{"meta":{"query_hash":"7fe6c767c8e6","filters":{"venue":"Journal of the American Medical Informatics Association"},"cohort_total":238,"direct_labels_cover":0,"predictions_cover":238,"exported":238,"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/7fe6c767c8e6","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+the+American+Medical+Informatics+Association"},"results":[{"id":"W107663614","doi":"10.1197/jamia.m2799","title":"Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-detection Algorithms","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Computer science; Artificial intelligence; Public health; Algorithm; Data mining; Medicine; Nursing","score_opus":0.07856225860169881,"score_gpt":0.2978317227193995,"score_spread":0.2192694641177007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107663614","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":[0.266171,0.00024406398,0.7291741,0.0007309609,0.000032705604,0.00037076283,0.00032186104,0.0011839732,0.0017706072],"genre_scores_gemma":[0.80732226,0.00011816544,0.19125673,0.000112628295,0.000021619311,0.000306429,0.000324827,0.00008586133,0.00045142323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930854,0.0030667358,0.0005080859,0.0014707498,0.0014316317,0.00043740007],"domain_scores_gemma":[0.94445527,0.041701857,0.0059366524,0.0032329177,0.00413148,0.00054184644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01273603,0.0014828397,0.0008213064,0.002496095,0.00056234613,0.002780199,0.0026169368,0.0016831725,0.0011039649],"category_scores_gemma":[0.0719076,0.0007052718,0.0015178971,0.0015250427,0.0020859772,0.0039781625,0.001835424,0.001585525,0.0002651848],"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":[0.0002173379,0.00020791104,0.018492721,0.000109484645,0.000083740095,0.0000393495,0.00037228488,0.9471242,0.0018271037,0.009507929,0.00042988994,0.02158796],"study_design_scores_gemma":[0.000013482546,0.000059130653,0.0013726921,0.00000939653,0.0000151123695,0.000013841886,0.000024328181,0.9933007,0.0008626274,0.004165302,0.00015326196,0.000010127925],"about_ca_topic_score_codex":0.024723109,"about_ca_topic_score_gemma":0.009498064,"teacher_disagreement_score":0.024723109,"about_ca_system_score_codex":0.005788632,"about_ca_system_score_gemma":0.0035545304,"threshold_uncertainty_score":0.067355394},"labels":[],"label_agreement":null},{"id":"W108802775","doi":"10.1136/amiajnl-2011-000261","title":"The challenges in making electronic health records accessible to patients: Table 1","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Public Health Ontario; University of Toronto; Carleton University; University Health Network","funders":"Ontario Ministry of Health and Long-Term Care; Government of Ontario","keywords":"Health records; Table (database); Internet privacy; Liability; Business; Computer science; Computer security; Health care; Data access; Database; Political science; Law; Finance","score_opus":0.06885914985454133,"score_gpt":0.4188123619941304,"score_spread":0.3499532121395891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W108802775","genre_codex":"commentary","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":[0.010116564,0.050832227,0.019147636,0.768041,0.0061894413,0.000242749,0.0007838283,0.00045526423,0.14419132],"genre_scores_gemma":[0.25522715,0.24624155,0.13152725,0.22822435,0.018960645,0.00067066174,0.0019514924,0.0005763187,0.11662056],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9827188,0.0062081334,0.0014603363,0.00063515466,0.008100358,0.00087710546],"domain_scores_gemma":[0.960083,0.020394834,0.0029432334,0.0016754668,0.01187359,0.0030299446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013095702,0.0005942378,0.00047559582,0.0020273738,0.0032680316,0.012824443,0.0018310518,0.005001634,0.018585762],"category_scores_gemma":[0.030941412,0.00041230576,0.00060341053,0.0028376752,0.0028793132,0.010703913,0.0035919333,0.005417131,0.0063118967],"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":[0.00007738779,0.000100855344,0.009419335,0.0022243664,0.00006443873,0.0009885028,0.0036881045,0.0010065319,0.001441127,0.12913798,0.42607868,0.4257727],"study_design_scores_gemma":[0.000014651158,0.00009552207,0.0060563693,0.002428489,0.000035770456,0.0034023183,0.006253379,0.0006881506,0.0006793529,0.033866778,0.94640356,0.00007566173],"about_ca_topic_score_codex":0.005020945,"about_ca_topic_score_gemma":0.007732274,"teacher_disagreement_score":0.018585762,"about_ca_system_score_codex":0.002557183,"about_ca_system_score_gemma":0.008629643,"threshold_uncertainty_score":0.06925756},"labels":[],"label_agreement":null},{"id":"W124914759","doi":"10.1197/jamia.m1887","title":"The Development and Evaluation of an Integrated Electronic Prescribing and Drug Management System for Primary Care","year":2005,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Primary care; Drug; Electronic prescribing; Medicine; Nursing; Pharmacology; Family medicine; Medical prescription","score_opus":0.02260549482283195,"score_gpt":0.37589358209472207,"score_spread":0.3532880872718901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W124914759","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":[0.9835745,0.00025364818,0.0110382885,0.00034497745,0.000042895477,0.0021045797,0.00021486967,0.000643783,0.0017824207],"genre_scores_gemma":[0.8830839,0.0003035202,0.11275569,0.00040171726,0.00005562124,0.0012386997,0.0008745256,0.0000311859,0.0012552246],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.996265,0.0017979019,0.00032390916,0.0003122666,0.0011391344,0.00016180445],"domain_scores_gemma":[0.9883017,0.0054108514,0.0012705295,0.0010689038,0.0030482225,0.00089984457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006981208,0.00033573288,0.00031398205,0.00048192986,0.0004643343,0.00088963227,0.00071019237,0.00062012376,0.0015441667],"category_scores_gemma":[0.023373498,0.0002683948,0.00033314628,0.0002952455,0.00037703017,0.000850423,0.0008287418,0.00043146673,0.00032679323],"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":[0.0029139167,0.011800644,0.23847394,0.0011616567,0.00025053156,0.0009179318,0.003721249,0.004389364,0.034313083,0.0003034605,0.0038740989,0.6978801],"study_design_scores_gemma":[0.0031654576,0.06427361,0.8404408,0.0005521496,0.0006773367,0.0023465988,0.002156856,0.03685117,0.029011296,0.00036909382,0.020011708,0.00014393644],"about_ca_topic_score_codex":0.0016758778,"about_ca_topic_score_gemma":0.0017653031,"teacher_disagreement_score":0.006981208,"about_ca_system_score_codex":0.001034277,"about_ca_system_score_gemma":0.0024461881,"threshold_uncertainty_score":0.036920607},"labels":[],"label_agreement":null},{"id":"W13965371","doi":"10.1136/amiajnl-2012-001160","title":"The intended and unintended consequences of communication systems on general internal medicine inpatient care delivery: a prospective observational case study of five teaching hospitals","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Sunnybrook Health Science Centre; Mount Sinai Hospital; Health Sciences Centre; University Health Network; University of Toronto","funders":"Ontario Medical Association; University of Toronto; Ontario Ministry of Health and Long-Term Care; University Health Network","keywords":"Psychological intervention; Observational study; Unintended consequences; Medicine; Pager; Nursing; Alphanumeric; Qualitative research; Family medicine; Medical education","score_opus":0.07326199188384659,"score_gpt":0.39638797298321127,"score_spread":0.3231259810993647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W13965371","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":[0.9993524,0.000103478385,0.00013097956,0.00007745599,0.0000030550761,0.00011136901,0.000021951431,0.0000016640603,0.000197496],"genre_scores_gemma":[0.9986308,0.000296364,0.00058122835,0.00010540813,0.000011291189,0.00016297385,0.000030894094,0.0000021205947,0.00017876897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99140793,0.0051305597,0.00061525573,0.00053651043,0.00077954825,0.0015301406],"domain_scores_gemma":[0.97578114,0.01166954,0.007533423,0.0009133776,0.0014737669,0.0026287443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073946337,0.0006012405,0.0005767337,0.0019266806,0.005019121,0.001639785,0.0015240185,0.0015904829,0.0013069889],"category_scores_gemma":[0.01899401,0.0009779573,0.0006011878,0.0013949602,0.0021552807,0.0012160937,0.0029033402,0.0016388358,0.00015807647],"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":[0.00047406234,0.0038719727,0.6407562,0.0005383502,0.00009206512,0.022162972,0.3115552,0.00029882402,0.0012838749,0.00040863102,0.0005294128,0.018028418],"study_design_scores_gemma":[0.000127459,0.006726685,0.40824312,0.0005549306,0.00013475571,0.009454508,0.5691249,0.00086754106,0.0015948588,0.00021521293,0.0028394635,0.00011665215],"about_ca_topic_score_codex":0.014307316,"about_ca_topic_score_gemma":0.03312833,"teacher_disagreement_score":0.014307316,"about_ca_system_score_codex":0.008425605,"about_ca_system_score_gemma":0.0050205616,"threshold_uncertainty_score":0.06113237},"labels":[],"label_agreement":null},{"id":"W142564969","doi":"10.1136/amiajnl-2013-001624","title":"À la Recherche du Temps Perdu: extracting temporal relations from medical text in the 2012 i2b2 NLP challenge","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Topic Modeling","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"U.S. National Library of Medicine","keywords":"Recall; Natural language processing; Artificial intelligence; Computer science; Task (project management); Relation (database); Relationship extraction; Post hoc; Information retrieval; Psychology; Information extraction; Cognitive psychology; Data mining; Medicine; Engineering","score_opus":0.08013885544504842,"score_gpt":0.3295911244913692,"score_spread":0.24945226904632076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W142564969","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":[0.31593913,0.024936259,0.39005795,0.041949242,0.0053976653,0.0025318284,0.13888004,0.03895889,0.041349012],"genre_scores_gemma":[0.34260166,0.0039528273,0.4056005,0.002790432,0.0018818869,0.0016656582,0.21767254,0.0029724974,0.020862041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99019927,0.004174786,0.0009488626,0.0020623815,0.002169936,0.00044482303],"domain_scores_gemma":[0.97633696,0.016175494,0.0011967281,0.0018572506,0.0033781254,0.0010553832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012444331,0.0018341264,0.0012612579,0.0043521444,0.002415804,0.003814273,0.0014875344,0.003142558,0.004367372],"category_scores_gemma":[0.041545983,0.00058590755,0.0013612983,0.003889744,0.0010291052,0.0038819162,0.0031741355,0.0027252561,0.004044179],"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":[0.0013522542,0.00047508735,0.018140959,0.004711219,0.00037515716,0.0017758238,0.0072024097,0.014958721,0.022363072,0.009746752,0.45181975,0.4670789],"study_design_scores_gemma":[0.00050828035,0.00076740846,0.049577836,0.0010093239,0.00037354176,0.004007031,0.0072187595,0.21358635,0.04836629,0.018428547,0.6556517,0.0005050021],"about_ca_topic_score_codex":0.026740681,"about_ca_topic_score_gemma":0.028653763,"teacher_disagreement_score":0.026740681,"about_ca_system_score_codex":0.00292027,"about_ca_system_score_gemma":0.00652619,"threshold_uncertainty_score":0.06581271},"labels":[],"label_agreement":null},{"id":"W1481389199","doi":"10.1136/amiajnl-2013-001636","title":"Literature review of SNOMED CT use","year":2013,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"SNOMED CT; Systematized Nomenclature of Medicine; Implementation; Computer science; Medicine; MEDLINE; Domain (mathematical analysis); Information retrieval; Medical physics; Terminology; Software engineering","score_opus":0.0225821128250893,"score_gpt":0.33241396151467795,"score_spread":0.30983184868958863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481389199","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":[0.0008307685,0.9956045,0.0003166013,0.00096093846,0.00032435943,0.00006869227,0.00056123064,0.000014732563,0.0013181497],"genre_scores_gemma":[0.008646335,0.9869653,0.0012920036,0.001476247,0.00034675002,0.00015361793,0.0008665623,0.000021790858,0.00023138241],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.98284274,0.0055968757,0.006520959,0.0013243583,0.0034182013,0.00029681483],"domain_scores_gemma":[0.8932551,0.08411138,0.011535103,0.0015710475,0.008852624,0.000674729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0144766215,0.0014159739,0.0030164805,0.043042388,0.0008791152,0.0033727556,0.0029349767,0.0021733742,0.009966039],"category_scores_gemma":[0.07022895,0.0007606728,0.0035711383,0.03388907,0.0017655132,0.004666637,0.0024304122,0.0012957582,0.0014243401],"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":[0.00017420869,0.000027374914,0.0017073489,0.7016974,0.001993346,0.0005630561,0.0014637938,0.0002035508,0.0005059084,0.0019595705,0.01584483,0.27385968],"study_design_scores_gemma":[0.000041173513,0.00010779228,0.0058109355,0.7809905,0.006859155,0.001975966,0.0013979982,0.00008889663,0.0005262324,0.0013068767,0.20084184,0.00005247102],"about_ca_topic_score_codex":0.004768458,"about_ca_topic_score_gemma":0.009882319,"teacher_disagreement_score":0.043042388,"about_ca_system_score_codex":0.0038863812,"about_ca_system_score_gemma":0.010716411,"threshold_uncertainty_score":0.07656062},"labels":[],"label_agreement":null},{"id":"W1510747555","doi":"10.1136/jamia.2001.0080324","title":"A Primer on Aspects of Cognition for Medical Informatics","year":2001,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":170,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine","keywords":"Cognition; Health informatics; Multidisciplinary approach; Variety (cybernetics); Informatics; Computer science; Set (abstract data type); Data science; Information science; Engineering informatics; Translational research informatics; Cognitive science; Field (mathematics); Psychology; Artificial intelligence; Medicine; Engineering; Library science; Neuroscience; Sociology; Social science; Nursing","score_opus":0.06607525783329601,"score_gpt":0.47831263032393706,"score_spread":0.4122373724906411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510747555","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":[0.00024658756,0.9374114,0.0097816875,0.028235571,0.003819469,0.000056204644,0.00004404709,0.00004933727,0.020355606],"genre_scores_gemma":[0.0056791776,0.93086165,0.015436258,0.029733678,0.0061489996,0.00032466842,0.00006996968,0.000037911042,0.011707736],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988071,0.0005975919,0.00012778322,0.000093260765,0.00031190325,0.000062341576],"domain_scores_gemma":[0.9953662,0.0037836567,0.00022054957,0.00012236838,0.00036610715,0.00014113921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023281358,0.0010699206,0.001514477,0.005582794,0.0014461549,0.0038893635,0.0016232537,0.005712688,0.0061922944],"category_scores_gemma":[0.004205432,0.0006267429,0.00091354724,0.0071383943,0.005102042,0.010450662,0.0019063152,0.008608138,0.0037326864],"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":[0.000039600247,0.00016724433,0.0003934009,0.0047798054,0.00005155024,0.0006617636,0.004147475,0.0006818972,0.0010305203,0.39406285,0.17989191,0.4140919],"study_design_scores_gemma":[0.0000060796488,0.00003709774,0.00048067505,0.0032832972,0.000008628975,0.0010611163,0.00058893114,0.0001425416,0.00008317724,0.11641846,0.8778729,0.00001717034],"about_ca_topic_score_codex":0.0016923646,"about_ca_topic_score_gemma":0.0035759758,"teacher_disagreement_score":0.0061922944,"about_ca_system_score_codex":0.0019341378,"about_ca_system_score_gemma":0.0022554316,"threshold_uncertainty_score":0.020715296},"labels":[],"label_agreement":null},{"id":"W1519605031","doi":"10.1197/jamia.m1662","title":"Participant Perceptions of the Influences of the NLM-Sponsored Woods Hole Medical Informatics Course","year":2005,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Informatics; Health informatics; Medical education; Perception; Qualitative research; Psychology; Knowledge management; Medicine; Computer science; Nursing; Engineering; Sociology","score_opus":0.03450785178222356,"score_gpt":0.42894698872609316,"score_spread":0.3944391369438696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519605031","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":[0.9982331,0.000021865868,0.000116256575,0.00016385442,0.000008308327,0.000035184086,0.000012127112,0.000005596597,0.0014037536],"genre_scores_gemma":[0.9979031,0.000054553526,0.0003360607,0.00013430647,0.000014519976,0.00011043808,0.000021236936,0.0000048155553,0.0014210003],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99447215,0.0033758543,0.00020897065,0.00028477985,0.0010204361,0.00063778815],"domain_scores_gemma":[0.97619075,0.014540628,0.002880412,0.00049750495,0.0023846864,0.0035060511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008325942,0.00023884143,0.00028841913,0.00046276656,0.0022503245,0.0018849506,0.00050052337,0.0006067305,0.003907098],"category_scores_gemma":[0.02280974,0.00020809172,0.0002322916,0.00027115332,0.0007951061,0.00075139623,0.0014392218,0.0008743035,0.00029443062],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024134323,0.005339534,0.33716673,0.00036787888,0.000051772997,0.0016906201,0.5238149,0.0004811941,0.021536764,0.0009251407,0.0024281163,0.10378391],"study_design_scores_gemma":[0.00020074123,0.009777895,0.45148706,0.0002118824,0.000075884185,0.00051735,0.51167107,0.0010917521,0.0062183444,0.00026326146,0.01835882,0.00012589296],"about_ca_topic_score_codex":0.0051807645,"about_ca_topic_score_gemma":0.009530994,"teacher_disagreement_score":0.008325942,"about_ca_system_score_codex":0.0012315424,"about_ca_system_score_gemma":0.0014830169,"threshold_uncertainty_score":0.044032335},"labels":[],"label_agreement":null},{"id":"W1533598587","doi":"10.1197/jamia.m2589","title":"Costs Associated with Developing and Implementing a Computerized Clinical Decision Support System for Medication Dosing for Patients with Renal Insufficiency in the Long-term Care Setting","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"Agency for Healthcare Research and Quality","keywords":"Dosing; Clinical decision support system; Medicine; Order entry; Informatics; Decision support system; Medical emergency; Intensive care medicine; Emergency medicine; Internal medicine; Computer science; Data mining","score_opus":0.030867382009863782,"score_gpt":0.4158677770021679,"score_spread":0.38500039499230415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533598587","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":[0.9849503,0.0005296603,0.004300169,0.0021669008,0.000086457076,0.00062505994,0.00040563205,0.00015942434,0.0067764586],"genre_scores_gemma":[0.990703,0.0002841096,0.007419374,0.00022396503,0.00003480521,0.00018151222,0.0002889756,0.000010901239,0.0008534131],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9943123,0.0033157188,0.0005350743,0.00018917485,0.001189266,0.00045850637],"domain_scores_gemma":[0.98098856,0.011614986,0.0026088473,0.000772045,0.0022411125,0.0017743891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044383244,0.0005101957,0.0002536536,0.0010888734,0.0013299595,0.0012783343,0.00067839056,0.000525205,0.0029797065],"category_scores_gemma":[0.02986056,0.00052102556,0.0007551102,0.0008440342,0.00050762884,0.0010269231,0.0015981026,0.0009723646,0.0003086257],"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":[0.002703857,0.0033529035,0.3471942,0.0004458278,0.0004940774,0.0014316441,0.0011650096,0.04544823,0.0021693078,0.0016041301,0.007553824,0.586437],"study_design_scores_gemma":[0.0012923997,0.011190772,0.80394197,0.00050644024,0.0011088648,0.0030288482,0.0052518193,0.15153624,0.0062568733,0.001994893,0.013633872,0.0002570046],"about_ca_topic_score_codex":0.01899843,"about_ca_topic_score_gemma":0.0277619,"teacher_disagreement_score":0.01899843,"about_ca_system_score_codex":0.0059957323,"about_ca_system_score_gemma":0.00611341,"threshold_uncertainty_score":0.04350233},"labels":[],"label_agreement":null},{"id":"W1543447953","doi":"10.1136/jamia.2002.0090409","title":"American