher_disagreement_score":0.015311142,"about_ca_system_score_codex":0.002050771,"about_ca_system_score_gemma":0.003828588,"threshold_uncertainty_score":0.08097398},"labels":[],"label_agreement":null},{"id":"W4414252446","doi":"10.6000/1929-6029.2025.14.52","title":"Heart Disease Prediction using an Ensemble Learning Method: A Study at King Abdullah Hospital in Bisha, Saudi Arabia","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"False positive paradox; Logistic regression; Support vector machine; Ensemble learning; Disease; Psychological intervention; Heart disease; Health care","score_opus":0.28159851425152704,"score_gpt":0.6403699487134586,"score_spread":0.3587714344619316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414252446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991412,0.0001605687,0.00013115561,0.00020924318,0.000007254393,0.000010648776,0.00011262854,0.0000035613493,0.00022369123],"genre_scores_gemma":[0.99903166,0.00020732536,0.00015352711,0.00010476811,0.000019286415,0.000007989877,0.00025863375,0.0000021526703,0.0002145749],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995316,0.00013609153,0.00004413895,0.0000877755,0.00010604845,0.00009441225],"domain_scores_gemma":[0.997603,0.00059881917,0.00036268577,0.00015355748,0.000888036,0.0003939095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010514115,0.00035200076,0.00034360387,0.0006043919,0.0005071189,0.00053154666,0.00041591146,0.00051623216,0.00078090123],"category_scores_gemma":[0.0029956033,0.00018431667,0.00043846213,0.00065640727,0.00019449538,0.00048331736,0.00047708652,0.00058923516,0.00029098973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018452863,0.0002153077,0.98473215,0.000036910755,0.00007996909,0.00061083335,0.001149757,0.00048141074,0.00028097487,0.000047873473,0.0009810627,0.011199308],"study_design_scores_gemma":[0.000028870734,0.00041816532,0.9849814,0.000044093533,0.000102416656,0.00078245776,0.003808185,0.008324217,0.00030209776,0.00006149681,0.0011187217,0.000027882048],"about_ca_topic_score_codex":0.037066244,"about_ca_topic_score_gemma":0.032332707,"teacher_disagreement_score":0.037066244,"about_ca_system_score_codex":0.0008151862,"about_ca_system_score_gemma":0.0006882676,"threshold_uncertainty_score":0.073701024},"labels":[],"label_agreement":null},{"id":"W4414262541","doi":"10.6000/1929-6029.2025.14.51","title":"Gender Prediction from Angular and Linear Parameters in Cranium Lateral View by using Machine Learning Algorithms: A Computed Tomography Study","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear discriminant analysis; Random forest; Logistic regression; Linear regression; Computed tomography; Pattern recognition (psychology); Naive Bayes classifier; Bayes' theorem","score_opus":0.04552103353803056,"score_gpt":0.41260266352434455,"score_spread":0.367081629986314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414262541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99447733,0.0005984581,0.0041536964,0.000047051097,0.000013160108,0.00001821093,0.000099449986,0.000012193207,0.00058051996],"genre_scores_gemma":[0.9966826,0.00022787496,0.0027651966,0.000013721823,0.00001785999,0.000009961241,0.000104220075,0.000004643199,0.0001739268],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99955815,0.00018587476,0.000046292294,0.00006424083,0.00011580554,0.000029714844],"domain_scores_gemma":[0.9983735,0.0008648445,0.00029034974,0.00008673315,0.00031417742,0.000070277565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012034797,0.0004514522,0.00025963457,0.0009398852,0.00012638826,0.000351664,0.00014782339,0.00035215885,0.00070191984],"category_scores_gemma":[0.0046657627,0.00012550734,0.00034316766,0.00036586876,0.00020217203,0.0003145666,0.0001704341,0.00019658673,0.00030446783],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004510808,0.000072706076,0.95456487,0.000042809617,0.00006930256,0.00028868794,0.00019473545,0.0019828787,0.004182364,0.00008357076,0.00013657685,0.037930496],"study_design_scores_gemma":[0.000017119128,0.0008449643,0.9584641,0.0000577634,0.000117370524,0.00230057,0.00057314587,0.03240307,0.0041025337,0.00025301336,0.0008427652,0.0000236413],"about_ca_topic_score_codex":0.001267557,"about_ca_topic_score_gemma":0.0014773344,"teacher_disagreement_score":0.001267557,"about_ca_system_score_codex":0.00010979948,"about_ca_system_score_gemma":0.00021086255,"threshold_uncertainty_score":0.0063646436},"labels":[],"label_agreement":null},{"id":"W4414555877","doi":"10.6000/1929-6029.2025.14.55","title":"Evaluation of A Novel Risk Factor Screening Tool for Gestational Diabetes Mellitus: A Machine Learning Based Predictive Method","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gestational diabetes; Logistic regression; Random forest; Predictive modelling; Risk factor; Pregnancy; Predictive value; Diabetes mellitus","score_opus":0.10755057567639305,"score_gpt":0.4981720587890995,"score_spread":0.39062148311270645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414555877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35426942,0.00304482,0.62989205,0.001601088,0.00034264615,0.00063941756,0.0026336752,0.0042143925,0.0033625807],"genre_scores_gemma":[0.74103796,0.0007600503,0.25461328,0.00023360403,0.00011311208,0.00032673648,0.0016702159,0.000059228827,0.0011859736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989133,0.00036006115,0.00010120247,0.00020701691,0.000326055,0.00009236312],"domain_scores_gemma":[0.9961035,0.0026346003,0.0003001882,0.00013432892,0.00071792863,0.00010942121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026931008,0.0010812666,0.0010199993,0.0032009904,0.0003099813,0.0011969393,0.0007978658,0.001058382,0.0016282775],"category_scores_gemma":[0.009207416,0.00022940127,0.000973337,0.0013745904,0.00019719274,0.000722677,0.00052195485,0.00086479075,0.0006004497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011559568,0.00095053995,0.20499563,0.0004174446,0.0005123924,0.00080693775,0.0001390397,0.11957697,0.00941228,0.0016178174,0.0056153815,0.6547996],"study_design_scores_gemma":[0.00006312336,0.00028502347,0.022329276,0.00009469101,0.00020388486,0.00045258077,0.00005932222,0.9690634,0.005029769,0.0010985329,0.001274955,0.000045528013],"about_ca_topic_score_codex":0.0037513594,"about_ca_topic_score_gemma":0.0022840146,"teacher_disagreement_score":0.0037513594,"about_ca_system_score_codex":0.00056550524,"about_ca_system_score_gemma":0.0011876684,"threshold_uncertainty_score":0.014242649},"labels":[],"label_agreement":null},{"id":"W4414556207","doi":"10.6000/1929-6029.2025.14.54","title":"Transforming Breast Cancer Prediction: Advanced Machine Learning Models for Accurate Prediction and Personalized Care","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpretability; Random forest; Support vector machine; Breast cancer; Gradient boosting; Artificial neural network; Logistic regression; Preprocessor; Lasso (programming