College of Medical Informatics Fellows and International Associates, 2001","year":2002,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Informatics; Medical school; Research center; Political science; Medical education; Medicine; Computer science; Law","score_opus":0.01296708532550859,"score_gpt":0.26989050053070085,"score_spread":0.25692341520519224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543447953","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":[0.0015519311,0.03073068,0.010473489,0.08801862,0.034754008,0.0005507029,0.0032923387,0.0024185462,0.82820964],"genre_scores_gemma":[0.0056503178,0.026196163,0.011135588,0.0136490865,0.0053085564,0.0006131856,0.0037118027,0.000678898,0.9330564],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99747866,0.00045876787,0.00029255546,0.0003274326,0.0010604325,0.00038222416],"domain_scores_gemma":[0.9901928,0.0011941947,0.00050329184,0.0010052446,0.0039541903,0.0031502682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042916154,0.0010206748,0.0006399829,0.004501802,0.001984909,0.006458115,0.0015712649,0.0029543585,0.27921048],"category_scores_gemma":[0.014780442,0.0007097289,0.00048556132,0.005364675,0.0011090708,0.0063089575,0.0046289405,0.0044468152,0.23160441],"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":[0.0000118067555,0.000032375894,0.00034462518,0.000057451583,0.0000017247633,0.000038361883,0.0000743834,0.00001569167,0.00006211801,0.0035508543,0.86057156,0.13523914],"study_design_scores_gemma":[0.000003506794,0.0000047109256,0.00041510392,0.00010135422,0.0000011835255,0.00012822385,0.000063872176,0.000021442316,0.00002327355,0.0010881361,0.9981458,0.000003335673],"about_ca_topic_score_codex":0.0022062778,"about_ca_topic_score_gemma":0.0050288816,"teacher_disagreement_score":0.27921048,"about_ca_system_score_codex":0.001502478,"about_ca_system_score_gemma":0.0065391934,"threshold_uncertainty_score":0.9340521},"labels":[],"label_agreement":null},{"id":"W1571868071","doi":"10.1197/jamia.m2087","title":"Effectiveness of Clinician-selected Electronic Information Resources for Answering Primary Care Physicians' Information Needs","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Correctness; Observational study; Computer science; Information needs; Information retrieval; Primary care; Medical education; Think aloud protocol; Information seeking; Medicine; Psychology; Family medicine; World Wide Web; Usability; Pathology","score_opus":0.004191668197098514,"score_gpt":0.2848025016747033,"score_spread":0.28061083347760474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1571868071","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":[0.9928141,0.0017616939,0.000637442,0.00094903476,0.000041693223,0.0004173657,0.00013458109,0.00013782752,0.0031062698],"genre_scores_gemma":[0.99317825,0.0006879441,0.0049047745,0.00032197233,0.00006622709,0.0003742821,0.00013748508,0.000014731615,0.00031433933],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9675226,0.02491635,0.0019889423,0.0015308969,0.003320164,0.00072097604],"domain_scores_gemma":[0.8095351,0.15996376,0.018198814,0.003984648,0.0044526444,0.003865076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013871701,0.0006909318,0.0008161694,0.0010496686,0.0006445703,0.0015865624,0.0010448119,0.0015751775,0.0030026098],"category_scores_gemma":[0.20565322,0.00057806476,0.00055237755,0.00088328944,0.0006115417,0.00198691,0.0015029359,0.0006932363,0.00050333195],"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":[0.020355584,0.025635757,0.3338705,0.0053264596,0.000765637,0.0007350172,0.013053753,0.0032572951,0.0029427446,0.00018391627,0.0047833836,0.58909005],"study_design_scores_gemma":[0.009657908,0.0602654,0.8784315,0.003546164,0.0014776543,0.002383934,0.010873642,0.013408748,0.006154574,0.0006926795,0.012854628,0.000253123],"about_ca_topic_score_codex":0.0021731437,"about_ca_topic_score_gemma":0.0026512633,"teacher_disagreement_score":0.013871701,"about_ca_system_score_codex":0.0011492168,"about_ca_system_score_gemma":0.0022175543,"threshold_uncertainty_score":0.07336146},"labels":[],"label_agreement":null},{"id":"W1592467020","doi":"10.1136/jamia.2001.0080111","title":"Educational Instruction on a Hospital Information System for Medical Students During Their Surgical Rotations","year":2001,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Test (biology); Medicine; Chart; Order entry; Educational program; Medical education; Medical emergency; Mathematics; Statistics","score_opus":0.008216586643399162,"score_gpt":0.302879684939005,"score_spread":0.2946630982956059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592467020","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":[0.9974964,0.00067709334,0.0001301684,0.00013429232,0.00007011051,0.0009872204,0.00007595357,0.00001795566,0.00041075484],"genre_scores_gemma":[0.9944084,0.0011486503,0.0014267121,0.00019046327,0.00022642309,0.0017841931,0.00014400332,0.000003367538,0.00066779304],"study_design_codex":"randomized_trial","study_design_gemma":"observational","domain_scores_codex":[0.99814415,0.0008832677,0.00017344455,0.0002379601,0.0003164565,0.00024458946],"domain_scores_gemma":[0.9936907,0.0026362932,0.001934295,0.00016371581,0.00019740798,0.0013776772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023930683,0.00075103756,0.0010863919,0.0005776899,0.0005267785,0.00068959343,0.00082519377,0.001756054,0.0060871877],"category_scores_gemma":[0.006393032,0.00054650614,0.0007761602,0.00046244022,0.0006662745,0.00063008367,0.0005984768,0.0016553198,0.00047495295],"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":[0.49780858,0.24991193,0.035816055,0.0050518536,0.0011155236,0.00020615885,0.0007918818,0.000732182,0.0041169105,0.00017711616,0.0012400087,0.2030318],"study_design_scores_gemma":[0.25968465,0.6706732,0.06429241,0.00042662624,0.00071472547,0.00008983194,0.00020918048,0.00047384834,0.0021134198,0.000058561727,0.0012266673,0.000036826717],"about_ca_topic_score_codex":0.0010940613,"about_ca_topic_score_gemma":0.001930747,"teacher_disagreement_score":0.0060871877,"about_ca_system_score_codex":0.0005901887,"about_ca_system_score_gemma":0.0017852733,"threshold_uncertainty_score":0.020363688},"labels":[],"label_agreement":null},{"id":"W1765052210","doi":"10.1197/jamia.m1274","title":"Overcoming Structural Constraints to Patient Utilization of Electronic Medical Records: A Critical Review and Proposal for an Evaluation Framework","year":2003,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Medical record; Adaptation (eye); Perspective (graphical); Patient record; Electronic medical record; Computer science; Knowledge management; Medicine; Risk analysis (engineering); Medical emergency; Psychology; Artificial intelligence","score_opus":0.10370807416152567,"score_gpt":0.5223497322469337,"score_spread":0.41864165808540804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1765052210","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":[0.03289736,0.58605677,0.14837119,0.15470281,0.004791439,0.058733158,0.0006693105,0.00041843,0.0133594945],"genre_scores_gemma":[0.20779511,0.21936233,0.4927575,0.01404082,0.0010932878,0.06329733,0.00032438914,0.00007900395,0.0012502695],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.5781621,0.33155027,0.04340911,0.0041963095,0.040218133,0.0024640122],"domain_scores_gemma":[0.3363091,0.5143368,0.03393187,0.009315811,0.103773735,0.002332685],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.4295679,0.0020041792,0.006878466,0.021159174,0.0045070704,0.01146904,0.00623942,0.0049325335,0.0011032232],"category_scores_gemma":[0.49450392,0.0015101754,0.0041566743,0.017322905,0.008454428,0.010496478,0.0040337746,0.0027004762,0.00018623713],"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":[0.00061359414,0.00040490017,0.005560726,0.15748563,0.002308938,0.0007431939,0.014711325,0.0023660304,0.0011528346,0.03581503,0.013008868,0.7658289],"study_design_scores_gemma":[0.0018279506,0.0037458462,0.0152746625,0.61088157,0.019981185,0.0016010803,0.05436432,0.011238413,0.008223575,0.061988134,0.2099661,0.0009071616],"about_ca_topic_score_codex":0.011635001,"about_ca_topic_score_gemma":0.024138844,"teacher_disagreement_score":0.4295679,"about_ca_system_score_codex":0.026293797,"about_ca_system_score_gemma":0.10556583,"threshold_uncertainty_score":0.70344436},"labels":[],"label_agreement":null},{"id":"W1775135849","doi":"10.1136/amiajnl-2013-002411","title":"Learning regular expressions for clinical text classification","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; U.S. Department of Veterans Affairs","keywords":"Computer science; Natural language processing; Artificial intelligence","score_opus":0.025299992043553883,"score_gpt":0.3547212481779366,"score_spread":0.3294212561343827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1775135849","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":[0.070952654,0.0014846586,0.902138,0.0016403936,0.00022395323,0.00087296136,0.004749591,0.015126772,0.0028111238],"genre_scores_gemma":[0.29447773,0.00066969317,0.68861866,0.00057483965,0.00023966879,0.001267897,0.011699555,0.00068463007,0.0017672542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99001896,0.0031221323,0.0016074426,0.00252906,0.0023539183,0.0003684689],"domain_scores_gemma":[0.9704927,0.019257586,0.0037602137,0.00203507,0.0040272567,0.00042724077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007466298,0.0013325086,0.001081518,0.004541108,0.0007455472,0.002135276,0.0019356986,0.0010705058,0.0020822107],"category_scores_gemma":[0.03618214,0.0004904255,0.0011832893,0.0030309325,0.0014053476,0.0029954738,0.0015962526,0.0018366115,0.002428413],"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":[0.0009023743,0.00036133043,0.020750064,0.0011623712,0.00017728719,0.000973513,0.001138646,0.0262085,0.019842992,0.011828872,0.025094917,0.89155906],"study_design_scores_gemma":[0.0001625788,0.00042930365,0.0075912327,0.0004502051,0.0001764867,0.001376867,0.00076796825,0.85438204,0.04722799,0.058748517,0.028573854,0.00011301938],"about_ca_topic_score_codex":0.001858388,"about_ca_topic_score_gemma":0.0017158327,"teacher_disagreement_score":0.007466298,"about_ca_system_score_codex":0.001539932,"about_ca_system_score_gemma":0.002339389,"threshold_uncertainty_score":0.03948605},"labels":[],"label_agreement":null},{"id":"W1779982606","doi":"10.1197/jamia.m2996","title":"Towards Automatic Recognition of Scientifically Rigorous Clinical Research Evidence","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Concordia University","funders":"National Institutes of Health","keywords":"Computer science; Artificial intelligence; Machine learning; Support vector machine; Precision and recall; Gold standard (test); Naive Bayes classifier; Classifier (UML); Information retrieval; Pattern recognition (psychology); Medicine","score_opus":0.12454115338744977,"score_gpt":0.4284704772819773,"score_spread":0.30392932389452754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1779982606","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":[0.09524005,0.024859408,0.847002,0.0059807277,0.00078829593,0.0016232871,0.0049168332,0.012795648,0.006793719],"genre_scores_gemma":[0.16564658,0.0029309953,0.82451046,0.0005464619,0.00048158754,0.00037338072,0.004658719,0.00015700565,0.0006948614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.971629,0.010769818,0.0059169927,0.0032486527,0.0078020254,0.0006334544],"domain_scores_gemma":[0.8217102,0.105263054,0.020829711,0.011817852,0.03848493,0.0018941283],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.030663399,0.0013549016,0.002702199,0.029308757,0.0012528367,0.009236764,0.0024573992,0.002944698,0.0014683071],"category_scores_gemma":[0.12849982,0.000718268,0.0019127777,0.010323426,0.0011157724,0.005357355,0.0038116265,0.0029739975,0.002002604],"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":[0.00033022056,0.00028703795,0.0217776,0.0027257828,0.00035482607,0.00037194695,0.0006561795,0.004376606,0.021806492,0.00511411,0.012132443,0.93006665],"study_design_scores_gemma":[0.00035016454,0.0010590579,0.0631709,0.0037961875,0.0019094055,0.0031315575,0.0023245763,0.61117476,0.12961672,0.10229317,0.08073217,0.0004413623],"about_ca_topic_score_codex":0.002373386,"about_ca_topic_score_gemma":0.0045904135,"teacher_disagreement_score":0.9693366,"about_ca_system_score_codex":0.0016398436,"about_ca_system_score_gemma":0.008040714,"threshold_uncertainty_score":0.16216552},"labels":[],"label_agreement":null},{"id":"W178544901","doi":"10.1136/amiajnl-2011-000127","title":"Lessons from the Canadian national health information technology plan for the United States: opinions of key Canadian experts","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cancer Care Ontario; McGill University","funders":"Canadian Institutes of Health Research; McGill University","keywords":"Stakeholder; Leverage (statistics); Public relations; Incentive; Stakeholder engagement; Key (lock); Scale (ratio); Sample (material); Business; Knowledge management; Political science; Computer science; Economics","score_opus":0.0748189344356822,"score_gpt":0.40836979271949064,"score_spread":0.3335508582838084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W178544901","genre_codex":"commentary","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":[0.033422295,0.020509502,0.0019628035,0.85739964,0.002282536,0.00026361123,0.00093834015,0.00010056072,0.08312068],"genre_scores_gemma":[0.72528255,0.046681114,0.009984672,0.16898438,0.000730905,0.00023642882,0.0011135441,0.0002305024,0.046755917],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.9725099,0.005727543,0.001063008,0.0013720053,0.01330378,0.0060238037],"domain_scores_gemma":[0.95199764,0.012867941,0.0007626904,0.0005721858,0.022261819,0.011537663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027365116,0.00086787145,0.0006312311,0.0026710592,0.028909368,0.01413104,0.0036902556,0.0063327523,0.00537907],"category_scores_gemma":[0.043039206,0.00059557764,0.00076464895,0.005508556,0.008532604,0.0039893687,0.0048394855,0.010783205,0.0004418462],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00008009047,0.000090480615,0.012124232,0.0015801146,0.000057228528,0.0032984875,0.17779389,0.0017126547,0.0011965474,0.05516223,0.6195043,0.12739979],"study_design_scores_gemma":[0.0000142571635,0.000028181847,0.009234847,0.0020735261,0.000052688236,0.00032478938,0.20978501,0.0005211701,0.0005174443,0.00408452,0.773228,0.00013558165],"about_ca_topic_score_codex":0.98917633,"about_ca_topic_score_gemma":0.99194217,"teacher_disagreement_score":0.7946218,"about_ca_system_score_codex":0.20537819,"about_ca_system_score_gemma":0.45532355,"threshold_uncertainty_score":0.9216487},"labels":[],"label_agreement":null},{"id":"W1838551284","doi":"10.1136/amiajnl-2013-002214","title":"Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Topic Modeling","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"CONTEST; Computer science; Narrative; Natural language processing; Health records; Artificial intelligence; Task (project management); Interpretation (philosophy); Structuring; Information retrieval; Biomedical text mining; Quality (philosophy); Text mining; Linguistics; Programming language; Health care","score_opus":0.005966570501120155,"score_gpt":0.2444841220209323,"score_spread":0.23851755151981216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1838551284","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":[0.002969557,0.007898379,0.97111815,0.0026624014,0.0003081642,0.0006808842,0.0055123563,0.005968153,0.0028819975],"genre_scores_gemma":[0.014698185,0.006084793,0.9666205,0.00049179746,0.0003893248,0.0010231477,0.009229584,0.00033685155,0.0011258849],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99315417,0.0028414505,0.001171477,0.001002677,0.0017096766,0.00012045676],"domain_scores_gemma":[0.9807818,0.015633143,0.00095763017,0.0011254764,0.001337596,0.00016440953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009997256,0.0025283862,0.001969578,0.015740028,0.0013631015,0.0066200793,0.0029554204,0.0019230363,0.004546032],"category_scores_gemma":[0.02826357,0.000962064,0.003168588,0.012426809,0.0020284236,0.0081508495,0.0036797586,0.0030814428,0.004501986],"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":[0.00011605349,0.00022154932,0.0020939503,0.004864482,0.00038767632,0.00039502897,0.001068562,0.009240581,0.0053385445,0.04299458,0.04104234,0.89223665],"study_design_scores_gemma":[0.00013541988,0.0001813278,0.0042957803,0.0026654275,0.00040119144,0.0011608998,0.0016951403,0.24334776,0.013674315,0.51276857,0.21938379,0.0002903926],"about_ca_topic_score_codex":0.0039614085,"about_ca_topic_score_gemma":0.0045135194,"teacher_disagreement_score":0.015740028,"about_ca_system_score_codex":0.0016819183,"about_ca_system_score_gemma":0.003844746,"threshold_uncertainty_score":0.052871227},"labels":[],"label_agreement":null},{"id":"W1843376238","doi":"10.1093/jamia/ocv026","title":"Challenges to the implementation of a nationwide electronic prescribing network in primary care: a qualitative study of users’ perceptions","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Université de Montréal; McGill University","funders":"","keywords":"Medical prescription; Pharmacy; Medicine; Thematic analysis; Terminology; Family medicine; Electronic prescribing; Community pharmacy; Qualitative research; Nursing; Medical emergency","score_opus":0.06659403730766675,"score_gpt":0.474785807871131,"score_spread":0.40819177056346423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843376238","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":[0.9965905,0.00017169227,0.0002916309,0.001219891,0.000011419236,0.000063605985,0.00003773874,0.000005036388,0.001608604],"genre_scores_gemma":[0.9983706,0.00019626699,0.00027750287,0.0005072743,0.0000063592743,0.000041975898,0.000016371852,0.0000040912496,0.00057969184],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9914859,0.005381813,0.00031322698,0.0003897652,0.0010088522,0.0014204016],"domain_scores_gemma":[0.9780832,0.013280185,0.0021383467,0.00040498507,0.0029071365,0.0031861027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008974676,0.00022156189,0.00042629786,0.0006884998,0.0070130113,0.0036183782,0.0012292184,0.0013178707,0.0019101577],"category_scores_gemma":[0.016388252,0.00045636445,0.00025385682,0.001129818,0.0042148815,0.0019862272,0.0026265238,0.0014722105,0.00011947547],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003439334,0.000066636036,0.031902354,0.000120993216,0.000006917349,0.00070285704,0.959993,0.000044170676,0.0005777226,0.00027525917,0.00043536318,0.00584042],"study_design_scores_gemma":[0.0000052455516,0.00011531665,0.016509421,0.000119382275,0.0000060000584,0.00015865783,0.979087,0.00018387646,0.00011835782,0.000045830664,0.0036379464,0.000013007129],"about_ca_topic_score_codex":0.2640431,"about_ca_topic_score_gemma":0.3307693,"teacher_disagreement_score":0.2640431,"about_ca_system_score_codex":0.013066493,"about_ca_system_score_gemma":0.014261642,"threshold_uncertainty_score":0.5250124},"labels":[],"label_agreement":null},{"id":"W1867621500","doi":"10.1136/jamia.2009.000232","title":"The inadvertent disclosure of personal health information through peer-to-peer file sharing programs","year":2010,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Vanderbilt University","keywords":"Personally identifiable information; File sharing; Information sharing; Internet privacy; Computer science; Peer-to-peer; World Wide Web; Business; Computer security; The Internet","score_opus":0.024510732302132816,"score_gpt":0.40997415705435925,"score_spread":0.38546342475222645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867621500","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":[0.87753254,0.0030625954,0.03699982,0.015707571,0.00022572398,0.002253842,0.0034797308,0.0006059522,0.060132142],"genre_scores_gemma":[0.98300433,0.00078661146,0.010526672,0.0014081576,0.00016138365,0.00055264175,0.00045009522,0.000059822927,0.0030502754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8850646,0.07129154,0.0070745694,0.004234191,0.028948044,0.0033871043],"domain_scores_gemma":[0.5799311,0.26147246,0.08056298,0.0442699,0.02911258,0.004650948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049378287,0.00045060372,0.00044976882,0.0035563123,0.0025030798,0.0039326195,0.0023337156,0.001379831,0.0066775065],"category_scores_gemma":[0.28197455,0.000384696,0.00044584463,0.0033403072,0.0027181054,0.005595032,0.00534869,0.001294939,0.0010159255],"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":[0.0004761201,0.00038657125,0.5593402,0.0022794516,0.00030793174,0.00088033645,0.031513825,0.0013759712,0.001851819,0.011870599,0.016079191,0.37363803],"study_design_scores_gemma":[0.00021835387,0.0019590997,0.65466833,0.008579402,0.0008266951,0.014352932,0.05677704,0.021465663,0.018382534,0.01826515,0.20412974,0.00037499488],"about_ca_topic_score_codex":0.020176908,"about_ca_topic_score_gemma":0.01700501,"teacher_disagreement_score":0.049378287,"about_ca_system_score_codex":0.003840628,"about_ca_system_score_gemma":0.008811796,"threshold_uncertainty_score":0.26114047},"labels":[],"label_agreement":null},{"id":"W1870267357","doi":"10.1197/jamia.m1180","title":"Handheld Computing in Medicine","year":2003,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":315,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Mobile device; Confidentiality; Computer science; Medical literature; Multimedia; Medicine; World Wide Web; Computer security","score_opus":0.08914184005739356,"score_gpt":0.5211498568036638,"score_spread":0.4320080167462702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1870267357","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":[0.0002283422,0.99257797,0.00022120966,0.00083747075,0.0003754121,0.000017220113,0.000016157157,0.0000078364255,0.005718276],"genre_scores_gemma":[0.0037159414,0.9915011,0.0010739965,0.0012855023,0.0005072828,0.000029533765,0.000022791279,0.0000035442088,0.0018602553],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997009,0.0009976574,0.0004309385,0.00025055287,0.0012039694,0.00010787671],"domain_scores_gemma":[0.99362856,0.004467941,0.0006897523,0.00020210372,0.00081258576,0.0001990615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022124778,0.0005598144,0.0012022422,0.004452246,0.0006903368,0.0029292642,0.0006907587,0.0022011446,0.010281118],"category_scores_gemma":[0.0064880294,0.00030438282,0.00076791167,0.0066578975,0.0014830219,0.003128463,0.0012212192,0.0016820865,0.0026384548],"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":[0.000037449754,0.00005439136,0.0008560737,0.029313369,0.00013031183,0.00040112407,0.00045928417,0.0001869014,0.000549589,0.0095425,0.021904824,0.9365642],"study_design_scores_gemma":[0.000022062135,0.00012934032,0.005242443,0.03326296,0.00016529253,0.0036601871,0.00051603373,0.00006182801,0.00041282346,0.0076334924,0.94886047,0.000033115146],"about_ca_topic_score_codex":0.0036221226,"about_ca_topic_score_gemma":0.005881838,"teacher_disagreement_score":0.010281118,"about_ca_system_score_codex":0.0017751193,"about_ca_system_score_gemma":0.0046909926,"threshold_uncertainty_score":0.034393787},"labels":[],"label_agreement":null},{"id":"W1885248688","doi":"10.1197/jamia.m1653","title":"Validation of a Discharge Summary Term Search Method to Detect Adverse Events","year":2004,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Institute for Clinical Evaluative Sciences; University of British Columbia","funders":"Physicians' Services Incorporated Foundation","keywords":"Term (time); Computer science; Patient discharge; Artificial intelligence; MEDLINE; Chemistry","score_opus":0.035525034921842016,"score_gpt":0.43169225744244943,"score_spread":0.39616722252060743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885248688","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":[0.960262,0.001303769,0.030608917,0.00033354035,0.00019710924,0.0023159576,0.0026087502,0.00045517637,0.0019147228],"genre_scores_gemma":[0.9482699,0.00037621846,0.04580476,0.00020059291,0.0001712109,0.0009770046,0.0038272846,0.000050782593,0.00032219282],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.95746386,0.022391079,0.007547482,0.003591609,0.008441798,0.00056415173],"domain_scores_gemma":[0.72996867,0.18663937,0.022846851,0.011496849,0.04741429,0.0016339468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049732532,0.000844078,0.0008854922,0.005905831,0.0005685285,0.0019759838,0.0015357499,0.0014229578,0.0007642123],"category_scores_gemma":[0.19444537,0.00041554883,0.0011598616,0.0021147402,0.00077803887,0.0014852867,0.0016115062,0.00076567783,0.0005038896],"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":[0.0023826065,0.0007641603,0.9184283,0.000597552,0.00058230135,0.00019780805,0.0012052867,0.0018189076,0.0036390536,0.00026477594,0.0012268613,0.068892345],"study_design_scores_gemma":[0.001137158,0.008509244,0.905915,0.0004927112,0.00093324616,0.002098995,0.00078253576,0.058314174,0.01575166,0.0003903445,0.0054761223,0.00019883928],"about_ca_topic_score_codex":0.0029589464,"about_ca_topic_score_gemma":0.0026095058,"teacher_disagreement_score":0.049732532,"about_ca_system_score_codex":0.0010385009,"about_ca_system_score_gemma":0.002138601,"threshold_uncertainty_score":0.26301396},"labels":[],"label_agreement":null},{"id":"W1923038081","doi":"10.1197/jamia.m1712","title":"Patient-Perceived Usefulness of Online Electronic Medical Records: Employing Grounded Theory in the Development of Information and Communication Technologies for Use by Patients Living with Chronic Illness","year":2005,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":209,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Generalizability theory; Patient portal; Psychosocial; Grounded theory; Medicine; Qualitative research; Medical record; Telemedicine; Health care; Focus group; eHealth; Exploratory research; Nursing; Family medicine; Psychology; Psychiatry","score_opus":0.017837408103633285,"score_gpt":0.33301294397325837,"score_spread":0.3151755358696251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1923038081","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":[0.9484024,0.00085798465,0.033874635,0.00509227,0.000054166598,0.0018685888,0.00014967892,0.000023920527,0.009676327],"genre_scores_gemma":[0.9791742,0.0005021326,0.019138312,0.00038414347,0.00000792135,0.0005494764,0.00004247199,0.0000048392694,0.00019643869],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.96511966,0.03055706,0.00086111674,0.0005501338,0.0022094524,0.000702496],"domain_scores_gemma":[0.9311331,0.062611885,0.0022904656,0.0012754119,0.001963429,0.00072560395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03877924,0.0004488931,0.0004440084,0.0025453812,0.002726428,0.005066965,0.0013637254,0.0010364861,0.0010639243],"category_scores_gemma":[0.037803236,0.00031001144,0.00048311907,0.0017425972,0.007327185,0.004856048,0.003534946,0.0017816409,0.00009367236],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075262484,0.00047295965,0.02299913,0.0009881981,0.00003729855,0.00035209965,0.90006226,0.0005072319,0.0009893869,0.014484103,0.00049281813,0.058539204],"study_design_scores_gemma":[0.00009682623,0.00048663307,0.0129982615,0.0021959126,0.000074129865,0.00033821372,0.9570823,0.0034378825,0.0012508711,0.014807232,0.007181259,0.00005046283],"about_ca_topic_score_codex":0.0032660349,"about_ca_topic_score_gemma":0.006242149,"teacher_disagreement_score":0.03877924,"about_ca_system_score_codex":0.006385697,"about_ca_system_score_gemma":0.008630296,"threshold_uncertainty_score":0.20508671},"labels":[],"label_agreement":null},{"id":"W192740919","doi":"10.1197/jamia.m2507","title":"Emergency Physicians' Perceptions of Health Information Exchange","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"U.S. National Library of Medicine; Hort Innovation; Pfizer","keywords":"Health information exchange; Medical emergency; Medicine; Emergency department; Health care; Quarter (Canadian coin); Hyperlink; Health information; Family medicine; Web page; Nursing; Computer science; World Wide Web","score_opus":0.02192235455612944,"score_gpt":0.4154704650629017,"score_spread":0.39354811050677224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W192740919","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":[0.982437,0.00084614934,0.00042293128,0.007329855,0.00003845326,0.000037424757,0.000072169765,0.000015144162,0.00880105],"genre_scores_gemma":[0.9981762,0.0004320868,0.00021912082,0.0007552001,0.000028574394,0.000013643624,0.000027372593,0.0000024093038,0.0003453149],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99215406,0.0048379367,0.00069990626,0.00026986055,0.0014637935,0.00057440164],"domain_scores_gemma":[0.93794197,0.040969178,0.010545503,0.0009173506,0.0038860238,0.0057399953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008710178,0.00015988738,0.00022995265,0.00084875297,0.0011091331,0.002411785,0.00035198062,0.0011001099,0.007529749],"category_scores_gemma":[0.054405402,0.00019498653,0.00023643071,0.0005688572,0.0011434244,0.0018264924,0.0013888102,0.00085354043,0.0003980193],"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":[0.000913921,0.0010790537,0.6835263,0.0011042721,0.00019769432,0.0038118337,0.21583486,0.00083235995,0.002034289,0.0022415176,0.010314364,0.07810955],"study_design_scores_gemma":[0.00022652361,0.0015400762,0.5840604,0.0013179417,0.00010668635,0.0040188204,0.37345102,0.0013830232,0.0007696061,0.0016122821,0.031404912,0.00010871402],"about_ca_topic_score_codex":0.0026970827,"about_ca_topic_score_gemma":0.0017694193,"teacher_disagreement_score":0.008710178,"about_ca_system_score_codex":0.0012961507,"about_ca_system_score_gemma":0.0013585072,"threshold_uncertainty_score":0.046064377},"labels":[],"label_agreement":null},{"id":"W1932473789","doi":"10.1093/jamia/ocv052","title":"m-Health adoption by healthcare professionals: a systematic review","year":2015,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":675,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Health professionals; Health care; Systematic review; Medicine; MEDLINE; Nursing; Psychology; Political science","score_opus":0.055764428857324466,"score_gpt":0.5059281499775833,"score_spread":0.4501637211202588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1932473789","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":[0.0012148593,0.9969959,0.00024986285,0.00036007864,0.00012116918,0.00058682536,0.00014253547,0.0000074770896,0.00032133548],"genre_scores_gemma":[0.010835811,0.9863701,0.0012099211,0.00038470136,0.00006623892,0.0009182272,0.000117616655,0.000003666273,0.00009369244],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98568094,0.005391564,0.004665187,0.0008407561,0.0030199953,0.00040154415],"domain_scores_gemma":[0.951352,0.03615729,0.006518973,0.0006447936,0.0047779824,0.00054900785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014680323,0.0017095609,0.005942689,0.0142244585,0.0012020962,0.0031996504,0.0019023934,0.0025552877,0.0033203221],"category_scores_gemma":[0.06317407,0.0014560013,0.0057656975,0.01371868,0.0010800353,0.0036979124,0.0021996044,0.0015703428,0.00032042197],"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":[0.0000808605,0.00003164043,0.0007295773,0.93015754,0.0026702676,0.00010286207,0.0005381906,0.00006938867,0.00014630554,0.0002631404,0.0012883389,0.06392182],"study_design_scores_gemma":[0.000074026444,0.00016073618,0.002450469,0.962995,0.016572956,0.00029759374,0.0007074146,0.00006914706,0.0001633794,0.00019884929,0.01628123,0.00002914368],"about_ca_topic_score_codex":0.008567743,"about_ca_topic_score_gemma":0.028092137,"teacher_disagreement_score":0.014680323,"about_ca_system_score_codex":0.005027913,"about_ca_system_score_gemma":0.026368683,"threshold_uncertainty_score":0.07763791},"labels":[],"label_agreement":null},{"id":"W1945159329","doi":"10.1136/amiajnl-2012-001027","title":"Privacy-preserving heterogeneous health data sharing","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Center for Research Resources; U.S. National Library of Medicine; National Heart, Lung, and Blood Institute; Agency for Healthcare Research and Quality","keywords":"Differential privacy; Computer science; Data mining; Scalability; Data publishing; Information privacy; Discriminative model; Information sensitivity; Raw data; Private information retrieval; Privacy software; Probabilistic logic; Data anonymization; Machine learning; Computer security; Artificial intelligence; Database; Publishing","score_opus":0.05159888266659976,"score_gpt":0.3364032246551978,"score_spread":0.28480434198859805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1945159329","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":[0.031059684,0.00016978853,0.9652438,0.0009317691,0.00003857138,0.00016204703,0.0003014806,0.00028998914,0.0018028284],"genre_scores_gemma":[0.80324394,0.00025456073,0.19273527,0.0004897118,0.00014668942,0.00026794896,0.00072184915,0.00006995228,0.002070016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9855159,0.0055723446,0.0012637624,0.0027304674,0.0041777245,0.00073977635],"domain_scores_gemma":[0.9664403,0.011641379,0.002581973,0.01687644,0.0019781173,0.000481854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0116371,0.00058504037,0.0014715819,0.0010980833,0.0011524656,0.0030171545,0.0031811693,0.001495849,0.001728139],"category_scores_gemma":[0.041527115,0.00050534145,0.0015913692,0.0023786158,0.0019526955,0.0060549527,0.005889432,0.0017726228,0.00066697906],"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":[0.001822988,0.0005404782,0.020493643,0.0004232515,0.00049619045,0.0018153493,0.002310238,0.2378033,0.020460723,0.23255675,0.006992414,0.4742847],"study_design_scores_gemma":[0.00018644356,0.00035116382,0.0022305096,0.000082456405,0.00017721715,0.0023853227,0.00045605676,0.649974,0.040361185,0.29239044,0.011331536,0.0000736975],"about_ca_topic_score_codex":0.00043373258,"about_ca_topic_score_gemma":0.00025123684,"teacher_disagreement_score":0.0116371,"about_ca_system_score_codex":0.0011547056,"about_ca_system_score_gemma":0.0020140258,"threshold_uncertainty_score":0.061543643},"labels":[],"label_agreement":null},{"id":"W1951380831","doi":"10.1136/amiajnl-2013-002116","title":"ICD-10 codes used to identify adverse drug events in administrative data: a systematic review","year":2013,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; Vancouver Coastal Health Research Institute; University of British Columbia; Vancouver Coastal Health","funders":"","keywords":"Coding (social sciences); Medicine; Adverse effect; Diagnosis code; MEDLINE; Set (abstract data type); Computer science; Information retrieval; Data mining; Statistics; Pharmacology","score_opus":0.1934403541551048,"score_gpt":0.5454114426251365,"score_spread":0.3519710884700317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951380831","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":[0.002700051,0.990204,0.000833836,0.00087536604,0.00027213147,0.002532469,0.0020159618,0.00002026517,0.00054599077],"genre_scores_gemma":[0.04124942,0.9404615,0.0063380725,0.0015945489,0.00024351405,0.0080758305,0.0018461554,0.000021619142,0.00016933325],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9109506,0.033299405,0.03708651,0.0038812119,0.013604399,0.0011778624],"domain_scores_gemma":[0.68272334,0.25055397,0.043407228,0.00373903,0.018528286,0.0010480575],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05452134,0.0019424036,0.008491202,0.035172604,0.0013829594,0.0043609035,0.0030751256,0.0024936502,0.0035459895],"category_scores_gemma":[0.22532117,0.0014343262,0.0066052917,0.028740175,0.0019490914,0.004770097,0.0034068709,0.001772999,0.00045013704],"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":[0.000100014775,0.000016130747,0.0021513766,0.9685154,0.0037392187,0.00007947847,0.0004623258,0.000084111154,0.000085451684,0.00035195262,0.0015737948,0.022840742],"study_design_scores_gemma":[0.00013425454,0.000090954985,0.0051689055,0.9666666,0.015984623,0.00017710096,0.00057862554,0.00008844949,0.0001263627,0.00038072228,0.010566462,0.000036838333],"about_ca_topic_score_codex":0.0097979065,"about_ca_topic_score_gemma":0.026941115,"teacher_disagreement_score":0.9454787,"about_ca_system_score_codex":0.0094992975,"about_ca_system_score_gemma":0.030206336,"threshold_uncertainty_score":0.28833985},"labels":[],"label_agreement":null},{"id":"W1958649880","doi":"10.1136/amiajnl-2011-000221","title":"Point-of-care clinical documentation: assessment of a bladder cancer informatics tool (<i>eCancerCare<sup>Bladder</sup></i>): a randomized controlled study of efficacy, efficiency and user friendliness compared with standard electronic medical records","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's College Hospital; University Health Network; Princess Margaret Cancer Centre","funders":"","keywords":"Documentation; Medicine; Randomized controlled trial; Bladder cancer; Medical physics; Point of care; Informatics; Medical record; Health informatics; Cancer; Computer science; Internal medicine; Pathology","score_opus":0.02245119160366342,"score_gpt":0.42145241510177023,"score_spread":0.3990012234981068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958649880","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":[0.98879343,0.0016481908,0.0007904173,0.00023876198,0.00028306138,0.0068950504,0.0002970891,0.00007404745,0.0009800029],"genre_scores_gemma":[0.9787436,0.001081326,0.005215932,0.00032848972,0.00045901118,0.013154548,0.00026443618,0.00001553484,0.00073712954],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.98932296,0.0072723483,0.0010991469,0.0009752549,0.0009694453,0.00036084227],"domain_scores_gemma":[0.98136914,0.009375199,0.0057424353,0.00079500704,0.00080996845,0.0019082307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007793051,0.0013479936,0.0021106417,0.0011113399,0.00069031614,0.0014986394,0.0013663294,0.0024811046,0.006546822],"category_scores_gemma":[0.017153999,0.00081779447,0.0019359675,0.0007896037,0.0015298619,0.001798935,0.0008398752,0.0022035714,0.0004705435],"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":[0.88081545,0.068232104,0.003112382,0.0022071127,0.0012626473,0.000031099236,0.00021475712,0.00028202974,0.0014667275,0.00010559093,0.0003972367,0.041872762],"study_design_scores_gemma":[0.46227333,0.525259,0.009021257,0.00016793497,0.00087526295,0.0000303929,0.000111304784,0.00074629573,0.0009962483,0.00006587092,0.00041287398,0.000040199244],"about_ca_topic_score_codex":0.0009980501,"about_ca_topic_score_gemma":0.0013315204,"teacher_disagreement_score":0.007793051,"about_ca_system_score_codex":0.0012659475,"about_ca_system_score_gemma":0.0016181299,"threshold_uncertainty_score":0.04121411},"labels":[],"label_agreement":null},{"id":"W2095933166","doi":"10.1136/amiajnl-2012-000847","title":"Self-reported fever and measured temperature in emergency department records used for syndromic surveillance: Table 