language)","score_opus":0.04821177731210035,"score_gpt":0.4215333976759396,"score_spread":0.37332162036383926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414556207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057439797,0.009745577,0.91610104,0.006917177,0.0005255958,0.0001660378,0.0020286448,0.0031760107,0.0039001976],"genre_scores_gemma":[0.75464123,0.007435028,0.23038882,0.0008645289,0.0009575405,0.00030164694,0.0029093896,0.00020667004,0.0022950706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988783,0.00058637164,0.000048547903,0.00019107215,0.00024822686,0.00004736734],"domain_scores_gemma":[0.9961404,0.0027088195,0.00030678947,0.0002861529,0.0004770824,0.00008059254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031776149,0.0009824801,0.0010011985,0.0010274128,0.00025162328,0.0012851886,0.00089237577,0.00090855424,0.001857357],"category_scores_gemma":[0.010827546,0.00028322698,0.0007894205,0.0013461636,0.00033728415,0.0011477864,0.0010928451,0.0022966494,0.0008943131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025710402,0.00023619711,0.016752819,0.0003433588,0.0002720461,0.00016649302,0.00012776411,0.5412604,0.001560829,0.008336079,0.015431569,0.41525528],"study_design_scores_gemma":[0.0000144415135,0.000052604923,0.001143282,0.00006706868,0.00004242784,0.00005535154,0.00001782677,0.9780724,0.0005872753,0.016637316,0.0032928067,0.000017247246],"about_ca_topic_score_codex":0.0039755823,"about_ca_topic_score_gemma":0.0027129527,"teacher_disagreement_score":0.0039755823,"about_ca_system_score_codex":0.00063449936,"about_ca_system_score_gemma":0.00089747575,"threshold_uncertainty_score":0.016805053},"labels":[],"label_agreement":null},{"id":"W4414556808","doi":"10.6000/1929-6029.2025.14.53","title":"Machine Learning-Based Maternal Health Risk Assessment: A Comparative Analysis of Classification Algorithms for Predicting Risk Levels During Pregnancy","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Random forest; Logistic regression; Support vector machine; Pregnancy; Risk assessment; Identification (biology); Statistical classification","score_opus":0.1711624789203163,"score_gpt":0.5625953389311843,"score_spread":0.39143286001086797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414556808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8494539,0.02752029,0.11108121,0.0021541903,0.0004601676,0.00043254765,0.002724977,0.0012201323,0.004952595],"genre_scores_gemma":[0.9401234,0.003475326,0.053251833,0.00019798742,0.00017191014,0.00018235734,0.0021566343,0.00007761777,0.00036301746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936265,0.0036869827,0.00063389423,0.0007630577,0.001087255,0.00020233839],"domain_scores_gemma":[0.9527777,0.041612636,0.0017001553,0.0010738248,0.002522169,0.0003135935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015456731,0.0011425796,0.0012914255,0.0041634953,0.00033560087,0.0014160722,0.0009357558,0.0011839026,0.0006436393],"category_scores_gemma":[0.031113729,0.0002199232,0.0013409811,0.0019057265,0.00034105388,0.0013234421,0.0006981875,0.0008938407,0.00027419496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041175834,0.000882844,0.30099523,0.00096712925,0.0027748765,0.00012252288,0.00024461193,0.16203845,0.0010634045,0.001654773,0.004393953,0.5207447],"study_design_scores_gemma":[0.00019161394,0.0020852697,0.09401335,0.00041463794,0.0007627599,0.0004035388,0.00027356384,0.89482284,0.0018540925,0.0029268018,0.0021507186,0.000100754136],"about_ca_topic_score_codex":0.004048418,"about_ca_topic_score_gemma":0.0023492877,"teacher_disagreement_score":0.015456731,"about_ca_system_score_codex":0.0009388577,"about_ca_system_score_gemma":0.0009464054,"threshold_uncertainty_score":0.081744015},"labels":[],"label_agreement":null},{"id":"W4414710539","doi":"10.6000/1929-6029.2025.14.57","title":"A Novel Method for Viral Conjunctivitis Detection using CNN-Based Image Analysis","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image processing; Preprocessor; Convolutional neural network; Deep learning; Medical imaging; Reliability (semiconductor); Field (mathematics); Digital image processing; Feature extraction","score_opus":0.04804412656212525,"score_gpt":0.4830535135247362,"score_spread":0.43500938696261093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414710539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07396117,0.0026836195,0.90807855,0.00066233333,0.0006489109,0.00042706027,0.00095629465,0.00457762,0.008004507],"genre_scores_gemma":[0.4540331,0.002513211,0.5255967,0.0008102477,0.00034050748,0.00030747184,0.0026211936,0.0003152148,0.013462338],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994487,0.000038834085,0.00003780971,0.00014611508,0.00021531811,0.000113216716],"domain_scores_gemma":[0.99965036,0.00004135072,0.000044574168,0.00005094886,0.00018500285,0.000027869628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005374388,0.0011348947,0.00083511614,0.0020658623,0.00041608198,0.0009874791,0.0011540403,0.0008460629,0.0017465018],"category_scores_gemma":[0.00096047873,0.0004481793,0.0012796493,0.0010219149,0.00036735137,0.0009697732,0.0009558699,0.00083568215,0.0012121138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037922492,0.00020842816,0.008379473,0.00028011765,0.00024310181,0.0005932661,0.000105482795,0.02173697,0.14839719,0.0024599074,0.012639161,0.8045776],"study_design_scores_gemma":[0.000026560303,0.00011320583,0.00625284,0.00004284051,0.00012321622,0.00072042434,0.000053931024,0.9288175,0.054872055,0.0014296421,0.00750349,0.000044354158],"about_ca_topic_score_codex":0.009360395,"about_ca_topic_score_gemma":0.011209133,"teacher_disagreement_score":0.009360395,"about_ca_system_score_codex":0.00077186857,"about_ca_system_score_gemma":0.0010253687,"threshold_uncertainty_score":0.018611848},"labels":[],"label_agreement":null},{"id":"W4414710547","doi":"10.6000/1929-6029.2025.14.56","title":"Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorical variable; Support vector machine; Logistic regression; Random forest; Heart disease; Set (abstract data type); Test set; Data set; Hyperparameter optimization","score_opus":0.42112494453671073,"score_gpt":0.65631888865129,"score_spread":0.23519394411457922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414710547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7354772,0.029957367,0.2092978,0.005846096,0.0012485028,0.00034655555,0.0038326837,0.0025931604,0.011400639],"genre_scores_gemma":[0.9664345,0.002092835,0.026809126,0.00022314933,0.00020068936,0.00011732316,0.0027245367,0.00014613375,0.001251691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99432653,0.0037786092,0.00034979382,0.0005123589,0.0007321819,0.00030056023],"domain_scores_gemma":[0.939008,0.054734785,0.001055834,0.0013368736,0.0033795321,0.00048508783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016651176,0.0017674665,0.0015585367,0.0045421557,0.0006295869,0.0018420228,0.0012619735,0.0015233257,0.0021047266],"category_scores_gemma":[0.0351325,0.0004096762,0.0021074945,0.0019043238,0.00042468577,0.0018797098,0.0008325585,0.0015642495,0.00063534564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018412414,0.00043539787,0.040001083,0.00047047954,0.0009572014,0.00017313722,0.00012583334,0.839378,0.00046057778,0.004421825,0.0062166,0.10551852],"study_design_scores_gemma":[0.000022350938,0.00019319242,0.003856909,0.000039332608,0.00009914448,0.000031055835,0.000053596,0.993317,0.00026628334,0.0016712835,0.00043104365,0.000018804907],"about_ca_topic_score_codex":0.013256225,"about_ca_topic_score_gemma":0.0071694483,"teacher_disagreement_score":0.016651176,"about_ca_system_score_codex":0.0022626205,"about_ca_system_score_gemma":0.001474984,"threshold_uncertainty_score":0.088060856},"labels":[],"label_agreement":null},{"id":"W4415170028","doi":"10.6000/1929-6029.2025.14.58","title":"AI-Powered CNN Model for Automated Lung Cancer Diagnosis in Medical Imaging","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprocessor; Convolutional neural network; Normalization (sociology); Medical imaging; Pixel; Grayscale; Deep learning; Pattern recognition (psychology)","score_opus":0.03140246757287672,"score_gpt":0.5195471447686191,"score_spread":0.48814467719574234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415170028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23724781,0.0108321095,0.7105684,0.0031599521,0.0009091735,0.00050795125,0.0054878574,0.012292786,0.018993864],"genre_scores_gemma":[0.86340755,0.0019534419,0.10995235,0.00087154,0.00026018993,0.0003134888,0.0069210045,0.00018001169,0.016140463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997894,0.000030349056,0.000012875377,0.00006679438,0.00005416613,0.000046420522],"domain_scores_gemma":[0.9997515,0.00007299354,0.000024753937,0.000026958078,0.00010690728,0.000016854498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055423816,0.0009339895,0.0005213758,0.00068363,0.00026336257,0.0006104215,0.0015513862,0.0010179877,0.0031146368],"category_scores_gemma":[0.0013739556,0.0003091531,0.0007361977,0.00060425996,0.00022091219,0.00068958063,0.000547184,0.00097613776,0.001510128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061493163,0.00028354157,0.007816942,0.0003449944,0.00025700463,0.00033588827,0.00005805137,0.492422,0.01670273,0.0031798668,0.022662498,0.45532152],"study_design_scores_gemma":[0.00000912154,0.000039990467,0.0009215039,0.000016818633,0.000029416624,0.000052663825,0.000005646887,0.9942468,0.0027039961,0.0006702568,0.0012969152,0.00000683005],"about_ca_topic_score_codex":0.021647418,"about_ca_topic_score_gemma":0.024090162,"teacher_disagreement_score":0.021647418,"about_ca_system_score_codex":0.0012769654,"about_ca_system_score_gemma":0.0012473422,"threshold_uncertainty_score":0.04304284},"labels":[],"label_agreement":null},{"id":"W4415465276","doi":"10.6000/1929-6029.2025.14.60","title":"Development and Validation of a Brief Instrument to Evaluate Primary-Care AMI Management in Mexico","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Internal consistency; Reliability (semiconductor); Content validity; Sample (material); Myocardial infarction; Consistency (knowledge bases); Test (biology); Index (typography)","score_opus":0.1281318556237505,"score_gpt":0.5606090325150296,"score_spread":0.43247717689127907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415465276","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906748,0.00029679638,0.0030142178,0.00033524144,0.00003311582,0.001927641,0.0012438381,0.000050309907,0.0024242],"genre_scores_gemma":[0.9639066,0.00072486483,0.025201697,0.00018922075,0.00003490771,0.006328401,0.0025733025,0.000012828671,0.0010281301],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982931,0.0007417633,0.00029572885,0.0001567511,0.00034080993,0.00017187033],"domain_scores_gemma":[0.9949096,0.001716623,0.001569913,0.000273001,0.0012771302,0.0002536842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070972885,0.00038336377,0.00047294144,0.0017401403,0.00069092587,0.00084162527,0.0006172027,0.000444744,0.0017086179],"category_scores_gemma":[0.009581687,0.00033661904,0.0005753614,0.0012477897,0.00040888126,0.0007005485,0.0009032794,0.0006430804,0.00020549676],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021116866,0.0010422077,0.9267516,0.0003138558,0.00009677008,0.00013973647,0.005049978,0.00045256826,0.0010941342,0.00046209924,0.0015578949,0.06282795],"study_design_scores_gemma":[0.00005621113,0.0007562708,0.99192315,0.00014762438,0.000053476968,0.000092980496,0.0025463868,0.00086751493,0.00030610454,0.00016099884,0.0030745256,0.000014768386],"about_ca_topic_score_codex":0.006404067,"about_ca_topic_score_gemma":0.008673308,"teacher_disagreement_score":0.0070972885,"about_ca_system_score_codex":0.0017013768,"about_ca_system_score_gemma":0.002655271,"threshold_uncertainty_score":0.037534475},"labels":[],"label_agreement":null},{"id":"W4415465281","doi":"10.6000/1929-6029.2025.14.59","title":"A Flexible Extension of the Log-Logistic Distribution with Application to Cancer Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kurtosis; Extension (predicate logic); Quantile; Flexibility (engineering); Skewness; Order statistic; Hazard; Novelty","score_opus":0.23377022193655236,"score_gpt":0.5746012732056966,"score_spread":0.34083105126914426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415465281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030219212,0.001701592,0.9630859,0.00130123,0.00013594476,0.00012420278,0.0006442689,0.0005137742,0.002273833],"genre_scores_gemma":[0.70218617,0.0045315227,0.28154877,0.0010440324,0.000558357,0.00073140155,0.0022629767,0.0004156351,0.006721221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99677026,0.0020489537,0.000112190486,0.00044699153,0.00043752234,0.00018414993],"domain_scores_gemma":[0.9907736,0.006651738,0.0006423693,0.00097513857,0.00071304396,0.00024402363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009712924,0.00097476476,0.0010989221,0.002258637,0.0007644839,0.0019279593,0.0032459295,0.0018627855,0.0029919026],"category_scores_gemma":[0.029348815,0.00054042303,0.0020390577,0.0033319772,0.0016995699,0.0027027014,0.0025500015,0.0033477147,0.0010186514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034004764,0.00012258714,0.03489838,0.00041710105,0.00031482938,0.0022313583,0.0006366015,0.4702095,0.0013904774,0.24380337,0.012622201,0.23301354],"study_design_scores_gemma":[0.000031893767,0.000078884776,0.0030555755,0.000066860266,0.000032209486,0.0007638469,0.00012606928,0.8675623,0.00022306744,0.121314295,0.0066925664,0.00005248929],"about_ca_topic_score_codex":0.005096979,"about_ca_topic_score_gemma":0.003705844,"teacher_disagreement_score":0.009712924,"about_ca_system_score_codex":0.0011269099,"about_ca_system_score_gemma":0.0016274352,"threshold_uncertainty_score":0.05136746},"labels":[],"label_agreement":null},{"id":"W4415465292","doi":"10.6000/1929-6029.2025.14.61","title":"The Table 2 Fallacy and Overfitting: A Persistent Problem in Contemporary Research?","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fallacy; Spurious relationship; Consumption (sociology); Statistical model; Directed acyclic graph; Internal validity; Selection (genetic algorithm); Model selection; Statistical hypothesis testing","score_opus":0.559293903084334,"score_gpt":0.5948790970291169,"score_spread":0.03558519394478288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415465292","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008928444,0.019034324,0.78696615,0.15901777,0.01244457,0.0017288255,0.0020948597,0.0016234348,0.008161703],"genre_scores_gemma":[0.18903767,0.011053657,0.62666106,0.14715227,0.01041759,0.008634595,0.0018022981,0.00122922,0.0040116096],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.5733888,0.3292822,0.028427644,0.030209774,0.037338242,0.0013534175],"domain_scores_gemma":[0.15767005,0.7633251,0.017293453,0.046022393,0.0146305775,0.0010583865],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.45819488,0.0021909946,0.0068024048,0.0071067642,0.0031221018,0.008551018,0.007913739,0.0072659967,0.009913805],"category_scores_gemma":[0.73327565,0.0025777956,0.004829697,0.010780341,0.021086771,0.012637437,0.0058169393,0.013909008,0.0022985656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012692203,0.0002251411,0.033711243,0.008276048,0.004732871,0.0011984846,0.010713394,0.008648957,0.0005491627,0.34491885,0.11672772,0.4690289],"study_design_scores_gemma":[0.00044941134,0.000302591,0.0055637415,0.0049404334,0.000648991,0.000859028,0.0015519025,0.018087534,0.00065600575,0.89928925,0.06738646,0.00026460784],"about_ca_topic_score_codex":0.0078065684,"about_ca_topic_score_gemma":0.007985158,"teacher_disagreement_score":0.54180515,"about_ca_system_score_codex":0.0047147293,"about_ca_system_score_gemma":0.008358991,"threshold_uncertainty_score":0.6681422},"labels":[],"label_agreement":null},{"id":"W4416458962","doi":"10.6000/1929-6029.2025.14.63","title":"Automated Detection of Posterior Tibial Slope on X-Ray Images Using VGG19","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Osteoarthritis; Joint (building); Feature (linguistics); Knee Joint; Medical imaging; Weakness; Feature