1","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Emergency department; Medicine; Health records; Influenza-like illness; Medical emergency; Medical record; Public health surveillance; Public health; Population; Complaint; Emergency medicine; Environmental health; Health care; Internal medicine; Virology; Psychiatry","score_opus":0.01056919686486339,"score_gpt":0.28550577147552386,"score_spread":0.27493657461066046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095933166","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":[0.6282553,0.003645091,0.0143054,0.00083775463,0.00025416518,0.0019005882,0.33246335,0.0007783449,0.017559975],"genre_scores_gemma":[0.8799317,0.0019847166,0.028838683,0.00039671213,0.00010814748,0.0011444234,0.08574467,0.00006348604,0.0017874335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99476445,0.0014708416,0.0018466194,0.0005551372,0.0012447753,0.00011814638],"domain_scores_gemma":[0.98329884,0.0066904845,0.005848676,0.0010167431,0.0028638747,0.0002813073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024351683,0.00037930752,0.00056542415,0.0033350235,0.00023525892,0.0011006288,0.00046044745,0.00022767766,0.00447831],"category_scores_gemma":[0.01517385,0.00015037184,0.0004938758,0.0042678756,0.00019576689,0.00055664784,0.000415699,0.00029580787,0.0008723634],"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":[0.00032331306,0.000075548945,0.94083023,0.0010394468,0.00030376448,0.00017749511,0.00027635976,0.00041185808,0.0013756243,0.00033288045,0.0055491882,0.049304258],"study_design_scores_gemma":[0.000042508,0.00030593565,0.9738628,0.00047754328,0.00023210164,0.0013533464,0.00050773827,0.0021472771,0.004070843,0.00024223344,0.01672039,0.00003714862],"about_ca_topic_score_codex":0.0031084158,"about_ca_topic_score_gemma":0.0039247395,"teacher_disagreement_score":0.00447831,"about_ca_system_score_codex":0.0004720496,"about_ca_system_score_gemma":0.0007045131,"threshold_uncertainty_score":0.014981449},"labels":[],"label_agreement":null},{"id":"W2096700916","doi":"10.1136/jamia.2010.006668","title":"Information needs of case managers caring for persons living with HIV","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Health Resources and Services Administration; National Institute of Nursing Research; U.S. National Library of Medicine; U.S. Public Health Service; NIH Clinical Center; National Institutes of Health","keywords":"Information needs; Context (archaeology); Referral; Needs assessment; Knowledge management; Information system; Medicine; Event (particle physics); Population; Data collection; Psychology; Computer science; Medical education; Nursing; World Wide Web; Environmental health; Sociology","score_opus":0.016756347303651335,"score_gpt":0.2940491370809771,"score_spread":0.27729278977732574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096700916","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":[0.99608314,0.00037041845,0.00024734985,0.0020089608,0.000013927229,0.000110126544,0.000058155885,0.00000769737,0.0011002755],"genre_scores_gemma":[0.99728084,0.0005406741,0.0011237311,0.0004803245,0.000025846346,0.00016092308,0.00008974871,0.0000022209415,0.0002957399],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9931253,0.0042599635,0.00069610367,0.00023698973,0.0010161835,0.0006654831],"domain_scores_gemma":[0.9669474,0.019194057,0.0085249785,0.00043693345,0.0024596523,0.0024370805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073846136,0.0002833223,0.0004437632,0.0017874466,0.0021073981,0.0017436792,0.00075514865,0.0010791498,0.0023384998],"category_scores_gemma":[0.055649504,0.00033436719,0.00035943429,0.00085046026,0.00061972847,0.002156639,0.0018142038,0.001030596,0.00018225449],"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":[0.000382027,0.00138336,0.6230927,0.000968549,0.000095238516,0.0041210465,0.26886067,0.00027415395,0.0009910769,0.0008675499,0.007007647,0.09195594],"study_design_scores_gemma":[0.00010283174,0.0014466261,0.30248567,0.0010752729,0.00009626297,0.006113962,0.6711164,0.0018621802,0.0007746112,0.0018036986,0.013017046,0.00010543852],"about_ca_topic_score_codex":0.0025851792,"about_ca_topic_score_gemma":0.0033767135,"teacher_disagreement_score":0.0073846136,"about_ca_system_score_codex":0.0019021019,"about_ca_system_score_gemma":0.0030290717,"threshold_uncertainty_score":0.039054036},"labels":[],"label_agreement":null},{"id":"W2097508746","doi":"10.1136/amiajnl-2012-001525","title":"An information-gain approach to detecting three-way epistatic interactions in genetic association studies","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"U.S. National Library of Medicine; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Eye Institute; National Institutes of Health","keywords":"Epistasis; Association (psychology); Genetic association; Computer science; Genome-wide association study; Information gain; Computational biology; Genetics; Artificial intelligence; Biology; Psychology; Gene; Genotype; Single-nucleotide polymorphism","score_opus":0.013705443031700312,"score_gpt":0.3037846948312116,"score_spread":0.29007925179951133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097508746","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":[0.027744852,0.0004111638,0.9705129,0.00024385797,0.00001901933,0.00013000055,0.0001674837,0.00027821452,0.00049248454],"genre_scores_gemma":[0.4345325,0.00027774938,0.5635739,0.00020552073,0.000092369824,0.000553781,0.00031587243,0.00006126398,0.00038704334],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9895415,0.006788198,0.0006243715,0.0010699619,0.00174958,0.00022641214],"domain_scores_gemma":[0.92634153,0.06523818,0.0025041373,0.0037213564,0.0015824107,0.00061227015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015820611,0.0010411836,0.0017789037,0.0074012345,0.0010018207,0.0017073164,0.0024452424,0.0016508948,0.001291717],"category_scores_gemma":[0.06301133,0.000527799,0.002128991,0.0036990813,0.00284721,0.001820061,0.0028517204,0.0021706047,0.00018130292],"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":[0.001305265,0.00083544204,0.0667722,0.0011243045,0.0026574652,0.0011294596,0.0008843327,0.3085346,0.02334998,0.12082224,0.0026661186,0.46991855],"study_design_scores_gemma":[0.00009845664,0.00043122374,0.015857695,0.00007285456,0.00027378113,0.0007193278,0.000105289626,0.8213945,0.004265642,0.15526213,0.0013910809,0.00012810368],"about_ca_topic_score_codex":0.0020774454,"about_ca_topic_score_gemma":0.0026512092,"teacher_disagreement_score":0.015820611,"about_ca_system_score_codex":0.0014179891,"about_ca_system_score_gemma":0.001735464,"threshold_uncertainty_score":0.08366835},"labels":[],"label_agreement":null},{"id":"W2098044946","doi":"10.1136/jamia.2001.0080527","title":"Clinical Decision Support Systems for the Practice of Evidence-based Medicine","year":2001,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":752,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"U.S. National Library of Medicine","keywords":"Clinical decision support system; Decision support system; Evidence-based medicine; Incentive; Implementation; Quality (philosophy); R-CAST; Health care; Evidence-based practice; Knowledge management; Computer science; Management science; Business decision mapping; Medicine; Alternative medicine; Artificial intelligence; Engineering; Political science","score_opus":0.16467382934029562,"score_gpt":0.5507247057078797,"score_spread":0.38605087636758406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098044946","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":[0.0025162064,0.015977418,0.81270796,0.123158745,0.004341487,0.0024834054,0.0015116782,0.0058390624,0.03146409],"genre_scores_gemma":[0.026823232,0.009279129,0.94924766,0.0059278146,0.0022745794,0.0024476978,0.0016412532,0.00032206692,0.002036512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.8689592,0.09016321,0.017964087,0.004124472,0.017488694,0.0013003383],"domain_scores_gemma":[0.5604598,0.32895193,0.01898328,0.036304772,0.04723045,0.008069703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.116435945,0.0018448374,0.0027196438,0.0110985795,0.0035889496,0.023391725,0.0059768385,0.010727898,0.025991473],"category_scores_gemma":[0.36397216,0.0015688468,0.0031222797,0.012020871,0.00749039,0.028183438,0.013444879,0.0127059845,0.011372211],"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":[0.00030329864,0.0002927783,0.0021625808,0.005273645,0.0003782888,0.000260739,0.0023613814,0.009092358,0.00089795666,0.46224287,0.08727775,0.42945626],"study_design_scores_gemma":[0.0003616193,0.00016780064,0.001032338,0.005632124,0.00017886644,0.0003030435,0.00074147194,0.020778202,0.0010608847,0.6362566,0.33327886,0.0002082815],"about_ca_topic_score_codex":0.0028167616,"about_ca_topic_score_gemma":0.0024377506,"teacher_disagreement_score":0.116435945,"about_ca_system_score_codex":0.0076267696,"about_ca_system_score_gemma":0.023081424,"threshold_uncertainty_score":0.6157795},"labels":[],"label_agreement":null},{"id":"W2098415242","doi":"10.1136/amiajnl-2011-000609","title":"The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Canadian Patient Safety Institute","keywords":"Drug; Medicine; Context (archaeology); Cluster (spacecraft); Medical emergency; Randomized controlled trial; Intensive care medicine; Emergency medicine; Pharmacology; Surgery; Computer science","score_opus":0.022688495998887344,"score_gpt":0.34875691195956277,"score_spread":0.32606841596067543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098415242","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":[0.9745705,0.0061701415,0.001365334,0.0013447711,0.0014155804,0.012284462,0.0008826321,0.00025268982,0.0017138025],"genre_scores_gemma":[0.9710288,0.0030342403,0.004511116,0.0012455378,0.0011983258,0.016312128,0.0005916183,0.000029616507,0.0020486298],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99663347,0.0018939156,0.0003333622,0.0005911661,0.00028262768,0.0002653864],"domain_scores_gemma":[0.9948757,0.0023956855,0.0009727625,0.00044595922,0.0004052874,0.00090464577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042385953,0.0019097151,0.004819586,0.001061843,0.00085143984,0.0015730964,0.0016280047,0.0034704534,0.0066176057],"category_scores_gemma":[0.0076785707,0.0010454805,0.0039196517,0.0008944883,0.0020176528,0.0018699854,0.00093148724,0.003449941,0.0007382027],"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":[0.98453486,0.006603704,0.0002853215,0.00059812906,0.00096297386,0.000011843988,0.000026324167,0.0002672529,0.0002208632,0.000051560964,0.00023906735,0.006198135],"study_design_scores_gemma":[0.9730252,0.025404805,0.00045866944,0.000037029906,0.00044647898,0.000005675661,0.000009925072,0.00037277082,0.00007407822,0.000069160655,0.00008773989,0.0000084047315],"about_ca_topic_score_codex":0.0037370196,"about_ca_topic_score_gemma":0.0031895149,"teacher_disagreement_score":0.0066176057,"about_ca_system_score_codex":0.0017771359,"about_ca_system_score_gemma":0.0022885033,"threshold_uncertainty_score":0.022416055},"labels":[],"label_agreement":null},{"id":"W2098465658","doi":"10.1136/amiajnl-2012-001072","title":"Direct comparison between support vector machine and multinomial naive Bayes algorithms for medical abstract classification","year":2012,"lang":"en","type":"letter","venue":"Journal of the American Medical Informatics Association","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Computer science; Machine learning; Naive Bayes classifier; Artificial intelligence; Algorithm; Bayesian probability; Relevance vector machine; Multinomial distribution; Task (project management); Bayes' theorem; Structured support vector machine; Data mining; Mathematics; Statistics; Engineering","score_opus":0.030097218431528877,"score_gpt":0.3180617413496284,"score_spread":0.2879645229180995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098465658","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":[0.09110195,0.082090095,0.7072254,0.06710758,0.011142186,0.0017631766,0.0031933521,0.0027668772,0.033609446],"genre_scores_gemma":[0.46459606,0.013810736,0.49321067,0.012894868,0.005379591,0.0017364316,0.002780863,0.00053383963,0.0050569875],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.92879695,0.045099284,0.005417681,0.0032291485,0.016885402,0.000571454],"domain_scores_gemma":[0.7060374,0.25307602,0.0040044216,0.0076330495,0.028101858,0.0011472369],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06655764,0.0010443098,0.0024658788,0.0053108446,0.0008261494,0.0039568483,0.0020317673,0.0036565633,0.007355015],"category_scores_gemma":[0.3191024,0.00047129317,0.0016146278,0.004039453,0.0008105437,0.0059969975,0.0014579333,0.003407169,0.0041987575],"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":[0.0050361394,0.00030956205,0.0075112507,0.0014147471,0.00079980714,0.00008103134,0.00021315715,0.005624972,0.00074501825,0.012785972,0.026236525,0.9392419],"study_design_scores_gemma":[0.0031791432,0.0059627243,0.023316428,0.0035736603,0.0011641145,0.0031829749,0.00064148195,0.66128117,0.007383578,0.21881716,0.070950754,0.0005468128],"about_ca_topic_score_codex":0.0017490983,"about_ca_topic_score_gemma":0.0027148924,"teacher_disagreement_score":0.93344235,"about_ca_system_score_codex":0.0026928305,"about_ca_system_score_gemma":0.00229458,"threshold_uncertainty_score":0.35199463},"labels":[],"label_agreement":null},{"id":"W2099846354","doi":"10.1136/jamia.2010.004838","title":"A review on systematic reviews of health information system studies","year":2010,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":195,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Ottawa; University of Victoria","funders":"Canadian Institutes of Health Research","keywords":"Guideline; Health care; Systematic review; Incentive; Legislation; Quality (philosophy); MEDLINE; Medicine; Nursing; Knowledge management; Psychology; Computer science; Political science","score_opus":0.14672886708298505,"score_gpt":0.5273676316765108,"score_spread":0.38063876459352575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099846354","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":[0.0003648768,0.99688303,0.00049115886,0.0005456467,0.00026438318,0.0005780533,0.00043422647,0.00001514262,0.00042348373],"genre_scores_gemma":[0.006559287,0.98756844,0.0027894883,0.0008864399,0.00014823304,0.0015373233,0.0003521048,0.0000109494495,0.00014764823],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.94762427,0.021151215,0.020013144,0.0020842026,0.00833483,0.0007923652],"domain_scores_gemma":[0.8807536,0.08876452,0.017222133,0.0019182931,0.010629376,0.00071202335],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.030272324,0.0028771944,0.010991218,0.024065297,0.0012607877,0.004239654,0.0029461165,0.0028627152,0.0073159775],"category_scores_gemma":[0.159203,0.002030641,0.007903032,0.025697114,0.0016951229,0.004825635,0.0028214173,0.002149517,0.00080332847],"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":[0.00013862614,0.000014677328,0.0003472411,0.93311214,0.0054908786,0.00009096243,0.00020666112,0.000082313396,0.00017035102,0.00045606063,0.0033022687,0.056587756],"study_design_scores_gemma":[0.00019169535,0.00013949086,0.0021734177,0.92521906,0.03679541,0.0002763985,0.0002219328,0.000052642576,0.00019104492,0.0006328141,0.034067962,0.00003815989],"about_ca_topic_score_codex":0.008647136,"about_ca_topic_score_gemma":0.027153943,"teacher_disagreement_score":0.9697277,"about_ca_system_score_codex":0.00761541,"about_ca_system_score_gemma":0.021317992,"threshold_uncertainty_score":0.16009724},"labels":[],"label_agreement":null},{"id":"W2100063939","doi":"10.1136/amiajnl-2013-002068","title":"A personally controlled electronic health record for Australia","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Island Health","funders":"Australian Government","keywords":"Patient portal; eHealth; General partnership; Business; Health care; Stakeholder; Public relations; Internet privacy; Electronic health record; Psychological intervention; Medicine; Knowledge management; Nursing; Computer science; Political science","score_opus":0.03097630299521433,"score_gpt":0.42938443250955316,"score_spread":0.39840812951433885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100063939","genre_codex":"other","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":[0.19185512,0.010478186,0.20507489,0.07447543,0.003565748,0.011790198,0.013593511,0.023632627,0.4655343],"genre_scores_gemma":[0.3626194,0.005548647,0.25566062,0.008255881,0.0010188451,0.002659315,0.007090949,0.0011551736,0.35599115],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99585104,0.0014024696,0.000520792,0.00041975614,0.0015727209,0.00023315205],"domain_scores_gemma":[0.9831746,0.0023211406,0.0015498159,0.0027258152,0.0074787014,0.002749919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067249415,0.00024896485,0.00024389863,0.0014903289,0.001523198,0.002454738,0.0010463418,0.0010450364,0.034763936],"category_scores_gemma":[0.017917294,0.00031891765,0.00026215083,0.0015560056,0.0007337896,0.0033457011,0.003401143,0.0014892138,0.012996014],"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":[0.00033920046,0.00044085894,0.012298267,0.00095113344,0.000018119414,0.00042347546,0.0042665177,0.00021006512,0.010763614,0.012395675,0.12069394,0.83719915],"study_design_scores_gemma":[0.00008549949,0.00062640815,0.03668109,0.0005745983,0.000028765202,0.0010459189,0.0010998272,0.0014703461,0.0049417308,0.0021140056,0.9512669,0.00006482363],"about_ca_topic_score_codex":0.012277548,"about_ca_topic_score_gemma":0.013604746,"teacher_disagreement_score":0.034763936,"about_ca_system_score_codex":0.0024909133,"about_ca_system_score_gemma":0.012080298,"threshold_uncertainty_score":0.11629695},"labels":[],"label_agreement":null},{"id":"W2101055653","doi":"10.1197/jamia.m2974","title":"Standardizing Nursing Information in Canada for Inclusion in Electronic Health Records: C-HOBIC","year":2009,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Golder Associates (Canada); Ministry of Health and Long Term Care; Canadian Nurses Association","funders":"","keywords":"Terminology; Nursing Minimum Data Set; Interoperability; Nursing Outcomes Classification; Nursing; Inclusion (mineral); Health care; Health informatics; Medicine; Nursing care; Health records; Electronic health record; Nursing research; Team nursing; Psychology; Computer science; Political science; Public health","score_opus":0.0044871947452658064,"score_gpt":0.300779868756655,"score_spread":0.2962926740113892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101055653","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":[0.33220762,0.02026148,0.24900733,0.078896716,0.0020674267,0.014227135,0.06537497,0.006949193,0.23100805],"genre_scores_gemma":[0.59116167,0.009848752,0.33554578,0.008017241,0.00024631227,0.0028696335,0.032124907,0.0006276343,0.01955808],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97683775,0.003450409,0.0022458034,0.0014863301,0.01431524,0.0016644155],"domain_scores_gemma":[0.941923,0.004298198,0.0029230965,0.0052055535,0.043288328,0.0023617297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018052634,0.0005174512,0.00051929173,0.008918192,0.0059060217,0.0067694625,0.0030119696,0.00078984717,0.0023125536],"category_scores_gemma":[0.05098635,0.00053571264,0.00096038583,0.021003725,0.002308859,0.0024172831,0.005403529,0.0012633048,0.00060063915],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025871015,0.0002554421,0.09920214,0.0021255694,0.00013160474,0.00025106152,0.009194579,0.0036605312,0.002506913,0.03454884,0.11319801,0.7346666],"study_design_scores_gemma":[0.00024485172,0.0002278572,0.44738147,0.0033730313,0.00023969947,0.0004107708,0.01088123,0.0114634,0.0063302633,0.009433236,0.5095701,0.0004440619],"about_ca_topic_score_codex":0.98703647,"about_ca_topic_score_gemma":0.9874117,"teacher_disagreement_score":0.98703647,"about_ca_system_score_codex":0.073746115,"about_ca_system_score_gemma":0.27705032,"threshold_uncertainty_score":0.53506804},"labels":[],"label_agreement":null},{"id":"W2101984653","doi":"10.1197/jamia.m2518","title":"Methodologic