extraction","score_opus":0.04478883652116666,"score_gpt":0.4511463758327046,"score_spread":0.40635753931153795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416458962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6907204,0.003056264,0.27272305,0.00038909895,0.0002164292,0.0006036598,0.011570714,0.014826042,0.0058944523],"genre_scores_gemma":[0.7756203,0.0014589074,0.20868589,0.00013527236,0.00010866019,0.00024313545,0.009985952,0.0003867165,0.0033751554],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979883,0.000022883141,0.000016747746,0.000053885324,0.000064721055,0.000042905685],"domain_scores_gemma":[0.9997242,0.000067774046,0.000047974005,0.000030290692,0.00010247463,0.000027131848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003437223,0.0006616259,0.0005972913,0.004854562,0.00015116743,0.00087650435,0.00045419423,0.00077441,0.003234504],"category_scores_gemma":[0.0008203084,0.0002419676,0.00046220465,0.0017780311,0.00020736521,0.00027291517,0.00048750403,0.00029644792,0.0018936483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011903805,0.00024521846,0.044239774,0.00063766266,0.00023097868,0.0011050412,0.00020731825,0.011748057,0.215321,0.0006575279,0.009466198,0.7149508],"study_design_scores_gemma":[0.00015610561,0.0005350844,0.41611835,0.00020861429,0.00021964154,0.0039402684,0.0005079846,0.4710475,0.09186882,0.0015807739,0.013687373,0.0001294309],"about_ca_topic_score_codex":0.0035434058,"about_ca_topic_score_gemma":0.006249359,"teacher_disagreement_score":0.004854562,"about_ca_system_score_codex":0.0002002497,"about_ca_system_score_gemma":0.00040031315,"threshold_uncertainty_score":0.010820508},"labels":[],"label_agreement":null},{"id":"W4416458995","doi":"10.6000/1929-6029.2025.14.62","title":"Robustness of Bayesian Methods in Healthcare System Assessment: A Comprehensive Review","year":2025,"lang":"en","type":"review","venue":"International Journal of Statistics in Medical Research","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Frequentist inference; Robustness (evolution); Bayesian probability; Pooling; Health care; Bayesian inference; Probabilistic logic; Inference","score_opus":0.6210497877290394,"score_gpt":0.6963659807216337,"score_spread":0.07531619299259429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416458995","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025474204,0.97780997,0.01635237,0.0036972503,0.0002633699,0.000054998705,0.00009141087,0.000035638073,0.0014403252],"genre_scores_gemma":[0.011702771,0.9737516,0.012311509,0.0010583998,0.00071587466,0.00016864111,0.00009649894,0.000036927162,0.00015775413],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9717523,0.017722161,0.0032151572,0.0017153115,0.0052788653,0.00031616015],"domain_scores_gemma":[0.7241529,0.25673983,0.006227668,0.0026428897,0.009623733,0.0006129629],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.053051706,0.0018293746,0.003723339,0.007958595,0.0007466502,0.004389766,0.0031211816,0.003584551,0.004300862],"category_scores_gemma":[0.16217892,0.0014486347,0.0039307,0.0077224355,0.0033561205,0.00433388,0.002374573,0.004040992,0.00094515027],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012787853,0.000048441867,0.0017338478,0.048069935,0.0016983934,0.00007491748,0.00039588354,0.009650621,0.00010996948,0.053417835,0.0117358295,0.87293637],"study_design_scores_gemma":[0.00016883577,0.0003671143,0.0065354453,0.2325503,0.0055388086,0.0011769782,0.00054895424,0.02018249,0.0011165448,0.2579217,0.4734248,0.0004680232],"about_ca_topic_score_codex":0.009967822,"about_ca_topic_score_gemma":0.008203018,"teacher_disagreement_score":0.9469483,"about_ca_system_score_codex":0.004421597,"about_ca_system_score_gemma":0.010622892,"threshold_uncertainty_score":0.2805676},"labels":[],"label_agreement":null},{"id":"W4416572479","doi":"10.6000/1929-6029.2025.14.66","title":"Comparing Frequentist and Bayesian Quantile Regression Models for Child Hypertension in South Africa","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Blood Pressure and Hypertension Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Frequentist inference; Quantile regression; Quantile; Bayesian probability; Bayesian linear regression; Credible interval; Confidence interval; Bayesian inference; Regression","score_opus":0.13262596626443204,"score_gpt":0.43114908417400777,"score_spread":0.29852311790957575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416572479","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7946282,0.0027355605,0.19644563,0.0023284229,0.0000706372,0.00019812997,0.0010360213,0.00020752501,0.0023497876],"genre_scores_gemma":[0.98102057,0.0006640262,0.01675177,0.00009772898,0.000033399254,0.000112169466,0.0004924645,0.00003657414,0.000791192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954691,0.0036381604,0.000112460686,0.0003762439,0.00017184387,0.00023215997],"domain_scores_gemma":[0.9650581,0.031456504,0.0017702923,0.00058967416,0.00087068434,0.00025476035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015980186,0.0006620132,0.0010411132,0.0008790939,0.0004007507,0.0015571964,0.001653469,0.0010439855,0.002431387],"category_scores_gemma":[0.052650522,0.00047099363,0.0013426482,0.0010252574,0.00068700116,0.0010399984,0.0012887365,0.00156139,0.00026695343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010963269,0.00016905293,0.12929873,0.00046133052,0.0013365902,0.00040031257,0.0018677439,0.7804345,0.00039456796,0.025212932,0.002106802,0.05722108],"study_design_scores_gemma":[0.00008557349,0.000140967,0.028340798,0.00016921137,0.00018628346,0.000077400604,0.00044847478,0.95360136,0.000104440056,0.015499746,0.0013090234,0.00003667516],"about_ca_topic_score_codex":0.054526675,"about_ca_topic_score_gemma":0.022103747,"teacher_disagreement_score":0.054526675,"about_ca_system_score_codex":0.0015130104,"about_ca_system_score_gemma":0.0013710147,"threshold_uncertainty_score":0.108418584},"labels":[],"label_agreement":null},{"id":"W4416572547","doi":"10.6000/1929-6029.2025.14.65","title":"Bilal-G Family of Distributions with Applications to Biomedical and Reliability Engineering Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Akaike information criterion; Reliability (semiconductor); Flexibility (engineering); Bayesian information criterion; Probability density function; Bayesian probability; Hazard; Component (thermodynamics); Probability distribution","score_opus":0.12034171297698809,"score_gpt":0.5157206091144363,"score_spread":0.3953788961374482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416572547","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009471849,0.0007926378,0.9866614,0.000512864,0.00005264873,0.00014808346,0.00056677725,0.000806125,0.0009875898],"genre_scores_gemma":[0.29451448,0.0028391196,0.69046545,0.0010891537,0.00029436135,0.0016605718,0.004516021,0.0008031249,0.0038177364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928617,0.0039814254,0.00037024388,0.0011303244,0.0013671826,0.0002891593],"domain_scores_gemma":[0.95891935,0.03173497,0.0021702067,0.0036128918,0.0029842225,0.0005784098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018000964,0.0014429347,0.0017857024,0.005009904,0.0012583757,0.0027899859,0.003109508,0.002333438,0.004498363],"category_scores_gemma":[0.05852524,0.00069474295,0.0024148524,0.0035268087,0.0027195844,0.0038979314,0.0033099442,0.0045471867,0.0020861658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044275582,0.000194748,0.022672387,0.00063474476,0.0003644725,0.0009721807,0.00094468886,0.3572184,0.002053611,0.33808488,0.0129741635,0.263443],"study_design_scores_gemma":[0.000044495126,0.0001257028,0.002013497,0.00012971433,0.00003399731,0.00055736763,0.00014948632,0.7798207,0.0006998467,0.20621786,0.0101301735,0.00007733218],"about_ca_topic_score_codex":0.004413475,"about_ca_topic_score_gemma":0.0027400546,"teacher_disagreement_score":0.018000964,"about_ca_system_score_codex":0.0020188524,"about_ca_system_score_gemma":0.002406493,"threshold_uncertainty_score":0.09519929},"labels":[],"label_agreement":null},{"id":"W4416597810","doi":"10.6000/1929-6029.2025.14.64","title":"Machine