Issues in Health Informatics Trials: The Complexities of Complex Interventions","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care","keywords":"Psychological intervention; Informatics; Health informatics; Health Administration Informatics; Randomized controlled trial; Key (lock); Health information technology; Medicine; Public health informatics; MEDLINE; Translational research informatics; Clinical trial; Data science; Computer science; Health care; Nursing; Health policy; Public health; HRHIS; Pathology; Engineering; Political science","score_opus":0.3760758682427842,"score_gpt":0.5684856620130346,"score_spread":0.19240979377025041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101984653","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":[0.005297171,0.036419854,0.79360723,0.09908422,0.017317124,0.04009487,0.00071663305,0.00107359,0.0063892636],"genre_scores_gemma":[0.094275706,0.0061860434,0.74633884,0.032055195,0.006207638,0.11337654,0.00021760153,0.00035983612,0.0009826261],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.02831746,0.89789736,0.04304558,0.0049073454,0.02519507,0.0006372484],"domain_scores_gemma":[0.016136602,0.9311346,0.016630981,0.024675878,0.010773484,0.00064858416],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.9025956,0.0051817996,0.01117692,0.015361957,0.0054446585,0.016755918,0.012073772,0.014802595,0.003583989],"category_scores_gemma":[0.9502506,0.0044423966,0.006793531,0.0152197145,0.04301432,0.019649964,0.008968333,0.017933168,0.0013801346],"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":[0.006218129,0.00085266755,0.015260115,0.05834034,0.009742893,0.0017596511,0.015979646,0.012666668,0.0009016874,0.38717318,0.0627001,0.42840493],"study_design_scores_gemma":[0.006763964,0.0030082797,0.007102659,0.06575934,0.00460055,0.0020097704,0.004454229,0.04452753,0.0028799078,0.7449631,0.11288182,0.0010487689],"about_ca_topic_score_codex":0.0041881204,"about_ca_topic_score_gemma":0.0045278217,"teacher_disagreement_score":0.09740442,"about_ca_system_score_codex":0.015479641,"about_ca_system_score_gemma":0.032113332,"threshold_uncertainty_score":0.12011707},"labels":[],"label_agreement":null},{"id":"W2102728199","doi":"10.1136/amiajnl-2011-000310","title":"The economics of health information technology in medication management: a systematic review of economic evaluations","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton; Centre for Advancing Health Outcomes; Programs for Assessment of Technology in Health Research Institute","funders":"","keywords":"Systematic review; Health information technology; Process (computing); Health technology; Health economics; Information technology; Knowledge management; MEDLINE; Management science; Computer science; Business; Health care; Medicine; Economics; Political science; Economic growth","score_opus":0.042851043687447556,"score_gpt":0.4598478972988712,"score_spread":0.41699685361142363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102728199","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":[0.0011293857,0.9958905,0.00044487498,0.00085583475,0.0002065078,0.00074537005,0.00025377842,0.000007926935,0.00046579968],"genre_scores_gemma":[0.029523376,0.96428704,0.0029035239,0.0009076291,0.00025962477,0.0017614466,0.00022526774,0.000011036951,0.00012107422],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9212279,0.044288114,0.017778318,0.0017806629,0.013746794,0.0011782305],"domain_scores_gemma":[0.782699,0.174618,0.027634623,0.001968062,0.012149181,0.0009311374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.055260032,0.0025351741,0.01158477,0.021810979,0.0012390664,0.0065093953,0.002274562,0.0032340104,0.004961685],"category_scores_gemma":[0.21156414,0.0017760763,0.0124091385,0.015998589,0.0017706673,0.005184939,0.002613137,0.0029163016,0.0003169517],"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":[0.00059573044,0.000074139745,0.0011114171,0.90525824,0.019112302,0.00010176226,0.00017289324,0.00044468325,0.00009461489,0.0011268343,0.001964784,0.069942534],"study_design_scores_gemma":[0.00047314313,0.00033488232,0.0026850272,0.9357869,0.04888452,0.00022316888,0.0002361239,0.0002970463,0.00015477634,0.0010514903,0.009830072,0.00004283735],"about_ca_topic_score_codex":0.006240981,"about_ca_topic_score_gemma":0.015321513,"teacher_disagreement_score":0.055260032,"about_ca_system_score_codex":0.012903899,"about_ca_system_score_gemma":0.024275757,"threshold_uncertainty_score":0.29224646},"labels":[],"label_agreement":null},{"id":"W2104293429","doi":"10.1136/amiajnl-2013-001705","title":"Health information technologies in geriatrics and gerontology: a mixed systematic review","year":2013,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Jewish General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Geriatrics; Health information technology; MEDLINE; Categorization; Health care; Knowledge management; Medicine; Gerontology; Computer science; Psychology; Medical education","score_opus":0.02318686354849357,"score_gpt":0.35141724229392635,"score_spread":0.32823037874543276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104293429","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":[0.0019617262,0.9952004,0.00048017825,0.00059038243,0.00019359897,0.0008870537,0.00033406302,0.000008339713,0.00034418548],"genre_scores_gemma":[0.030877488,0.9603008,0.0038685687,0.0012026147,0.00019221395,0.003059998,0.0003425898,0.00000772639,0.00014793556],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9693876,0.013179627,0.010874935,0.0013285598,0.004672075,0.0005572649],"domain_scores_gemma":[0.8899025,0.08888687,0.011969419,0.0015935049,0.006893152,0.0007545344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030642347,0.0016564825,0.0067891153,0.022205878,0.001214834,0.0047794776,0.002127873,0.0028485223,0.0030335824],"category_scores_gemma":[0.1113047,0.0011129254,0.0053537535,0.021349218,0.001219507,0.004097681,0.002589751,0.0011008861,0.00030764742],"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":[0.00012977513,0.000031332405,0.001106345,0.94322807,0.0075428593,0.00011083869,0.00065440283,0.0000769063,0.00016737675,0.00047742995,0.0011355631,0.045339093],"study_design_scores_gemma":[0.00012272377,0.00022980529,0.003105619,0.9515266,0.031863064,0.0002657883,0.0009139555,0.000088802124,0.00017116046,0.00043705685,0.011248252,0.000027245262],"about_ca_topic_score_codex":0.004520522,"about_ca_topic_score_gemma":0.016685618,"teacher_disagreement_score":0.030642347,"about_ca_system_score_codex":0.005590651,"about_ca_system_score_gemma":0.021431915,"threshold_uncertainty_score":0.16205412},"labels":[],"label_agreement":null},{"id":"W2104381725","doi":"10.1136/amiajnl-2011-000150","title":"Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":240,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Narrative; Benchmark (surveying); Process (computing); Computer science; State (computer science); Data science; Health care; Artificial intelligence; Natural language processing; Political science; Art; Cartography; Literature; Geography; Law","score_opus":0.046539093348759476,"score_gpt":0.34087169057266703,"score_spread":0.29433259722390753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104381725","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":[0.19018315,0.03479063,0.6749485,0.019621221,0.0014087961,0.002710633,0.013085633,0.044424854,0.018826598],"genre_scores_gemma":[0.23016682,0.005052824,0.7174981,0.0020877596,0.0007114905,0.0014008844,0.034645878,0.0012498938,0.0071863723],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9848445,0.00519685,0.0016720366,0.0036379383,0.0039423294,0.0007063025],"domain_scores_gemma":[0.97694415,0.014527619,0.0010612806,0.002330513,0.004370293,0.0007660954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015533911,0.0034274175,0.0018704507,0.004682809,0.0019535907,0.006802565,0.0047907205,0.005742455,0.0048036077],"category_scores_gemma":[0.03633727,0.0010554177,0.0019131855,0.0039096596,0.0011221156,0.006749342,0.0035875633,0.004488295,0.005077991],"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":[0.0009490907,0.0013179533,0.005643842,0.0022189417,0.00036684415,0.00024794755,0.0009187355,0.029674245,0.011834791,0.0025725132,0.054554597,0.8897006],"study_design_scores_gemma":[0.0007935545,0.0013147857,0.01067849,0.000898675,0.00040829094,0.0006474768,0.0016560783,0.83718896,0.048811257,0.022681676,0.07457717,0.00034370366],"about_ca_topic_score_codex":0.011820421,"about_ca_topic_score_gemma":0.013195903,"teacher_disagreement_score":0.015533911,"about_ca_system_score_codex":0.0034478467,"about_ca_system_score_gemma":0.0051366542,"threshold_uncertainty_score":0.08215213},"labels":[],"label_agreement":null},{"id":"W2104990432","doi":"10.1136/amiajnl-2011-000523","title":"The National Center for Biomedical Ontology","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":280,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Common Fund; National Institutes of Health","keywords":"Open Biomedical Ontologies; Ontology; Computer science; Biomedicine; Variety (cybernetics); World Wide Web; Data science; Semantic Web; Process ontology; Resource (disambiguation); Upper ontology; Ontology-based data integration; Analytics; Ontology alignment; Bioinformatics; Artificial intelligence","score_opus":0.021415495158009754,"score_gpt":0.3000184411159791,"score_spread":0.2786029459579693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104990432","genre_codex":"other","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":[0.0017561116,0.020953612,0.17802796,0.102275245,0.013062319,0.0034147848,0.09079521,0.028219687,0.5614951],"genre_scores_gemma":[0.020236947,0.035738207,0.41962355,0.04490783,0.004230756,0.008361154,0.292733,0.0057479045,0.16842054],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9829101,0.0039202403,0.0027429045,0.0021229954,0.0073265973,0.00097714],"domain_scores_gemma":[0.9584239,0.008738348,0.0026824407,0.011356166,0.013956937,0.004842274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020001736,0.0017921388,0.0022996562,0.009248925,0.004248622,0.013683201,0.005505059,0.0046647196,0.087827265],"category_scores_gemma":[0.061978642,0.0013212564,0.0019993703,0.010141381,0.0030715128,0.011648498,0.011863334,0.0074638287,0.0842035],"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":[0.00009995461,0.00007831026,0.0009369799,0.0009280579,0.00005921494,0.00015173719,0.00030747126,0.00025307303,0.00050749915,0.15432602,0.6974273,0.14492442],"study_design_scores_gemma":[0.000026624464,0.000012383568,0.00047437422,0.0005185497,0.000020525424,0.000108883,0.000086722124,0.00032626314,0.00015435008,0.032041393,0.9662043,0.000025613605],"about_ca_topic_score_codex":0.016716087,"about_ca_topic_score_gemma":0.010712823,"teacher_disagreement_score":0.087827265,"about_ca_system_score_codex":0.0056849313,"about_ca_system_score_gemma":0.037008505,"threshold_uncertainty_score":0.2938115},"labels":[],"label_agreement":null},{"id":"W2106431497","doi":"10.1136/amiajnl-2010-000026","title":"Does user-centred design affect the efficiency, usability and safety of CPOE order sets?","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University Health Network; Canadian Patient Safety Institute; Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Usability; Computer science; Task (project management); Test (biology); Patient safety; Set (abstract data type); Computerized physician order entry; Human–computer interaction; Health care; Engineering","score_opus":0.04065528224924532,"score_gpt":0.3706546977971099,"score_spread":0.3299994155478646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106431497","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":[0.9757751,0.0013713881,0.015420889,0.0010653588,0.00013997326,0.0009761467,0.00011845381,0.00021650434,0.0049162023],"genre_scores_gemma":[0.97784436,0.0004577156,0.020225415,0.00033732984,0.000055107117,0.00047640028,0.00007018981,0.00005600675,0.0004774111],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97614497,0.015884427,0.0023208214,0.00093763176,0.004125196,0.00058698806],"domain_scores_gemma":[0.8111764,0.1466722,0.016510533,0.0066062566,0.01570564,0.003328929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023597509,0.00039845667,0.00048575347,0.00064242916,0.0005290073,0.002971626,0.00075401174,0.0007409952,0.0031099764],"category_scores_gemma":[0.13232341,0.0003652105,0.0007167387,0.00049823656,0.0010491692,0.0016612388,0.00064385094,0.0005086792,0.0005876351],"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":[0.0077921357,0.0047552786,0.46820375,0.0048744995,0.00072989747,0.00038736738,0.020438267,0.0024441183,0.017097885,0.0009967072,0.0048573245,0.46742278],"study_design_scores_gemma":[0.002322511,0.024830759,0.90200484,0.0019250152,0.0010280974,0.0018698556,0.010684136,0.01059792,0.021572765,0.0031628408,0.019539166,0.0004622139],"about_ca_topic_score_codex":0.0012271303,"about_ca_topic_score_gemma":0.0017516969,"teacher_disagreement_score":0.023597509,"about_ca_system_score_codex":0.0011512262,"about_ca_system_score_gemma":0.0019774456,"threshold_uncertainty_score":0.124797046},"labels":[],"label_agreement":null},{"id":"W2107042346","doi":"10.1136/jamia.2000.0070569","title":"Impact of a Computer-based Patient Record System on Data Collection, Knowledge Organization, and Reasoning","year":2000,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":232,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Data collection; Data science; Patient record; Artificial intelligence; Information retrieval; Medical emergency; Medicine","score_opus":0.022064871797978707,"score_gpt":0.3816029858997074,"score_spread":0.3595381141017287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107042346","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":[0.9969483,0.0003155807,0.00071866676,0.00042960208,0.000019738829,0.00011775094,0.000045385343,0.00006742543,0.0013375494],"genre_scores_gemma":[0.9965029,0.00013729408,0.0027796868,0.00017119406,0.000022724482,0.0000960996,0.000055114982,0.000008974444,0.00022620903],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9809378,0.01365377,0.001179256,0.0008615428,0.0027108276,0.00065680285],"domain_scores_gemma":[0.71423846,0.2554562,0.015727013,0.005311455,0.0046434905,0.004623328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010825161,0.00038108823,0.00028597398,0.0007827832,0.0010334048,0.0022500877,0.0007335117,0.00089402305,0.003095019],"category_scores_gemma":[0.13168414,0.0003437895,0.00060631434,0.0006155138,0.0009737066,0.0014123505,0.0013607726,0.000909897,0.00025490747],"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":[0.017206172,0.015645752,0.46858254,0.0019582582,0.000813805,0.0011996374,0.019796204,0.005114922,0.024541946,0.00062304275,0.0018105219,0.44270718],"study_design_scores_gemma":[0.000905237,0.019742936,0.95141435,0.00050680933,0.0006159272,0.0009918463,0.0057953536,0.005914424,0.009115828,0.00050117454,0.004352348,0.00014374133],"about_ca_topic_score_codex":0.0028354053,"about_ca_topic_score_gemma":0.0026587409,"teacher_disagreement_score":0.010825161,"about_ca_system_score_codex":0.001649195,"about_ca_system_score_gemma":0.00201222,"threshold_uncertainty_score":0.057249606},"labels":[],"label_agreement":null},{"id":"W2107057736","doi":"10.1197/jamia.m3107","title":"Overcoming Barriers to the Implementation of a Pharmacy Bar Code Scanning System for Medication Dispensing: A Case Study","year":2009,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agency for Healthcare Research and Quality","keywords":"Vendor; Workflow; Pharmacy; Process (computing); Adaptation (eye); Medicine; Resistance (ecology); Health information technology; Computer science; Medical education; Process management; Knowledge management; Nursing; Business; Health care; Psychology","score_opus":0.04072276228445293,"score_gpt":0.47971854580988166,"score_spread":0.43899578352542873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107057736","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":[0.9896936,0.00031999164,0.0041373586,0.0029498923,0.000041028827,0.00025813028,0.00002001795,0.000030976622,0.0025490278],"genre_scores_gemma":[0.98414695,0.0013212714,0.010418888,0.0013243603,0.000061041595,0.0003196271,0.000030752064,0.00004177437,0.0023354068],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.97784036,0.015204268,0.00089722674,0.00068849965,0.0024942998,0.0028752808],"domain_scores_gemma":[0.94829786,0.03631296,0.005437245,0.0016272906,0.0039288513,0.0043958286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01339762,0.0012858338,0.00074636855,0.0020007438,0.011512679,0.003143036,0.0036114412,0.006291258,0.0016445193],"category_scores_gemma":[0.044529118,0.0015329724,0.00095037447,0.001709976,0.0030550417,0.0038829495,0.003101007,0.0047405316,0.0003741049],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004235197,0.0077469475,0.0618758,0.0014908164,0.00011542917,0.100040115,0.7283125,0.0015585949,0.0059537333,0.005034405,0.0045416215,0.08290651],"study_design_scores_gemma":[0.00015752064,0.0052557127,0.03521508,0.0012989673,0.00023846366,0.072889686,0.8312472,0.0065925904,0.011315621,0.0019830475,0.033463944,0.00034208267],"about_ca_topic_score_codex":0.010349112,"about_ca_topic_score_gemma":0.019582443,"teacher_disagreement_score":0.01339762,"about_ca_system_score_codex":0.0050055133,"about_ca_system_score_gemma":0.00675182,"threshold_uncertainty_score":0.07085425},"labels":[],"label_agreement":null},{"id":"W2107450547","doi":"10.1197/jamia.m2457","title":"Alternatives to Project-specific Consent for Access to Personal Information for Health Research: What Is the Opinion of the Canadian Public?","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; York University","funders":"","keywords":"Confidentiality; Informed consent; Public health; Internet privacy; Medical record; Health care; Personally identifiable information; Medical education; Medicine; Family medicine; Psychology; Public relations; Nursing; Political science; Alternative medicine; Computer science; Computer security","score_opus":0.641072614742122,"score_gpt":0.636279858626963,"score_spread":0.004792756115158947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107450547","genre_codex":"commentary","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":[0.28829774,0.018741427,0.0021295324,0.6494894,0.0008616722,0.00028875287,0.0006494642,0.000045576067,0.039496396],"genre_scores_gemma":[0.9439117,0.0075916923,0.00190686,0.044085052,0.0002468751,0.0000866281,0.00018747554,0.000018943803,0.0019647446],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9236765,0.026655575,0.0041980944,0.003054333,0.03197288,0.010442666],"domain_scores_gemma":[0.7735445,0.09944866,0.020553712,0.0063414,0.08243047,0.017681321],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09014773,0.00035225673,0.00074055255,0.002891816,0.012993416,0.011382085,0.002852404,0.0058603887,0.0034902506],"category_scores_gemma":[0.17763339,0.0007068932,0.0009407426,0.0048443475,0.019278016,0.0050758985,0.003789621,0.006625764,0.00034483048],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007545981,0.00030532575,0.28237292,0.0026508144,0.00024312391,0.0013796098,0.20201178,0.0005310927,0.0018830999,0.051466335,0.09521614,0.3611851],"study_design_scores_gemma":[0.0002545951,0.00035207998,0.28471088,0.0063014906,0.00039347113,0.00081294635,0.46118,0.0016949713,0.0010877294,0.01358469,0.22883289,0.0007941523],"about_ca_topic_score_codex":0.93114674,"about_ca_topic_score_gemma":0.932841,"teacher_disagreement_score":0.93198097,"about_ca_system_score_codex":0.06801903,"about_ca_system_score_gemma":0.17276415,"threshold_uncertainty_score":0.49351496},"labels":[],"label_agreement":null},{"id":"W2109977913","doi":"10.1197/jamia.m3083","title":"Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data","year":2009,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Informatics; Health