Learning-Based Prediction of Seasonal Influenza Trends in Saudi Arabia: A Tool for Regional Public Health Planning","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"World Health Organization","keywords":"Preparedness; Seasonal influenza; Random forest; Generalization; Public health; Population; Support vector machine","score_opus":0.11869238465763388,"score_gpt":0.48425609025335664,"score_spread":0.36556370559572277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416597810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9515851,0.0010529398,0.04117676,0.001360914,0.00004194545,0.00005860258,0.002634796,0.00047285898,0.0016160745],"genre_scores_gemma":[0.9902526,0.00020410425,0.008266152,0.0000295616,0.000012932772,0.000011786569,0.0010492869,0.000005938245,0.00016746542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981683,0.00009254149,0.000017852399,0.000030302399,0.000023168317,0.00001933551],"domain_scores_gemma":[0.9989863,0.00049193116,0.00016817143,0.0000668366,0.00023325927,0.000053411222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104698,0.00041125488,0.000281969,0.0010019682,0.00013318549,0.00054701424,0.00032365703,0.00030851626,0.000633506],"category_scores_gemma":[0.0036124708,0.000126266,0.00032872966,0.00047513068,0.000096394,0.00046795903,0.0003185369,0.00037616282,0.0002092028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028986484,0.00017993734,0.3634592,0.00014939281,0.0001848025,0.00013909268,0.0001709199,0.5020338,0.0022224635,0.0014940081,0.0028431462,0.12683322],"study_design_scores_gemma":[0.00000822591,0.00006218675,0.028214855,0.000024663275,0.000026246162,0.000018260342,0.00021082597,0.96943665,0.00073707453,0.0006417733,0.00061030465,0.0000089285795],"about_ca_topic_score_codex":0.030632751,"about_ca_topic_score_gemma":0.019556616,"teacher_disagreement_score":0.030632751,"about_ca_system_score_codex":0.00048640004,"about_ca_system_score_gemma":0.00081473996,"threshold_uncertainty_score":0.060908914},"labels":[],"label_agreement":null},{"id":"W4416853254","doi":"10.6000/1929-6029.2025.14.67","title":"Intelligent MRI Analysis for Parkinson’s Disease Detection","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pattern recognition (psychology); Feature (linguistics); Decision tree; Magnetic resonance imaging; Brain disease; Tree (set theory)","score_opus":0.054257686746451664,"score_gpt":0.45443970336537615,"score_spread":0.4001820166189245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416853254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0752968,0.0042965855,0.9115925,0.00038321922,0.00015607238,0.00015587543,0.00048496443,0.0029226465,0.0047113826],"genre_scores_gemma":[0.5650379,0.002626024,0.42775995,0.0002717551,0.00018251108,0.00014238454,0.0007660649,0.00010990993,0.0031035156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995691,0.00007015523,0.000040808165,0.00009668522,0.00018504323,0.000038184226],"domain_scores_gemma":[0.999521,0.0001257952,0.00007525749,0.000046828372,0.00020813852,0.000022954911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051532243,0.0006103718,0.0006127225,0.0025576476,0.00029734182,0.0007475519,0.00036581577,0.0005153789,0.0012657794],"category_scores_gemma":[0.0012016543,0.00021929712,0.00051749684,0.00090469886,0.00023461583,0.00047671422,0.00033760717,0.0003244999,0.0011521204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031171125,0.00012546853,0.011655443,0.00036982028,0.00010318634,0.00031863205,0.00011124433,0.006921858,0.20067678,0.002143517,0.004029032,0.7732334],"study_design_scores_gemma":[0.000069280635,0.0007510512,0.06708826,0.00014898018,0.00044453988,0.0039918968,0.0002628265,0.69058216,0.20099954,0.006850148,0.028646277,0.00016502733],"about_ca_topic_score_codex":0.0008551018,"about_ca_topic_score_gemma":0.0013753087,"teacher_disagreement_score":0.0025576476,"about_ca_system_score_codex":0.00021873912,"about_ca_system_score_gemma":0.00032629672,"threshold_uncertainty_score":0.004234493},"labels":[],"label_agreement":null},{"id":"W4417107648","doi":"10.6000/1929-6029.2025.14.70","title":"A New Robust Imputation Method for Longitudinal Data with Non-Normal Continuous Outcomes","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Imputation (statistics); Longitudinal data; Multivariate statistics; Normality; Robustness (evolution); Regression","score_opus":0.1668352769701048,"score_gpt":0.5590105407798045,"score_spread":0.3921752638096997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417107648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054953713,0.00018993,0.9986442,0.00010719037,0.00006252347,0.000023859448,0.00010792377,0.00018189597,0.00013292547],"genre_scores_gemma":[0.03165555,0.0006699489,0.9629323,0.00024364701,0.0003022818,0.0004807622,0.0010786504,0.00022233833,0.0024144256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946343,0.0028807658,0.0002969681,0.0009732265,0.0010427876,0.00017189884],"domain_scores_gemma":[0.9939276,0.003290356,0.0006677003,0.0007215741,0.0012339706,0.00015872557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075599025,0.0008755186,0.0021691923,0.001747438,0.00072872755,0.0012186662,0.0038484146,0.0018758872,0.004180765],"category_scores_gemma":[0.018111076,0.00070969714,0.0032639957,0.0031866888,0.0005419,0.0018352107,0.0016674846,0.0029512362,0.001897804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005327939,0.00020984972,0.005934746,0.00093066576,0.0013995757,0.0007423446,0.00041378706,0.18740998,0.007051157,0.08189422,0.025227943,0.6882529],"study_design_scores_gemma":[0.00011175297,0.00015754103,0.0013742594,0.000114024384,0.00020616356,0.0006777259,0.000039616636,0.94666505,0.002102749,0.03303507,0.015413375,0.00010269259],"about_ca_topic_score_codex":0.0022467107,"about_ca_topic_score_gemma":0.002037629,"teacher_disagreement_score":0.0075599025,"about_ca_system_score_codex":0.0005549991,"about_ca_system_score_gemma":0.0019999233,"threshold_uncertainty_score":0.039981008},"labels":[],"label_agreement":null},{"id":"W4417107654","doi":"10.6000/1929-6029.2025.14.71","title":"Adversarial Machine Learning in Healthcare: Risks to AI-Driven Diagnostics and Treatment Plans","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Adversarial machine learning; Smoothing; Software deployment; Deep learning; Randomized experiment; Resilience (materials science)","score_opus":0.06891208228734991,"score_gpt":0.47407467883792326,"score_spread":0.40516259655057335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417107654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15379818,0.002054193,0.8157909,0.015274836,0.000337057,0.00019307474,0.000278187,0.0010456211,0.011227928],"genre_scores_gemma":[0.9590568,0.00046940343,0.03850032,0.00088446686,0.000077586,0.00006697798,0.00008063461,0.00005150815,0.00081230106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942947,0.0034237632,0.00020631733,0.0005149863,0.0011929526,0.00036719866],"domain_scores_gemma":[0.9740538,0.018823927,0.0018930222,0.003546446,0.0012747167,0.00040805625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009925335,0.0007107909,0.0005039808,0.0004624433,0.00048106583,0.0016370245,0.00097102777,0.0012709209,0.0015127568],"category_scores_gemma":[0.036005154,0.00032037188,0.0004320996,0.00028585023,0.002397001,0.002125584,0.0020748144,0.002798842,0.00034263774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005574289,0.00014519993,0.015111279,0.00023212489,0.00020819965,0.0003198314,0.0003848797,0.72512484,0.010307698,0.09872782,0.004607536,0.14427324],"study_design_scores_gemma":[0.000030103014,0.00029369903,0.0029073432,0.00012501913,0.000037629557,0.0002695779,0.00013109004,0.9110894,0.009732333,0.06961903,0.005719761,0.00004492712],"about_ca_topic_score_codex":0.0016377947,"about_ca_topic_score_gemma":0.0012281652,"teacher_disagreement_score":0.009925335,"about_ca_system_score_codex":0.0012881125,"about_ca_system_score_gemma":0.0016951164,"threshold_uncertainty_score":0.05249083},"labels":[],"label_agreement":null},{"id":"W4417110018","doi":"10.6000/1929-6029.2025.14.68","title":"To Identify the Predictors of Mortality in Renal Patients Undergoing Dialysis","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dialysis; Proportional hazards model; Random forest; Feature selection; Risk of mortality; Hazard ratio; Lasso (programming language); Discriminative model","score_opus":0.04791723511370771,"score_gpt":0.47027150551558805,"score_spread":0.42235427040188034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417110018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98154575,0.004038822,0.00813321,0.0016862972,0.00012737807,0.00009388195,0.0031988812,0.000076814664,0.0010987894],"genre_scores_gemma":[0.9923022,0.0007731049,0.0040051774,0.00021095811,0.0001178716,0.00005862443,0.002246069,0.000007631769,0.00027846417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99924964,0.0003292431,0.000084302665,0.000113571106,0.0001444281,0.00007880882],"domain_scores_gemma":[0.9973183,0.0012458259,0.0007382013,0.0001518378,0.00039051182,0.00015532502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017392137,0.00047405195,0.0005948702,0.0007525302,0.00029799424,0.0007286536,0.00036893997,0.00050038,0.0010230162],"category_scores_gemma":[0.007218397,0.00013771752,0.0008357861,0.00091654115,0.00013455826,0.0005770288,0.0006395089,0.0012014861,0.00037835122],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018721489,0.0001537094,0.971349,0.0001007554,0.00017032913,0.0000504896,0.000046889858,0.0014575883,0.00032141493,0.00015078356,0.0013780408,0.02463373],"study_design_scores_gemma":[0.00003759456,0.0004811383,0.95533395,0.00014338468,0.00031749907,0.00035216834,0.00033023025,0.037558436,0.0010335705,0.001175752,0.0032080936,0.000028077975],"about_ca_topic_score_codex":0.001616296,"about_ca_topic_score_gemma":0.0028153786,"teacher_disagreement_score":0.0017392137,"about_ca_system_score_codex":0.00025145852,"about_ca_system_score_gemma":0.00097573665,"threshold_uncertainty_score":0.00919795},"labels":[],"label_agreement":null},{"id":"W4417150243","doi":"10.6000/1929-6029.2025.14.73","title":"Validating Medical Treatment Effects by Projected F-tests under High Dimension with a Small Sample Size","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tianjin Medical University","keywords":"Sample size determination; Principal component analysis; Multivariate statistics; Test statistic; Statistical hypothesis testing; Homogeneity (statistics); Monte Carlo method; Type I and type II errors; Statistic; Dimension (graph theory)","score_opus":0.11399389953813378,"score_gpt":0.504874503291354,"score_spread":0.3908806037532202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417150243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02610724,0.0005029058,0.96989584,0.0006145033,0.00019304616,0.0003916493,0.00053033524,0.00039026342,0.0013741859],"genre_scores_gemma":[0.48946384,0.00046754937,0.5041912,0.0009911633,0.00038900872,0.0028018313,0.0010392156,0.00018989613,0.00046633548],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9284294,0.054355312,0.0029197722,0.006292176,0.007393209,0.0006100054],"domain_scores_gemma":[0.62803555,0.33046177,0.011651774,0.021320993,0.0074610505,0.0010687426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06550764,0.0016036502,0.0027289586,0.0026021393,0.0014609519,0.0023940327,0.0024546585,0.0022377018,0.0045914133],"category_scores_gemma":[0.3445327,0.00060376694,0.0026867688,0.0021627017,0.006419085,0.0032121742,0.0031984064,0.0033235948,0.00074300263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034478891,0.0009133509,0.10522361,0.0027573816,0.004587988,0.0012217856,0.0018621237,0.0925224,0.011463195,0.16006231,0.008818287,0.60711974],"study_design_scores_gemma":[0.0009827217,0.003547677,0.052665375,0.00058583193,0.0011785913,0.0013871673,0.0006510967,0.4191107,0.01609667,0.49072075,0.012834586,0.00023880157],"about_ca_topic_score_codex":0.00087369105,"about_ca_topic_score_gemma":0.00057213,"teacher_disagreement_score":0.06550764,"about_ca_system_score_codex":0.00086580607,"about_ca_system_score_gemma":0.0029632668,"threshold_uncertainty_score":0.3464417},"labels":[],"label_agreement":null},{"id":"W7110098224","doi":"10.6000/1929-6029.2025.14.69","title":"Bayesian Inference and Sensitivity Analysis of Dengue Transmission in Sudan","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dengue fever; Bayesian probability; Transmission (telecommunications); Case fatality rate; Sensitivity (control systems); Vector (molecular biology); Robustness (evolution); Latin hypercube sampling; Aedes aegypti","score_opus":0.027156004335766468,"score_gpt":0.47025598462134865,"score_spread":0.4430999802855822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110098224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47093853,0.004198535,0.51358443,0.0015588555,0.00013269263,0.0011662013,0.0018798062,0.000247942,0.0062929387],"genre_scores_gemma":[0.9636692,0.0008583839,0.033304553,0.00017882777,0.000040714283,0.00047644856,0.0006018654,0.000026425236,0.00084368524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9719126,0.024981175,0.00054686685,0.0011796271,0.00084876025,0.00053096033],"domain_scores_gemma":[0.8375077,0.15447374,0.0036347748,0.0021922223,0.0018616383,0.00032995085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05247884,0.0013649799,0.00223779,0.0024058644,0.0007252811,0.0018043916,0.0014935227,0.0014135607,0.0023448607],"category_scores_gemma":[0.10621636,0.00087316305,0.0031948723,0.0011822645,0.0014711343,0.0013793383,0.0023526184,0.0020785904,0.0001192782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032064118,0.00007373825,0.0064059836,0.0002920546,0.0010989939,0.00024139669,0.00011702394,0.96606165,0.00037353428,0.01570774,0.0003915184,0.008915635],"study_design_scores_gemma":[0.00006326586,0.0002107284,0.0035894308,0.0001070389,0.00033122016,0.000089826004,0.000078863915,0.963836,0.00042403984,0.030620221,0.00059930084,0.000050029186],"about_ca_topic_score_codex":0.008845227,"about_ca_topic_score_gemma":0.0034989568,"teacher_disagreement_score":0.05247884,"about_ca_system_score_codex":0.0029642477,"about_ca_system_score_gemma":0.0016346782,"threshold_uncertainty_score":0.27753794},"labels":[],"label_agreement":null},{"id":"W7110311820","doi":"10.6000/1929-6029.2025.14.72","title":"Beyond the Cox Model: A Comparative Parametric Survival Modelling of Time to First Birth Among Married Women","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Global Maternal and Child Health","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proportional hazards model; Parametric statistics; Survival analysis; Hazard; Hazard ratio; Parametric model; Hazard model; Live