informatics; Domain (mathematical analysis); Rule-based system; Replicate; Natural language processing; Artificial intelligence; Data science; Medicine; Pathology; Engineering; Public health","score_opus":0.048107105421720676,"score_gpt":0.3755095698646421,"score_spread":0.3274024644429214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109977913","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":[0.005444541,0.00017030741,0.9064571,0.0018201293,0.00023481787,0.0024516166,0.0019608312,0.07821562,0.0032450065],"genre_scores_gemma":[0.039562017,0.00012834794,0.9471949,0.0015282881,0.00012185858,0.0013731617,0.0040093227,0.002192108,0.0038898883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99316496,0.0015504584,0.0013051202,0.0016808765,0.002084681,0.00021391662],"domain_scores_gemma":[0.98325306,0.009571032,0.0005641478,0.0024927321,0.0031884003,0.00093053235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009983857,0.0014548696,0.0017347555,0.0022241073,0.0015925962,0.0066173235,0.005750418,0.0045603714,0.018051999],"category_scores_gemma":[0.0267435,0.0013634339,0.001646734,0.0015479788,0.0011345175,0.0045759836,0.0026783491,0.003918643,0.013545759],"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":[0.0014868148,0.0022240735,0.010545838,0.0016510945,0.00086067687,0.006245217,0.0017353384,0.0515382,0.070541516,0.022071071,0.18183205,0.64926803],"study_design_scores_gemma":[0.00071796187,0.0004340702,0.0019834645,0.00029608558,0.00021199345,0.0022272614,0.0003556751,0.8100956,0.047800522,0.023125378,0.11241495,0.00033696246],"about_ca_topic_score_codex":0.007554048,"about_ca_topic_score_gemma":0.0071433336,"teacher_disagreement_score":0.018051999,"about_ca_system_score_codex":0.001165716,"about_ca_system_score_gemma":0.0031367438,"threshold_uncertainty_score":0.060389996},"labels":[],"label_agreement":null},{"id":"W2110366569","doi":"10.1136/amiajnl-2012-001442","title":"Comparison and validation of genomic predictors for anticancer drug sensitivity","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Drug; Personalized medicine; Clinical trial; Medicine; Precision medicine; Efficacy; Drug trial; Anticancer drug; Drug development; Sensitivity (control systems); Drug response; Pharmacology; Bioinformatics; Internal medicine; Biology; Pathology","score_opus":0.007769108686956617,"score_gpt":0.28182964904557267,"score_spread":0.27406054035861604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110366569","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":[0.94997597,0.0028408368,0.039327554,0.00023004097,0.00005907068,0.00012321628,0.0056377356,0.0008558862,0.0009495478],"genre_scores_gemma":[0.9751975,0.0003297556,0.011896922,0.000058270467,0.000026584368,0.0000687948,0.012218202,0.000039222738,0.00016470814],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974954,0.00087531976,0.0002151998,0.00074643455,0.0004925602,0.00017511078],"domain_scores_gemma":[0.9862918,0.010763368,0.00094980863,0.00082842755,0.0009286451,0.00023798514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049250103,0.0012524006,0.0009960614,0.0028796897,0.00026050193,0.0011227005,0.00069638464,0.00092672335,0.0010044096],"category_scores_gemma":[0.011456489,0.0002725389,0.0013227345,0.0013468234,0.0005303614,0.00058421626,0.0009317385,0.001044568,0.00045958007],"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":[0.0023660914,0.0008043873,0.50553554,0.00059950945,0.002330895,0.00024046711,0.00011432502,0.30866826,0.035351302,0.0005259913,0.002267368,0.14119571],"study_design_scores_gemma":[0.00014972694,0.0021227528,0.22294502,0.000110864705,0.000874652,0.0005637457,0.00010949997,0.71631885,0.052303296,0.0016619028,0.002738131,0.00010146997],"about_ca_topic_score_codex":0.0014795018,"about_ca_topic_score_gemma":0.0011878154,"teacher_disagreement_score":0.0049250103,"about_ca_system_score_codex":0.000625674,"about_ca_system_score_gemma":0.00066902844,"threshold_uncertainty_score":0.026046276},"labels":[],"label_agreement":null},{"id":"W2110961693","doi":"10.1136/amiajnl-2014-002901","title":"Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute; Common Fund; National Institutes of Health","keywords":"SNOMED CT; Computer science; Systematized Nomenclature of Medicine; Ontology; Scalability; Domain (mathematical analysis); Crowdsourcing; Artificial intelligence; Open Biomedical Ontologies; Subject-matter expert; Information retrieval; Machine learning; Data science; Terminology; Domain knowledge; Ontology alignment; Process ontology; World Wide Web; Expert system; Database","score_opus":0.021010209747288407,"score_gpt":0.340336319343528,"score_spread":0.3193261095962396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110961693","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":[0.9133872,0.0015048691,0.06693554,0.005299812,0.0002495641,0.0016536252,0.0005812004,0.00034674475,0.010041568],"genre_scores_gemma":[0.94947386,0.0004181383,0.04559205,0.0019297946,0.0001399828,0.00056693144,0.00042789907,0.00019146287,0.0012599097],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.90499276,0.06721622,0.0032789917,0.008587825,0.014729924,0.0011943214],"domain_scores_gemma":[0.45065904,0.464858,0.025863836,0.026947875,0.027658578,0.0040126718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08405937,0.001241073,0.0010616478,0.0064628315,0.0053254194,0.003678222,0.0040859347,0.0035130905,0.0022055528],"category_scores_gemma":[0.3054589,0.0009613444,0.0014160728,0.0030209636,0.0073763235,0.007817146,0.0068185506,0.0033668221,0.0006922477],"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":[0.0032627368,0.0042827767,0.385748,0.002932348,0.0014414842,0.00458063,0.20210195,0.026158936,0.006497864,0.023570621,0.020903533,0.31851915],"study_design_scores_gemma":[0.0013221033,0.0041638757,0.25618505,0.0033967097,0.0010917436,0.0054295887,0.16370098,0.32649183,0.016649766,0.12954523,0.090792954,0.0012301375],"about_ca_topic_score_codex":0.024688445,"about_ca_topic_score_gemma":0.01989474,"teacher_disagreement_score":0.08405937,"about_ca_system_score_codex":0.004686967,"about_ca_system_score_gemma":0.0055779913,"threshold_uncertainty_score":0.4445538},"labels":[],"label_agreement":null},{"id":"W2111697481","doi":"10.1136/amiajnl-2012-000821","title":"Healthcare information technology and economics","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institutes of Health Research","funders":"National Center for Research Resources","keywords":"Health care; Enabling; Health informatics; Value (mathematics); Informatics; Healthcare delivery; Health Administration Informatics; Health policy; Health economics; Healthcare system; Public relations; Business; Medicine; Political science; Computer science; Economic growth; Economics","score_opus":0.017109104239268674,"score_gpt":0.37732652220887253,"score_spread":0.3602174179696038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111697481","genre_codex":"other","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":[0.009044341,0.20697045,0.01872033,0.2779257,0.0030053058,0.00010499586,0.00028529268,0.00007111941,0.48387244],"genre_scores_gemma":[0.61784667,0.25152975,0.011970976,0.03168839,0.010590136,0.0002789019,0.00025191015,0.000069455426,0.075773835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971432,0.0013785681,0.00014274794,0.00024978063,0.0008107732,0.00027481216],"domain_scores_gemma":[0.9952632,0.0032612528,0.00032768017,0.0002848594,0.0005370693,0.00032589005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003426149,0.0004120763,0.000501646,0.002291193,0.001556631,0.0075091138,0.00038806756,0.0027421673,0.011712919],"category_scores_gemma":[0.0081985425,0.00023872485,0.0003068659,0.002991759,0.0075143105,0.006529372,0.0019636275,0.0024753134,0.0011426242],"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":[0.000005823413,0.000012922654,0.00059341715,0.0000862874,0.000009162984,0.000037979655,0.00016049232,0.00040534278,0.00003300205,0.96504885,0.014777458,0.018829357],"study_design_scores_gemma":[0.00000913941,0.00002798375,0.0014806137,0.0005182609,0.00000699381,0.000122725,0.0004383208,0.0005926571,0.00006205767,0.82562405,0.17109945,0.000017656235],"about_ca_topic_score_codex":0.0050214096,"about_ca_topic_score_gemma":0.0038047635,"teacher_disagreement_score":0.011712919,"about_ca_system_score_codex":0.0064657037,"about_ca_system_score_gemma":0.0045884443,"threshold_uncertainty_score":0.046912193},"labels":[],"label_agreement":null},{"id":"W2114645350","doi":"10.1136/amiajnl-2011-000126","title":"Adjusting outbreak detection algorithms for surveillance during epidemic and non-epidemic periods","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Outbreak; Disease surveillance; Incidence (geometry); Infectious disease (medical specialty); Foot-and-mouth disease; Algorithm; Disease; Geography; Computer science; Virology; Medicine; Mathematics; Pathology","score_opus":0.01752868203361374,"score_gpt":0.28729413663631437,"score_spread":0.2697654546027006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114645350","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":[0.29915303,0.00059396203,0.6971452,0.0005497603,0.00010750391,0.00024786315,0.00018113841,0.001551634,0.00046992916],"genre_scores_gemma":[0.7180706,0.0001429802,0.28097624,0.00012864385,0.00003450104,0.000113392314,0.00030260137,0.000057643476,0.00017345244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966084,0.0013217018,0.0005489538,0.0007507517,0.0005865228,0.00018362064],"domain_scores_gemma":[0.97509557,0.015540309,0.0029346803,0.0019944052,0.004094831,0.00034016423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009780319,0.00082503073,0.0008366303,0.0019582766,0.00050102454,0.0010771798,0.0011723522,0.00084873964,0.0002558044],"category_scores_gemma":[0.051724628,0.00044363507,0.0005940707,0.0012637452,0.00038721118,0.0015007977,0.0008179016,0.0012022944,0.00011005987],"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":[0.0007612908,0.00048051,0.22111149,0.00021919393,0.00046594013,0.00011585559,0.0006006114,0.27514768,0.018898852,0.002421017,0.0015936763,0.4781839],"study_design_scores_gemma":[0.000085945,0.00027889156,0.025226751,0.000019971865,0.00008786992,0.0001452687,0.00011649736,0.9555008,0.014633841,0.0023395764,0.0015096158,0.000054947755],"about_ca_topic_score_codex":0.0052179475,"about_ca_topic_score_gemma":0.0048396075,"teacher_disagreement_score":0.009780319,"about_ca_system_score_codex":0.0009404724,"about_ca_system_score_gemma":0.002001435,"threshold_uncertainty_score":0.051723897},"labels":[],"label_agreement":null},{"id":"W2114801561","doi":"10.1136/amiajnl-2013-002203","title":"Barriers and facilitators to implementing electronic prescription: a systematic review of user groups' perceptions","year":2013,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier universitaire de Québec; Université Laval","funders":"","keywords":"Medical prescription; Perception; Electronic prescribing; Primary care; Medicine; Family medicine; Nursing; Psychology","score_opus":0.025232028616739174,"score_gpt":0.4306330955245783,"score_spread":0.4054010669078391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114801561","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":[0.027559875,0.9676384,0.00084916624,0.0010097976,0.00014702973,0.0009521527,0.00074038364,0.000019619541,0.0010836441],"genre_scores_gemma":[0.13694274,0.8561991,0.0029095414,0.0010779789,0.00006956991,0.0020226953,0.000561377,0.000017449998,0.00019966676],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96361977,0.014302057,0.013843792,0.0013557228,0.0062714387,0.0006072145],"domain_scores_gemma":[0.87786907,0.09849889,0.013407808,0.0011659297,0.00808821,0.0009701109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032481644,0.0009160239,0.005160243,0.012723671,0.000975132,0.0030146283,0.0015542745,0.0016553978,0.0021875654],"category_scores_gemma":[0.09717273,0.0010724125,0.006085278,0.010949187,0.0014009778,0.0042308965,0.002272525,0.0012334372,0.00018695254],"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":[0.00039860175,0.00006023441,0.010429163,0.834225,0.005962241,0.00031675972,0.009910597,0.00010362688,0.0004310443,0.00043908082,0.0013019638,0.13642174],"study_design_scores_gemma":[0.00019165811,0.0005857843,0.021326508,0.91679865,0.022765903,0.0010648236,0.012536815,0.00009978257,0.00044978943,0.00039827309,0.023687607,0.00009447396],"about_ca_topic_score_codex":0.00724545,"about_ca_topic_score_gemma":0.017043328,"teacher_disagreement_score":0.032481644,"about_ca_system_score_codex":0.0030248237,"about_ca_system_score_gemma":0.014470173,"threshold_uncertainty_score":0.17178136},"labels":[],"label_agreement":null},{"id":"W2114933741","doi":"10.1197/jamia.m1130","title":"Speech Recognition as a Transcription Aid: A Randomized Comparison With Standard Transcription","year":2003,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Nutrition, Metabolism and Diabetes","funders":"","keywords":"Computer science; Transcription (linguistics); Speech recognition; Randomized controlled trial; Natural language processing; Medicine","score_opus":0.029239556586696712,"score_gpt":0.38027634768006235,"score_spread":0.35103679109336566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114933741","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":[0.94337535,0.0023811895,0.0044139586,0.0008337616,0.00265505,0.043412395,0.00071499444,0.00023536282,0.0019779708],"genre_scores_gemma":[0.9035058,0.0013136016,0.012257585,0.0010363936,0.0020077596,0.07638692,0.0003673707,0.00005838802,0.0030660727],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.96777546,0.022665963,0.003254293,0.0030590708,0.0019812712,0.0012638684],"domain_scores_gemma":[0.958836,0.026012905,0.008356692,0.0025311988,0.0019308359,0.00233235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017692706,0.0025667583,0.005232166,0.0015381309,0.0010203794,0.0018983235,0.002359756,0.0043188604,0.015912782],"category_scores_gemma":[0.0294428,0.0016065883,0.003244287,0.0011787175,0.003787195,0.0028980896,0.0013804277,0.004156676,0.0019814982],"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":[0.97729266,0.0125936065,0.00038396957,0.00047039147,0.00035395578,0.00002351244,0.0000746318,0.00022272374,0.0005770245,0.00015557806,0.00021414786,0.0076377345],"study_design_scores_gemma":[0.79603976,0.20070472,0.00081945525,0.00006593575,0.00046279133,0.000017633098,0.00005326533,0.00071687036,0.00054728583,0.00022206928,0.00032506287,0.000025125439],"about_ca_topic_score_codex":0.0009327116,"about_ca_topic_score_gemma":0.0011084677,"teacher_disagreement_score":0.017692706,"about_ca_system_score_codex":0.002191103,"about_ca_system_score_gemma":0.0033651076,"threshold_uncertainty_score":0.09356904},"labels":[],"label_agreement":null},{"id":"W2115883395","doi":"10.1136/amiajnl-2012-001509","title":"Biomedical data privacy: problems, perspectives, and recent advances","year":2012,"lang":"en","type":"editorial","venue":"Journal of the American Medical Informatics Association","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":156,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Context (archaeology); Internet privacy; Health care; Data science; Information privacy; Computer science; Big data; Political science; Law; Data mining; Geography","score_opus":0.1374027271057757,"score_gpt":0.5133070202361707,"score_spread":0.375904293130395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115883395","genre_codex":"review","genre_gemma":"editorial","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007718035,0.8799362,0.007189254,0.10120785,0.0023024553,0.000016418804,0.000060147708,0.000042689364,0.008473059],"genre_scores_gemma":[0.026828183,0.9239195,0.0095744785,0.016246999,0.021404678,0.000049007114,0.00013113557,0.00004514992,0.0018009532],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9888966,0.004987199,0.00065326574,0.0013802322,0.0035176943,0.0005649089],"domain_scores_gemma":[0.9170391,0.06774954,0.0022270195,0.0029976855,0.00773256,0.00225399],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02765506,0.0014435848,0.0019410776,0.0068924115,0.0025024572,0.011246093,0.0044221533,0.010243782,0.005747949],"category_scores_gemma":[0.043833397,0.0011387293,0.0014648066,0.011380364,0.015943374,0.030070728,0.0061654323,0.012969709,0.0015692808],"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":[0.0001576671,0.00015184852,0.0012607968,0.0035015289,0.00008738375,0.0003366167,0.00083858834,0.0020059438,0.0002551546,0.45656565,0.06753879,0.4673],"study_design_scores_gemma":[0.000037782153,0.00013323438,0.0009962397,0.0047141165,0.000049702936,0.0017094072,0.0016159258,0.003985265,0.00036664572,0.38765305,0.5986464,0.00009228013],"about_ca_topic_score_codex":0.0022395859,"about_ca_topic_score_gemma":0.0015533698,"teacher_disagreement_score":0.97234493,"about_ca_system_score_codex":0.0058809207,"about_ca_system_score_gemma":0.004868838,"threshold_uncertainty_score":0.14625573},"labels":[],"label_agreement":null},{"id":"W2118579843","doi":"10.1197/jamia.m1462","title":"Incorporating the International Classification of Functioning, Disability, and Health (ICF) into an Electronic Health Record to Create Indicators of Function: Proof of Concept Using the SF-12","year":2004,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Victoria Hospital; McGill University Health Centre; York University; University of Toronto; McGill University","funders":"","keywords":"International Classification of Functioning, Disability and Health; Mental health; Quality of life (healthcare); Medicine; Gerontology; Coding (social sciences); Stroke (engine); Cohort; Population; Patient-Reported Outcomes Measurement Information System; Psychology; Physical therapy; Physical medicine and rehabilitation; Clinical psychology; Psychometrics; Computerized adaptive testing; Psychiatry; Rehabilitation; Statistics; Environmental health; Mathematics","score_opus":0.023556626990914452,"score_gpt":0.33326906956994384,"score_spread":0.3097124425790294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118579843","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":[0.23063831,0.003259156,0.6971242,0.006894782,0.002362256,0.032544877,0.014165617,0.0029330866,0.010077834],"genre_scores_gemma":[0.20422597,0.0016080152,0.76902455,0.001432646,0.00077982736,0.013737495,0.0075699207,0.00023607042,0.0013854741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9278495,0.0464366,0.006076289,0.0035297784,0.01519157,0.00091631967],"domain_scores_gemma":[0.9050588,0.04089892,0.008040478,0.012355416,0.032074712,0.0015716653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.063623846,0.0010764012,0.0010100775,0.0023110986,0.00062467123,0.00330707,0.0021831319,0.0020947303,0.0034049547],"category_scores_gemma":[0.12268825,0.0005646688,0.0013930398,0.0021099118,0.0010774384,0.004095685,0.0024115408,0.0016589914,0.0019556168],"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":[0.0030509438,0.0037200972,0.11701982,0.0025388564,0.00067249034,0.00029067503,0.001470848,0.0031623226,0.011877255,0.0031933803,0.019990811,0.8330126],"study_design_scores_gemma":[0.016748888,0.0417913,0.47646877,0.009815563,0.00396315,0.0057939445,0.0052476525,0.09829559,0.079609424,0.011700354,0.24902298,0.0015424732],"about_ca_topic_score_codex":0.0028815714,"about_ca_topic_score_gemma":0.0017805065,"teacher_disagreement_score":0.063623846,"about_ca_system_score_codex":0.000725615,"about_ca_system_score_gemma":0.004541093,"threshold_uncertainty_score":0.33647907},"labels":[],"label_agreement":null},{"id":"W2119647354","doi":"10.1136/jamia.2010.006437","title":"Agreement