birth","score_opus":0.07770865344373248,"score_gpt":0.4392534370148948,"score_spread":0.36154478357116226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110311820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2016824,0.0055438336,0.7812552,0.0032891266,0.0003572365,0.00062668684,0.0013507085,0.00041880048,0.005476049],"genre_scores_gemma":[0.9158049,0.003799404,0.07545769,0.00027492267,0.0002875033,0.00077140116,0.0007515509,0.00011872367,0.0027340136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99134606,0.007212432,0.00018741205,0.000488582,0.00049244333,0.00027299256],"domain_scores_gemma":[0.9464734,0.0481319,0.002173242,0.0015507333,0.001268967,0.00040176863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023067506,0.00073655177,0.0012095222,0.0015163898,0.00047382267,0.0021557575,0.0021379457,0.0011228909,0.0025871473],"category_scores_gemma":[0.058477797,0.00040574724,0.0023343172,0.0016292506,0.00095417147,0.0016072554,0.0016191107,0.0020227327,0.00031802867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097388687,0.0002444357,0.1199486,0.0009499537,0.0015300635,0.0009872597,0.004255771,0.5998376,0.0006494569,0.117081754,0.0063127726,0.14722838],"study_design_scores_gemma":[0.00005828077,0.0007064073,0.012553084,0.00032152902,0.00027892107,0.00035222698,0.00094928354,0.914538,0.00021027541,0.06248047,0.0074595367,0.00009193579],"about_ca_topic_score_codex":0.010044547,"about_ca_topic_score_gemma":0.0054542185,"teacher_disagreement_score":0.023067506,"about_ca_system_score_codex":0.0010082676,"about_ca_system_score_gemma":0.0027953668,"threshold_uncertainty_score":0.12199408},"labels":[],"label_agreement":null},{"id":"W7117253960","doi":"10.6000/1929-6029.2025.14.75","title":"Dataset-Specific Bootstrap-Stability Weighting for Calibrated and Clinically Useful Ensemble Prediction in Medical Diagnosis","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weighting; Calibration; Medical diagnosis; Random forest; Resampling; Decision tree; Complement (music); Probabilistic logic","score_opus":0.13555728040161633,"score_gpt":0.5023661450127471,"score_spread":0.3668088646111308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117253960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22328003,0.0006925109,0.77304906,0.00034385786,0.000036481644,0.00013512488,0.0001670759,0.0011913753,0.0011045312],"genre_scores_gemma":[0.87933683,0.00009362954,0.11980689,0.00008855187,0.000032866872,0.00009927672,0.00023752778,0.000074953154,0.00022948724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998195,0.0010210525,0.00009362464,0.00032468713,0.0002909161,0.000074733965],"domain_scores_gemma":[0.9945498,0.0032881498,0.0005522955,0.0006222774,0.00082668645,0.00016080996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00737721,0.0006893803,0.00071559247,0.0011622327,0.00036603864,0.00074988836,0.0008186619,0.00075638416,0.0008365111],"category_scores_gemma":[0.019411087,0.0002624054,0.0005245861,0.00057021953,0.00047260456,0.00089079014,0.001240188,0.00095741614,0.00027723316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067572034,0.00026285453,0.040271226,0.00016874235,0.00034668588,0.00012697306,0.00024049981,0.5016701,0.01709055,0.0038170267,0.0025175158,0.4328121],"study_design_scores_gemma":[0.000020575888,0.00014593462,0.0048247287,0.000025032963,0.000046263707,0.00007418401,0.000020536545,0.9835579,0.0068162167,0.0038909668,0.00055903447,0.0000185946],"about_ca_topic_score_codex":0.0015544136,"about_ca_topic_score_gemma":0.0020484994,"teacher_disagreement_score":0.00737721,"about_ca_system_score_codex":0.0006907169,"about_ca_system_score_gemma":0.00082307,"threshold_uncertainty_score":0.039014876},"labels":[],"label_agreement":null},{"id":"W7117531920","doi":"10.6000/1929-6029.2025.14.80","title":"A New Family of Generalized Distributions, with Applications and Benchmarking against Machine Learning Models","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Exponential family; Estimator; Probability density function; Probability distribution; Laplace distribution; Density estimation; Exponential function; Cumulative distribution function; Function (biology)","score_opus":0.10378976950002815,"score_gpt":0.4643605944552101,"score_spread":0.36057082495518195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117531920","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012583369,0.0006487109,0.9832902,0.00038227072,0.00007449145,0.00009785229,0.0003771313,0.0009428216,0.0016031836],"genre_scores_gemma":[0.41720182,0.0027597246,0.5683483,0.00083389384,0.00026881634,0.00084550685,0.003129141,0.0010217334,0.005591143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99687177,0.001423666,0.00015491604,0.00052054785,0.00085890025,0.00017015317],"domain_scores_gemma":[0.98935145,0.006179832,0.0008039898,0.0019132518,0.0014891521,0.0002623527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007668922,0.0011480816,0.0010078263,0.0023501986,0.00068121165,0.0022552847,0.0023121696,0.0015046749,0.0032224292],"category_scores_gemma":[0.027517866,0.00041929501,0.0015697298,0.0029573813,0.0018698865,0.004044273,0.0019905288,0.003042269,0.0014769954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025300204,0.00016988443,0.009872233,0.00035147805,0.00016424093,0.0003203612,0.00033581606,0.4644303,0.003063061,0.29481208,0.011390336,0.21483716],"study_design_scores_gemma":[0.000019896343,0.00008567882,0.0011161498,0.000048020407,0.000017902892,0.00023721858,0.00006352553,0.8940443,0.0009080357,0.09423731,0.009180838,0.000041219577],"about_ca_topic_score_codex":0.0053652246,"about_ca_topic_score_gemma":0.0041977847,"teacher_disagreement_score":0.007668922,"about_ca_system_score_codex":0.0015589277,"about_ca_system_score_gemma":0.0020756985,"threshold_uncertainty_score":0.040557623},"labels":[],"label_agreement":null},{"id":"W7117533347","doi":"10.6000/1929-6029.2025.14.83","title":"Use of Convolutional Neural Networks for Detection of Pathologies in Dental X-Ray Images in Clinical Decision Support Systems","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Relevance (law); Metric (unit); Unified Modeling Language; Decision support system; Domain (mathematical analysis); Clinical decision support system; Novelty","score_opus":0.34498977222644167,"score_gpt":0.6057074572577265,"score_spread":0.2607176850312848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117533347","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32889673,0.0022072794,0.658401,0.0011631045,0.000077708675,0.00023887146,0.0005166697,0.003456464,0.005042183],"genre_scores_gemma":[0.8846041,0.0003942294,0.11350646,0.00011335706,0.000017022176,0.00005765952,0.00038681546,0.00005633732,0.0008640684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876475,0.0004813687,0.00009902589,0.00024015941,0.00032609454,0.00008861416],"domain_scores_gemma":[0.99746007,0.0014737404,0.00025243615,0.00016713074,0.00057801954,0.00006866506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026170337,0.0008557641,0.00042703343,0.0014280486,0.00024457465,0.0011970578,0.0008355117,0.0006782189,0.0012902883],"category_scores_gemma":[0.007416986,0.00030198283,0.0006782099,0.000492678,0.00039683917,0.0009789057,0.00083193043,0.00063506264,0.0003058617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006314124,0.00025282495,0.027654124,0.00037485102,0.0003589396,0.0002683291,0.00024213719,0.47275648,0.01802225,0.0032554378,0.0017915302,0.47439176],"study_design_scores_gemma":[0.000007519567,0.000063120606,0.0031490282,0.000033824323,0.000051492436,0.000038981307,0.000028905444,0.98816526,0.0062970505,0.0014834022,0.00067309284,0.000008297913],"about_ca_topic_score_codex":0.013867353,"about_ca_topic_score_gemma":0.018067067,"teacher_disagreement_score":0.013867353,"about_ca_system_score_codex":0.0020164647,"about_ca_system_score_gemma":0.0015921444,"threshold_uncertainty_score":0.027573287},"labels":[],"label_agreement":null},{"id":"W7117535605","doi":"10.6000/1929-6029.2025.14.82","title":"Comparative Analysis of Parametric Survival Models in HIV Patient Data","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"HIV-related health complications and treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Akaike information criterion; Gompertz function; Proportional hazards model; Bayesian information criterion; Weibull distribution; Survival analysis; Goodness of fit; Parametric statistics; Model