between common goals discussed and documented in the ICU","year":2010,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. National Library of Medicine; National Institute of Nursing Research","keywords":"Documentation; Sedation; Medicine; Health records; Descriptive statistics; Intensive care unit; Medical record; Reliability (semiconductor); Medical emergency; Health care; Nursing; Emergency medicine; Intensive care medicine; Computer science; Statistics","score_opus":0.01775070408079543,"score_gpt":0.4187140064906158,"score_spread":0.40096330240982037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119647354","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":[0.98080975,0.0006667063,0.012541683,0.00034763833,0.000059110884,0.00026632147,0.00052062323,0.00016831754,0.00461978],"genre_scores_gemma":[0.9935114,0.00016658756,0.00552289,0.000086582644,0.000012785014,0.00012248398,0.0003386269,0.00001838782,0.00022023811],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.93277556,0.040274527,0.011392241,0.0036862125,0.010591676,0.0012798238],"domain_scores_gemma":[0.780052,0.13683778,0.037984993,0.011220969,0.03167378,0.0022304954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0339217,0.00030628746,0.00046540133,0.0028119683,0.0004428023,0.0016864319,0.0008054061,0.00052358554,0.0007859748],"category_scores_gemma":[0.15632164,0.00026571954,0.0005807982,0.0013577052,0.00094651466,0.0017278749,0.0028441916,0.0007176255,0.0002519207],"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":[0.00067811867,0.00017336913,0.8767725,0.0006750361,0.00032457954,0.00019055571,0.021831147,0.0010965049,0.0019176648,0.00074352854,0.0015146835,0.094082184],"study_design_scores_gemma":[0.000049561837,0.00077571906,0.96117646,0.0009793577,0.0001511488,0.00056878146,0.017630441,0.007651354,0.0036345401,0.0021600102,0.0050965385,0.00012597631],"about_ca_topic_score_codex":0.0011948574,"about_ca_topic_score_gemma":0.0014614468,"teacher_disagreement_score":0.0339217,"about_ca_system_score_codex":0.0010281159,"about_ca_system_score_gemma":0.0014895316,"threshold_uncertainty_score":0.17939728},"labels":[],"label_agreement":null},{"id":"W2120467164","doi":"10.1197/jamia.m2544","title":"HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":503,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"The Internet; Visualization; Computer science; Infectious disease (medical specialty); Disease; World Wide Web; Data science; Medicine; Artificial intelligence; Pathology","score_opus":0.013291141399848121,"score_gpt":0.34652977210781216,"score_spread":0.33323863070796406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120467164","genre_codex":"software","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":[0.20948231,0.0019163026,0.22578393,0.00399991,0.00056092255,0.002146423,0.10013672,0.43814772,0.01782581],"genre_scores_gemma":[0.5735584,0.0008585879,0.34531403,0.0005150906,0.0006313219,0.0009422372,0.072335474,0.001942801,0.0039020693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750227,0.0008393591,0.00019015424,0.0005392033,0.00080654747,0.0001224573],"domain_scores_gemma":[0.99140817,0.0035780931,0.0017863896,0.0011914615,0.0015617018,0.0004742073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034301353,0.0016754448,0.00054297695,0.01049567,0.00046544525,0.0026602293,0.0012012802,0.00072954403,0.0036817573],"category_scores_gemma":[0.011914876,0.000397989,0.0006140873,0.0035545335,0.00042510967,0.0029636628,0.0020104828,0.00064223364,0.0025028882],"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":[0.0015181543,0.0006940847,0.14660487,0.0011776977,0.0005424603,0.0007319279,0.0013780575,0.021017535,0.009833995,0.0035465371,0.21963333,0.5933213],"study_design_scores_gemma":[0.00028418744,0.00070814765,0.1646074,0.00038089638,0.00027843638,0.0007459556,0.0013820205,0.7032286,0.030832749,0.011133123,0.08614567,0.0002728107],"about_ca_topic_score_codex":0.00572099,"about_ca_topic_score_gemma":0.004135867,"teacher_disagreement_score":0.01049567,"about_ca_system_score_codex":0.00064998434,"about_ca_system_score_gemma":0.0011263577,"threshold_uncertainty_score":0.018140495},"labels":[],"label_agreement":null},{"id":"W2122067803","doi":"10.1136/amiajnl-2011-000454","title":"A systematic review to evaluate the accuracy of electronic adverse drug event detection","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Adverse drug event; Adverse effect; Medicine; Systematic review; Computer science; MEDLINE; Internal medicine","score_opus":0.062090071315468634,"score_gpt":0.4695414874542294,"score_spread":0.4074514161387608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122067803","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":[0.0035558364,0.98885554,0.0010646714,0.0007428513,0.00029989897,0.0025620048,0.0019546726,0.000033074906,0.00093140575],"genre_scores_gemma":[0.08141508,0.8994438,0.0082390765,0.002234152,0.00030554394,0.00627847,0.0017586562,0.000030817082,0.00029431353],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9512745,0.016132446,0.022748595,0.001980391,0.007314236,0.0005497948],"domain_scores_gemma":[0.79824793,0.1479186,0.031628557,0.003358437,0.01795816,0.0008883506],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.035283446,0.0021058975,0.009886892,0.022696976,0.0010243127,0.004208576,0.0028380626,0.0022810998,0.004495506],"category_scores_gemma":[0.19563347,0.0014773948,0.008742334,0.016442623,0.0013860664,0.004607647,0.0022422415,0.0012986821,0.0004704323],"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":[0.00018524664,0.0000152033435,0.002213052,0.9560139,0.019678509,0.00009472867,0.00016477717,0.00011067945,0.00012919503,0.00021433581,0.0009344423,0.020246048],"study_design_scores_gemma":[0.00029387386,0.00019018745,0.0047306917,0.89234513,0.09240744,0.00031658527,0.00017715826,0.00018243601,0.00021316526,0.00034046173,0.008767854,0.000034986344],"about_ca_topic_score_codex":0.007415,"about_ca_topic_score_gemma":0.021433564,"teacher_disagreement_score":0.96471655,"about_ca_system_score_codex":0.006446994,"about_ca_system_score_gemma":0.018008264,"threshold_uncertainty_score":0.18659896},"labels":[],"label_agreement":null},{"id":"W2122149485","doi":"10.1136/amiajnl-2011-000049","title":"Electronic decision support for diagnostic imaging in a primary care setting","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Children's Hospital of Winnipeg; Health Sciences Centre","funders":"Canadian Institutes of Health Research; Health Canada; Association Canadienne des Radiologistes","keywords":"Clinical decision support system; Decision support system; Guideline; Work flow; Medicine; Primary care; Work (physics); Clinical Practice; Family medicine; Medical physics; Medical emergency; Computer science; Artificial intelligence; Pathology","score_opus":0.016030246846248784,"score_gpt":0.37291683229068157,"score_spread":0.3568865854444328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122149485","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":[0.99424464,0.00079848233,0.000525703,0.0017246298,0.000021192398,0.00019778211,0.00022146574,0.000085047825,0.0021811053],"genre_scores_gemma":[0.9957985,0.00034677613,0.0024467255,0.0007444981,0.000043659857,0.00006911323,0.00010395786,0.00000593964,0.0004407959],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967686,0.001777775,0.00031578544,0.00037527064,0.0005628678,0.00019964772],"domain_scores_gemma":[0.96418476,0.021156615,0.008117196,0.0012328858,0.0017952098,0.0035133236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035898413,0.0002257848,0.00028385493,0.0008862954,0.001291802,0.0010314942,0.0006291491,0.0007990797,0.005523143],"category_scores_gemma":[0.0322843,0.0003348805,0.00020038913,0.00080288807,0.0005329916,0.00076088234,0.0012290669,0.0007179849,0.0009122843],"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":[0.0010285781,0.0039002614,0.7834169,0.0007830206,0.0001126312,0.0035766875,0.0126668485,0.0004037234,0.0019764667,0.00012998906,0.009306535,0.18269837],"study_design_scores_gemma":[0.0006339206,0.0042923028,0.96648604,0.00054962956,0.00010853235,0.006143132,0.0077216183,0.0029152522,0.0010841923,0.00040264908,0.009586793,0.000075945034],"about_ca_topic_score_codex":0.0037133605,"about_ca_topic_score_gemma":0.0074551194,"teacher_disagreement_score":0.005523143,"about_ca_system_score_codex":0.0011362869,"about_ca_system_score_gemma":0.001699994,"threshold_uncertainty_score":0.018985093},"labels":[],"label_agreement":null},{"id":"W2123527701","doi":"10.1197/jamia.m2270","title":"Systematic Review of Home Telemonitoring for Chronic Diseases: The Evidence Base","year":2007,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":839,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Medicine; MEDLINE; Diabetes mellitus; Intensive care medicine; Cochrane Library; Socioeconomic status; Emergency medicine; Medical emergency; Internal medicine; Meta-analysis; Population; Environmental health","score_opus":0.055698962105169864,"score_gpt":0.4462229108238785,"score_spread":0.39052394871870866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123527701","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":[0.0008737556,0.99787617,0.00012335692,0.00022605591,0.00010231739,0.0002505382,0.00031692538,0.0000074919108,0.00022347408],"genre_scores_gemma":[0.017765686,0.9795447,0.0009919237,0.00047653422,0.000111559806,0.00069326395,0.0003005266,0.000005786142,0.00011012317],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9866193,0.006062519,0.0041085654,0.0007769024,0.0021795873,0.00025305283],"domain_scores_gemma":[0.9348979,0.05123009,0.009491616,0.00071279076,0.003240962,0.00042666023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012492735,0.0016935171,0.00971781,0.011053046,0.0005697474,0.0027565327,0.0021052745,0.0020272096,0.005330033],"category_scores_gemma":[0.068269856,0.0010163878,0.006891485,0.011987257,0.0009119261,0.0020419678,0.0013072304,0.0012346233,0.00035132086],"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":[0.00021027574,0.00001837886,0.00058407785,0.9557293,0.009692913,0.000079645004,0.00009905923,0.00011145891,0.000080678874,0.00013768525,0.0013483019,0.031908195],"study_design_scores_gemma":[0.00043658263,0.00028514818,0.005034727,0.9133332,0.06751356,0.00028901832,0.00016843376,0.00013765229,0.00016613775,0.0002585346,0.0123487245,0.000028316195],"about_ca_topic_score_codex":0.008338479,"about_ca_topic_score_gemma":0.020937514,"teacher_disagreement_score":0.012492735,"about_ca_system_score_codex":0.003888122,"about_ca_system_score_gemma":0.011723328,"threshold_uncertainty_score":0.06606865},"labels":[],"label_agreement":null},{"id":"W2125056609","doi":"10.1197/jamia.m2374","title":"Presentation of the 2006 Morris F. Collen Award to Edward H. (Ted) Shortliffe","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Creativity; BLISS; Psychology; Sociology; Library science; Medicine; Law; Political science; Computer science","score_opus":0.040308221912290734,"score_gpt":0.4625203601205042,"score_spread":0.42221213820821346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125056609","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020510438,0.012520641,0.0013137624,0.2978588,0.5670999,0.00021470957,0.0005046628,0.00033501978,0.11810138],"genre_scores_gemma":[0.011803215,0.015877446,0.0012459583,0.052517276,0.092551775,0.00016356428,0.0007938774,0.00023664076,0.82481027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987626,0.000118397125,0.00004852136,0.00014914593,0.0007391526,0.00018219136],"domain_scores_gemma":[0.99501956,0.00019103305,0.00011643099,0.000079420846,0.0016976134,0.002895815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002072766,0.0008046477,0.00062150095,0.0008847914,0.001732802,0.0040762248,0.0009373548,0.0025763493,0.11163208],"category_scores_gemma":[0.0078026694,0.0002550947,0.000504987,0.0004123228,0.00050798553,0.0015529741,0.0027897647,0.0038211134,0.042743392],"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":[0.000004961274,0.0000052726937,0.000048929047,0.000009568807,8.339724e-7,0.0000246323,0.000011075109,0.000009959298,0.000025835905,0.00023201434,0.9933258,0.0063010263],"study_design_scores_gemma":[0.000007767828,0.000024064217,0.00033150098,0.00007192049,0.0000023427756,0.00019486729,0.000078755205,0.000037677088,0.000047275367,0.00026017998,0.9989365,0.0000071385884],"about_ca_topic_score_codex":0.0033177983,"about_ca_topic_score_gemma":0.010292283,"teacher_disagreement_score":0.11163208,"about_ca_system_score_codex":0.0025494546,"about_ca_system_score_gemma":0.0041656466,"threshold_uncertainty_score":0.37344652},"labels":[],"label_agreement":null},{"id":"W2125805292","doi":"10.1093/jamia/ocu009","title":"Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; Jewish General Hospital; McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Health Canada","keywords":"Medicine; Asthma; Randomized controlled trial; Intervention (counseling); Emergency department; Physical therapy; Cluster randomised controlled trial; Respiratory therapist; Decision support system; Emergency medicine; Internal medicine; Intensive care medicine; Nursing; Data mining","score_opus":0.07399234536742265,"score_gpt":0.45630669775883814,"score_spread":0.3823143523914155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125805292","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":[0.960593,0.008112991,0.0017927701,0.0012573653,0.0018012773,0.0229345,0.0012662605,0.00023291672,0.0020088742],"genre_scores_gemma":[0.96203506,0.0028697988,0.005711243,0.0010143372,0.00083784986,0.02507993,0.0006651757,0.000023618572,0.0017630127],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9926444,0.004760418,0.00061408855,0.0009601775,0.00052111427,0.00049969513],"domain_scores_gemma":[0.99285114,0.0035006346,0.0016198483,0.00042288718,0.0005130013,0.0010925335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006368357,0.0017213622,0.004709141,0.0008401601,0.000864878,0.0018297136,0.0017939995,0.003432061,0.0073937345],"category_scores_gemma":[0.0109155765,0.0009694854,0.004355094,0.0010761818,0.001979552,0.0013569013,0.00095038133,0.0029763824,0.00054194673],"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":[0.9849257,0.006137586,0.0004531692,0.0008758601,0.0017092074,0.000013548055,0.000032513733,0.00020921782,0.0002126433,0.000058449532,0.0001797084,0.0051924656],"study_design_scores_gemma":[0.9653176,0.032197986,0.0008258353,0.00006600067,0.00097574195,0.0000050421913,0.0000150498,0.0003161205,0.000083475315,0.000050421026,0.0001396283,0.0000071313625],"about_ca_topic_score_codex":0.0094677815,"about_ca_topic_score_gemma":0.00803532,"teacher_disagreement_score":0.0094677815,"about_ca_system_score_codex":0.0027332222,"about_ca_system_score_gemma":0.004138342,"threshold_uncertainty_score":0.033679485},"labels":[],"label_agreement":null},{"id":"W2128128412","doi":"10.1197/jamia.m3144","title":"A Globally Optimal k-Anonymity Method for the De-Identification of Health Data","year":2009,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":239,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Ottawa Hospital; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Vanderbilt University","keywords":"Anonymity; k-anonymity; Computer science; Information loss; Identification (biology); Data mining; Metric (unit); Protected health information; Patient Consent; Entropy (arrow of time); Algorithm; Artificial intelligence; Public health; Computer security; Medicine; Family medicine; Health policy; Engineering","score_opus":0.03560064357584823,"score_gpt":0.37546947988762586,"score_spread":0.3398688363117776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128128412","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":[0.012188305,0.00010523351,0.9864021,0.00016204125,0.000028704837,0.00008375956,0.0000756279,0.0002923624,0.00066187175],"genre_scores_gemma":[0.21926913,0.00010914614,0.7789375,0.00010030965,0.00004082907,0.00019454956,0.00026479285,0.000057788304,0.0010258995],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929636,0.0029537838,0.00053528545,0.0015073393,0.001719725,0.00032032168],"domain_scores_gemma":[0.9880855,0.005519595,0.0013950492,0.002718038,0.0019997198,0.00028207022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00666398,0.000885294,0.0013053406,0.0022078278,0.0015728342,0.0021823964,0.0017165288,0.0011918889,0.0014610208],"category_scores_gemma":[0.018144617,0.0003918925,0.0012271893,0.0020032218,0.0019983326,0.0039504734,0.0029404003,0.0017593899,0.00056718534],"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":[0.0009036985,0.0002964712,0.0053534177,0.00029814217,0.00022261395,0.0001712667,0.00092969154,0.28093487,0.014174959,0.09811846,0.0043433993,0.594253],"study_design_scores_gemma":[0.0000924562,0.00031429424,0.0014789846,0.000046348767,0.000045969267,0.0006045264,0.00037054214,0.8771713,0.019825751,0.094860196,0.00509072,0.000098915465],"about_ca_topic_score_codex":0.0010578049,"about_ca_topic_score_gemma":0.0010541179,"teacher_disagreement_score":0.00666398,"about_ca_system_score_codex":0.0017955241,"about_ca_system_score_gemma":0.0036807833,"threshold_uncertainty_score":0.035242915},"labels":[],"label_agreement":null},{"id":"W2130156904","doi":"10.1197/jamia.m2519","title":"An Interdisciplinary Computer-based Information Tool for Palliative Severe Pain Management","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pain Management and Opioid Use","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Victoria; Vancouver Native Health Society; University of Ottawa","funders":"","keywords":"Usability; Computer science; Palliative care; Knowledge management; Ontology; Process (computing); Human–computer interaction; Medicine; Nursing","score_opus":0.010549936419779447,"score_gpt":0.29909956101317386,"score_spread":0.2885496245933944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130156904","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":[0.39153352,0.0011620139,0.5471413,0.0042834147,0.00027393465,0.013721028,0.0018284513,0.0038265376,0.03622981],"genre_scores_gemma":[0.3724243,0.0003781854,0.6172713,0.00039405923,0.00003278459,0.0063719843,0.00066818367,0.000108517284,0.0023506891],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9868379,0.010376855,0.000971076,0.00046198635,0.0011173697,0.00023472411],"domain_scores_gemma":[0.9545032,0.038637895,0.0012415495,0.0023460246,0.0025337392,0.00073766924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013733502,0.0006938474,0.000393696,0.0032049883,0.0009838463,0.0020810291,0.0016897869,0.0010442426,0.0064221392],"category_scores_gemma":[0.035998482,0.00026496768,0.0005556849,0.0023471764,0.0010781634,0.003927421,0.0028929696,0.00087911484,0.00076355017],"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":[0.00073219877,0.0017309546,0.013270803,0.0038936373,0.000067324676,0.00086493394,0.06399119,0.0023263448,0.011081765,0.017971704,0.009862526,0.87420666],"study_design_scores_gemma":[0.0025981832,0.012361877,0.0924131,0.0121216,0.0006961062,0.008545732,0.12511632,0.04601704,0.035102133,0.06275438,0.60148484,0.00078860053],"about_ca_topic_score_codex":0.00033874233,"about_ca_topic_score_gemma":0.0006564845,"teacher_disagreement_score":0.013733502,"about_ca_system_score_codex":0.0012970985,"about_ca_system_score_gemma":0.0027282164,"threshold_uncertainty_score":0.072630584},"labels":[],"label_agreement":null},{"id":"W2130258915","doi":"10.1136/amiajnl-2013-001704","title":"Estimating