selection; Concordance","score_opus":0.23055465368630296,"score_gpt":0.5615031656646147,"score_spread":0.3309485119783117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117535605","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6791623,0.0024690954,0.31274083,0.00088795077,0.00008012413,0.00029375165,0.0016058821,0.0007981058,0.0019619786],"genre_scores_gemma":[0.96888214,0.00046620547,0.028715964,0.00008986281,0.00003799486,0.00017499352,0.0012101552,0.00006589958,0.00035679957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9807993,0.0163166,0.0005276922,0.0009816175,0.00096388895,0.0004109199],"domain_scores_gemma":[0.76257145,0.22671248,0.0040750937,0.0037415368,0.0023206244,0.0005787525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046285544,0.00095047825,0.0012423174,0.0030927896,0.00043644235,0.0019917008,0.0012737475,0.0010136149,0.0014910387],"category_scores_gemma":[0.120449275,0.00032871868,0.0020700337,0.0015683523,0.00087268895,0.0019930531,0.0013939609,0.0012855516,0.00028316918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001811778,0.00024687714,0.21280986,0.00045929218,0.0015514822,0.00065956166,0.0016074735,0.6440671,0.00079023145,0.020305596,0.0017334747,0.11395721],"study_design_scores_gemma":[0.000031137402,0.0004983562,0.02007839,0.00009374898,0.00012361466,0.0002682885,0.0005642817,0.96409214,0.00041892423,0.012817548,0.0009493407,0.00006413766],"about_ca_topic_score_codex":0.0033158911,"about_ca_topic_score_gemma":0.0021724808,"teacher_disagreement_score":0.046285544,"about_ca_system_score_codex":0.0010083157,"about_ca_system_score_gemma":0.0012512369,"threshold_uncertainty_score":0.24478424},"labels":[],"label_agreement":null},{"id":"W7117567139","doi":"10.6000/1929-6029.2025.14.79","title":"An Empirical Comparison among Four Estimation Methods for the Laplace Distribution and Its Potential Application in Medical Research","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Laplace's method; Estimator; Monte Carlo method; Estimation theory; Sample size determination; Laplace transform; Mean squared error; Scale (ratio); Inference","score_opus":0.2159397206242709,"score_gpt":0.64746413172996,"score_spread":0.43152441110568907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117567139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0546313,0.0054933685,0.9347594,0.0017215462,0.00014893826,0.00020315708,0.0002453194,0.0005461954,0.0022508132],"genre_scores_gemma":[0.5798349,0.0037963465,0.41308266,0.00070202816,0.00024603633,0.00063319976,0.0006262227,0.00024220305,0.00083645567],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97995114,0.014300548,0.0007838736,0.0017205697,0.0029781233,0.00026582048],"domain_scores_gemma":[0.69879305,0.27332997,0.00704451,0.010329765,0.009626711,0.0008760114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0520105,0.0011469633,0.0012604641,0.0028787558,0.00079837115,0.0025599562,0.0018549345,0.0025251661,0.0019794144],"category_scores_gemma":[0.2790437,0.0005384481,0.0011449313,0.0021553966,0.0027609593,0.003720853,0.002081862,0.002138321,0.00058503484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001415646,0.00024245457,0.11067337,0.0022531208,0.0012693297,0.00053624844,0.0025742087,0.25125045,0.0060981186,0.1267228,0.007372946,0.48959133],"study_design_scores_gemma":[0.00030592337,0.0009111336,0.029355898,0.0011524542,0.00045233226,0.0019853215,0.0011494928,0.8259767,0.010051295,0.11546472,0.012845429,0.0003493418],"about_ca_topic_score_codex":0.002477126,"about_ca_topic_score_gemma":0.0013590248,"teacher_disagreement_score":0.0520105,"about_ca_system_score_codex":0.0011420149,"about_ca_system_score_gemma":0.0021201205,"threshold_uncertainty_score":0.27506107},"labels":[],"label_agreement":null},{"id":"W7117572174","doi":"10.6000/1929-6029.2025.14.81","title":"A Cross-Sectional Study on Patient Safety Culture in a Tertiary Care Hospital in India","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Patient safety; Staffing; Teamwork; Safety culture; Multidisciplinary approach; Health care; Organizational culture; Patient care","score_opus":0.05917152406090519,"score_gpt":0.5537800704945371,"score_spread":0.49460854643363195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117572174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99862003,0.00011117824,0.00008494013,0.0002041204,0.000006576651,0.000054174332,0.00021820309,0.000005134102,0.0006957151],"genre_scores_gemma":[0.9990396,0.00016016513,0.00017528236,0.0002823032,0.00000921068,0.000057258607,0.00012992059,0.0000016126083,0.00014452283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99831486,0.0005102656,0.00027649206,0.00016695347,0.00038361797,0.0003479108],"domain_scores_gemma":[0.9949269,0.0011384302,0.0017133466,0.00025914036,0.000997402,0.0009648022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015207937,0.00022373912,0.00035669186,0.0011082238,0.0013082138,0.0013015276,0.00047804133,0.00044220037,0.0018787412],"category_scores_gemma":[0.003272746,0.00036773414,0.00037991168,0.001733188,0.0006644463,0.000623599,0.00090342044,0.0008731929,0.00032793396],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029007932,0.00015570935,0.9913701,0.00011667464,0.000025243491,0.00032165475,0.005274969,0.00003200784,0.00035231785,0.00004916523,0.00031793315,0.0019552577],"study_design_scores_gemma":[0.0000066220905,0.0006150318,0.9727753,0.00007612953,0.000024116089,0.0009029263,0.024403237,0.00008051562,0.00019666195,0.000023095607,0.0008797947,0.000016635524],"about_ca_topic_score_codex":0.010892655,"about_ca_topic_score_gemma":0.013336444,"teacher_disagreement_score":0.010892655,"about_ca_system_score_codex":0.0012880408,"about_ca_system_score_gemma":0.0026564265,"threshold_uncertainty_score":0.02165854},"labels":[],"label_agreement":null},{"id":"W7117608423","doi":"10.6000/1929-6029.2025.14.77","title":"Early-Stage Cardiovascular Disease Prediction Using a Sigmoidtropy-Based Decision Tree","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Decision tree; Naive Bayes classifier; Random forest; Tree (set theory); Disease; Cluster analysis","score_opus":0.2548215229229835,"score_gpt":0.5967117051398103,"score_spread":0.3418901822168268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117608423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30171257,0.0026332748,0.68595904,0.0016838153,0.0002912495,0.00032618007,0.0026395114,0.0011750066,0.0035792869],"genre_scores_gemma":[0.90109414,0.0010103392,0.09349526,0.00026321734,0.00011143103,0.00015066254,0.0021678167,0.00003161973,0.001675493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990709,0.00028956277,0.00011746225,0.00019940913,0.00019788883,0.00012475683],"domain_scores_gemma":[0.9979317,0.0012069474,0.00012353803,0.00009018432,0.0005188299,0.00012886808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028763548,0.0008178225,0.0010905587,0.0015587355,0.0005251887,0.0011641053,0.0011550621,0.00081993244,0.0015529076],"category_scores_gemma":[0.004875605,0.00031023257,0.001394093,0.0013798521,0.00029161794,0.0012587814,0.0007092198,0.0010313787,0.00059191283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011541473,0.00046725853,0.050902866,0.00024507544,0.00033421608,0.0004988052,0.00018789654,0.58263016,0.0026017397,0.0029025085,0.006130048,0.35194525],"study_design_scores_gemma":[0.000020602813,0.00011735833,0.001846333,0.000029267592,0.000051105013,0.00008332093,0.000031670028,0.9941882,0.00058410806,0.0024708246,0.00056213205,0.0000150402575],"about_ca_topic_score_codex":0.00789303,"about_ca_topic_score_gemma":0.006579931,"teacher_disagreement_score":0.00789303,"about_ca_system_score_codex":0.00072429347,"about_ca_system_score_gemma":0.0012680999,"threshold_uncertainty_score":0.015694141},"labels":[],"label_agreement":null}]}