the information gap between emergency department records of community medication compared to on-line access to the community-based pharmacy records","year":2013,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Pharmacy; Emergency department; Medical emergency; Medicine; Line (geometry); Community pharmacy; Medical record; Emergency medicine; Family medicine; Nursing; Internal medicine","score_opus":0.1979367472164486,"score_gpt":0.4659849624347712,"score_spread":0.2680482152183226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130258915","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":[0.998204,0.00018104844,0.00073460443,0.000056384208,0.0000041107774,0.000029797491,0.00057834724,0.000009250731,0.00020240857],"genre_scores_gemma":[0.99843055,0.000043194963,0.0007522363,0.000020054447,0.0000074628683,0.000024973402,0.0006595185,0.0000026047983,0.00005938011],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98836344,0.006199724,0.001541314,0.0015224392,0.0018699106,0.0005031124],"domain_scores_gemma":[0.8891492,0.07127381,0.029449547,0.0038699247,0.005189354,0.0010681165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015465534,0.00041740332,0.0005778438,0.0027790943,0.00036623556,0.0011481801,0.00088893453,0.0006636015,0.0011106277],"category_scores_gemma":[0.09447725,0.00030122686,0.00079707697,0.0026079079,0.00036720006,0.0012719993,0.001603954,0.00061015866,0.00018970513],"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":[0.00020823146,0.000031342573,0.9975804,0.000018626903,0.000098316275,0.000018848266,0.00011089611,0.0001532731,0.000032076172,0.000020349033,0.000044096625,0.0016834455],"study_design_scores_gemma":[0.000026170415,0.00028768094,0.9927551,0.000045326233,0.00009631576,0.000164475,0.0003972008,0.0057257493,0.00022026639,0.00007705583,0.00019563116,0.0000091004395],"about_ca_topic_score_codex":0.014107663,"about_ca_topic_score_gemma":0.0070181293,"teacher_disagreement_score":0.015465534,"about_ca_system_score_codex":0.0009740164,"about_ca_system_score_gemma":0.0009670786,"threshold_uncertainty_score":0.08179051},"labels":[],"label_agreement":null},{"id":"W2130865790","doi":"10.1136/amiajnl-2014-002707","title":"Query Health: standards-based, cross-platform population health surveillance","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; U.S. Food and Drug Administration; U.S. National Library of Medicine; Agency for Healthcare Research and Quality; National Institutes of Health; Hamilton Health Sciences Foundation","keywords":"Computer science; Public health informatics; Health informatics; Population; Population health; Data quality; Interoperability; Public health; Data science; HRHIS; World Wide Web; Health policy; Medicine; Environmental health; Business","score_opus":0.022327722155752957,"score_gpt":0.44583686223174934,"score_spread":0.4235091400759964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130865790","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":[0.013451141,0.0007038789,0.9164126,0.008382777,0.00027490105,0.0028418335,0.0046587484,0.04198254,0.011291637],"genre_scores_gemma":[0.12946059,0.00079461234,0.8382418,0.0029495459,0.00029379554,0.002382846,0.018664366,0.0042068237,0.0030056087],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.91151273,0.038096048,0.011603878,0.009701983,0.02649744,0.00258789],"domain_scores_gemma":[0.870755,0.042761274,0.009579528,0.0347955,0.038386773,0.00372198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13260914,0.0017259931,0.0011582733,0.006958884,0.0019247376,0.009621062,0.007060542,0.0024736125,0.002863976],"category_scores_gemma":[0.11394754,0.0012262136,0.001985508,0.0065785656,0.0033769992,0.014903016,0.010972087,0.0035759537,0.0019019978],"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":[0.0011846953,0.0012028357,0.07086445,0.003030676,0.0006880009,0.000695363,0.011518951,0.022054413,0.019854037,0.2811663,0.12987165,0.4578686],"study_design_scores_gemma":[0.00076023914,0.001897623,0.046145722,0.003053359,0.0006077593,0.0011980262,0.0058265575,0.17470585,0.06309505,0.15435354,0.54749596,0.0008603455],"about_ca_topic_score_codex":0.024381785,"about_ca_topic_score_gemma":0.010458397,"teacher_disagreement_score":0.13260914,"about_ca_system_score_codex":0.0057982476,"about_ca_system_score_gemma":0.019491196,"threshold_uncertainty_score":0.70131254},"labels":[],"label_agreement":null},{"id":"W2131664978","doi":"10.1197/jamia.m2158","title":"McMaster PLUS: A Cluster Randomized Clinical Trial of an Intervention to Accelerate Clinical Use of Evidence-based Information from Digital Libraries","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; McMaster University Medical Centre; Western University","funders":"","keywords":"Randomized controlled trial; Medicine; Crossover study; Cluster (spacecraft); Service (business); Intervention (counseling); Relevance (law); Clinical trial; MEDLINE; Family medicine; Computer science; Alternative medicine; Internal medicine; Nursing","score_opus":0.3005932180760031,"score_gpt":0.5338418913977131,"score_spread":0.23324867332171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131664978","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":[0.9850659,0.00089055375,0.0005443659,0.00065192627,0.00027017054,0.010607895,0.0004419234,0.00018242479,0.0013448744],"genre_scores_gemma":[0.9813323,0.0005168149,0.0040659746,0.0005558188,0.00029828935,0.010837476,0.00028393132,0.000012366804,0.0020969084],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99666095,0.0019753436,0.00018250354,0.0005533045,0.00028439699,0.00034350823],"domain_scores_gemma":[0.99646926,0.0009322403,0.0009322585,0.00027585958,0.00019653635,0.0011938361],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0027676155,0.0014810778,0.0022190656,0.00079286995,0.00084353564,0.0010310737,0.0014185079,0.0016744375,0.008236659],"category_scores_gemma":[0.0063160816,0.0006674576,0.001227222,0.00075313426,0.0018426295,0.0009954234,0.001056477,0.0014742189,0.0004907266],"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":[0.9776818,0.0085671395,0.0008165646,0.0004484463,0.0005126776,0.000016231956,0.00006049884,0.00018666545,0.0005242309,0.00009681619,0.0004469059,0.010641924],"study_design_scores_gemma":[0.88356113,0.11025281,0.0043355622,0.000040309955,0.00054462627,0.000009909885,0.00003501495,0.0005016014,0.00022230331,0.000137209,0.0003421024,0.000017291934],"about_ca_topic_score_codex":0.019945923,"about_ca_topic_score_gemma":0.030343922,"teacher_disagreement_score":0.9972324,"about_ca_system_score_codex":0.0038828275,"about_ca_system_score_gemma":0.00631018,"threshold_uncertainty_score":0.03965962},"labels":[],"label_agreement":null},{"id":"W2131977879","doi":"10.1136/amiajnl-2012-000949","title":"“Not all my friends need to know”: a qualitative study of teenage patients, privacy, and social media","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Norges Forskningsråd","keywords":"Social media; Personally identifiable information; Internet privacy; Qualitative research; The Internet; Psychology; Medicine; Sociology; World Wide Web; Computer science","score_opus":0.05710573354270274,"score_gpt":0.42476700944391405,"score_spread":0.3676612759012113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131977879","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":[0.98540735,0.0017277595,0.0013404131,0.0063816826,0.0002191975,0.000521135,0.00018870649,0.000029841192,0.004183889],"genre_scores_gemma":[0.9918677,0.0017484092,0.0008883123,0.0026959286,0.000053432028,0.00051493716,0.000086044536,0.000037576814,0.002107641],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98771775,0.009443325,0.00040876807,0.00047487096,0.00060268916,0.0013525307],"domain_scores_gemma":[0.9811352,0.012452944,0.0015898572,0.00036485775,0.0013260224,0.0031311817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012607124,0.0009157407,0.0013262099,0.0020634327,0.011536096,0.0050728675,0.0019242925,0.0026843422,0.003925845],"category_scores_gemma":[0.022418689,0.0011484645,0.00076361565,0.0016990385,0.009460116,0.0068094856,0.0064425967,0.0059541725,0.0003730779],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017289967,0.000076234115,0.0029315443,0.00009087843,0.000005036065,0.0007258594,0.9934143,0.0000090630765,0.00018963248,0.00041437062,0.0004916692,0.0016341769],"study_design_scores_gemma":[0.000004678854,0.000050838324,0.0006929039,0.00009957439,0.0000045006313,0.00027872532,0.99462044,0.00002092063,0.0000722096,0.00009944751,0.0040484527,0.0000072764947],"about_ca_topic_score_codex":0.01009934,"about_ca_topic_score_gemma":0.017665204,"teacher_disagreement_score":0.012607124,"about_ca_system_score_codex":0.005610686,"about_ca_system_score_gemma":0.0079924585,"threshold_uncertainty_score":0.06667364},"labels":[],"label_agreement":null},{"id":"W2133751849","doi":"10.1136/amiajnl-2012-001009","title":"Privacy protection and public goods: building a genetic database for health research in Newfoundland and Labrador","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland; Office of the Privacy Commissioner of Canada; Privacy Analytics (Canada); Privy Council Office","funders":"","keywords":"Population; Legitimacy; Legislature; Political science; Legislation; Research ethics; Statute; Analytics; Information privacy; Database; Internet privacy; Public administration; Law; Politics; Medicine; Computer science; Environmental health","score_opus":0.3895310686644282,"score_gpt":0.5672345529178378,"score_spread":0.17770348425340954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133751849","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":[0.31375068,0.0075904597,0.20508927,0.26602492,0.0010461187,0.0036230884,0.033390682,0.0061947308,0.16329005],"genre_scores_gemma":[0.48973858,0.004234954,0.435752,0.013381245,0.00042005908,0.0017837031,0.019033607,0.0005946922,0.03506113],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98247564,0.008390933,0.0018940737,0.0021759775,0.0029517415,0.0021115495],"domain_scores_gemma":[0.90705264,0.0354047,0.012585204,0.019328507,0.018286087,0.0073428587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030237865,0.00027712376,0.0004919654,0.0051627476,0.007390536,0.011216486,0.0039898595,0.0016793957,0.004793786],"category_scores_gemma":[0.04567558,0.00066776265,0.00050437247,0.007426397,0.006181045,0.005450412,0.0067886044,0.0018061746,0.0008683661],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040000153,0.00028738886,0.17365345,0.0005614986,0.00017667646,0.0032639722,0.023540637,0.009766532,0.0029798236,0.18018165,0.20988782,0.39530054],"study_design_scores_gemma":[0.00015872977,0.0001330529,0.16791652,0.001817553,0.00015045126,0.0013612863,0.023175586,0.007267928,0.003322735,0.021088073,0.7733026,0.00030549956],"about_ca_topic_score_codex":0.85649043,"about_ca_topic_score_gemma":0.9026034,"teacher_disagreement_score":0.14350957,"about_ca_system_score_codex":0.05570654,"about_ca_system_score_gemma":0.13156512,"threshold_uncertainty_score":0.40418112},"labels":[],"label_agreement":null},{"id":"W2135536555","doi":"10.1136/amiajnl-2012-001075","title":"MEDLINE clinical queries are robust when searching in recent publishing years","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hamilton Health Sciences; Western University; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Publishing; Computer science; MEDLINE; Information retrieval; Data science; World Wide Web; Political science","score_opus":0.037338715332168856,"score_gpt":0.3358608361451467,"score_spread":0.29852212081297785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135536555","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":[0.2909411,0.27738246,0.067685604,0.057507392,0.0071213767,0.02087024,0.16085255,0.006960339,0.11067901],"genre_scores_gemma":[0.6475356,0.074062556,0.13160819,0.020710023,0.0068295966,0.0137824165,0.09779893,0.0021089127,0.0055638035],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.65150666,0.104211375,0.16147627,0.016343981,0.0631271,0.00333467],"domain_scores_gemma":[0.18756579,0.57273984,0.11744804,0.043498527,0.07574671,0.003001162],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19002537,0.0013916866,0.004516065,0.063513495,0.0016785398,0.017866543,0.00405662,0.004032583,0.008193596],"category_scores_gemma":[0.715902,0.0016081089,0.0027608513,0.069403015,0.0028768675,0.020071957,0.006579086,0.0015740418,0.0057723653],"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":[0.0030949835,0.0002843847,0.19238861,0.09678664,0.00376395,0.0019247197,0.01206114,0.0012225969,0.0057606115,0.007929099,0.13851503,0.5362683],"study_design_scores_gemma":[0.0010329823,0.0013728,0.4166438,0.07729415,0.008254598,0.007985599,0.010828896,0.0041576833,0.007560257,0.02116663,0.4427381,0.0009645985],"about_ca_topic_score_codex":0.0028735048,"about_ca_topic_score_gemma":0.006112793,"teacher_disagreement_score":0.8099746,"about_ca_system_score_codex":0.0030840323,"about_ca_system_score_gemma":0.008690496,"threshold_uncertainty_score":0.9988429},"labels":[],"label_agreement":null},{"id":"W2135611962","doi":"10.1136/jamia.2010.003210","title":"Developing syndrome definitions based on consensus and current use","year":2010,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine","keywords":"Computer science; Consensus conference; Ontology; MEDLINE; Data science; Medicine","score_opus":0.029499667205239537,"score_gpt":0.3065207395399176,"score_spread":0.27702107233467804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135611962","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":[0.040297564,0.0020817085,0.9232124,0.004049372,0.0006112559,0.0035964134,0.003276168,0.0012063382,0.021668795],"genre_scores_gemma":[0.10186252,0.0010645922,0.8827603,0.0005195424,0.00011843768,0.00369421,0.0085504055,0.00023692861,0.0011930829],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9166766,0.03085886,0.026710639,0.0072694966,0.01743472,0.0010497125],"domain_scores_gemma":[0.8404038,0.06625909,0.013968191,0.016108405,0.06173954,0.0015210772],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07755487,0.0013153001,0.0014818967,0.017506555,0.002541626,0.0060161203,0.0051897597,0.0018267568,0.0031821162],"category_scores_gemma":[0.19921163,0.00077333703,0.0028993217,0.009644012,0.0027774242,0.012305346,0.00841901,0.0023432525,0.0010511515],"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":[0.00031642054,0.0002794146,0.066578284,0.0076252413,0.0005931794,0.0013982236,0.030352984,0.013812948,0.005617267,0.27370518,0.03735598,0.5623649],"study_design_scores_gemma":[0.00032284975,0.0007470328,0.03865074,0.014937248,0.0012511691,0.0040473216,0.03252418,0.080527134,0.017136434,0.29534155,0.51390046,0.0006138323],"about_ca_topic_score_codex":0.005328269,"about_ca_topic_score_gemma":0.005599096,"teacher_disagreement_score":0.9224451,"about_ca_system_score_codex":0.004691672,"about_ca_system_score_gemma":0.0123958625,"threshold_uncertainty_score":0.41015422},"labels":[],"label_agreement":null},{"id":"W2137422265","doi":"10.1136/amiajnl-2011-000560","title":"Multidimensional evaluation of a radio frequency identification wi-fi location tracking system in an acute-care hospital setting","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Alberta Health Services; University of Calgary","funders":"Canada Research Chairs; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Real-time locating system; Radio-frequency identification; Tracking (education); Computer science; Situation awareness; Identification (biology); Health care; Scale (ratio); Telemedicine; Tracking system; Asset (computer security); Real-time computing; Artificial intelligence; Computer security; Engineering; Cartography; Geography","score_opus":0.030138397345241236,"score_gpt":0.4191385344110653,"score_spread":0.38900013706582404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137422265","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":[0.99830174,0.00003647777,0.0009929545,0.00005148995,0.0000047507674,0.00014415341,0.000050786854,0.000023417013,0.0003943089],"genre_scores_gemma":[0.9954058,0.00006646228,0.004147353,0.000038849303,0.0000067821775,0.00006994382,0.00007099604,0.000004410138,0.00018954821],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99437356,0.0038468647,0.0005064494,0.00021846703,0.0008176622,0.00023701122],"domain_scores_gemma":[0.9888896,0.0059210784,0.0011676733,0.00053450064,0.0028539042,0.00063334027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055556255,0.0004481451,0.00039924565,0.00082179136,0.0005296483,0.0010508648,0.00057798205,0.00058710435,0.001017205],"category_scores_gemma":[0.017019548,0.00021646562,0.0003470727,0.0005370685,0.00047310983,0.00090032833,0.0010037252,0.00033956795,0.00024369829],"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":[0.013757259,0.016269198,0.5020771,0.003253906,0.0004740364,0.003408624,0.025327215,0.028159082,0.093498446,0.00092119817,0.002051888,0.310802],"study_design_scores_gemma":[0.0011744611,0.11714187,0.712517,0.0004241739,0.00067526626,0.0022049223,0.034135185,0.06947846,0.05572367,0.0004006255,0.0057123415,0.00041196728],"about_ca_topic_score_codex":0.002568172,"about_ca_topic_score_gemma":0.0034950154,"teacher_disagreement_score":0.0055556255,"about_ca_system_score_codex":0.0010490288,"about_ca_system_score_gemma":0.00070369506,"threshold_uncertainty_score":0.029381335},"labels":[],"label_agreement":null},{"id":"W2137967128","doi":"10.1197/jamia.m2606","title":"A Randomized Trial of the Effectiveness of On-demand versus Computer-triggered Drug Decision Support in Primary Care","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Randomized controlled trial; Medical prescription; Clinical decision support system; Decision support system; Drug; Personalization; Medical emergency; Cluster (spacecraft); Cluster randomised controlled trial; Emergency medicine; Intensive care medicine; Data mining; Computer science; Internal medicine; Nursing; Psychiatry","score_opus":0.02006016552868641,"score_gpt":0.3752577330274715,"score_spread":0.3551975674987851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137967128","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":[0.99265796,0.0008943395,0.00040471533,0.0004306277,0.0005852139,0.003507669,0.00035541353,0.000076596916,0.0010875463],"genre_scores_gemma":[0.99126375,0.00044870173,0.0015074894,0.00053753186,0.00060195837,0.00413644,0.00031002122,0.000013044268,0.0011810497],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9953492,0.002884994,0.00028484897,0.0008150206,0.00026217304,0.00040369615],"domain_scores_gemma":[0.9888805,0.005661893,0.0021491495,0.0008287745,0.00039468412,0.0020850685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035271337,0.002186449,0.0037763603,0.00068038807,0.00062101334,0.0013443186,0.0016664015,0.003574693,0.010355918],"category_scores_gemma":[0.009456519,0.0011828622,0.0021672186,0.00052072125,0.0025843151,0.0017774319,0.0009408843,0.0035627696,0.0009521414],"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":[0.985974,0.008989959,0.00032839508,0.00015570088,0.0003011928,0.000012563195,0.000023378763,0.00015839009,0.00023305004,0.000038401053,0.0001359024,0.003648916],"study_design_scores_gemma":[0.90593094,0.09170622,0.0011097488,0.000021205544,0.00021374013,0.000010655702,0.000018061717,0.000570161,0.00019442913,0.00011953783,0.000095843534,0.000009429766],"about_ca_topic_score_codex":0.0018065083,"about_ca_topic_score_gemma":0.0016868697,"teacher_disagreement_score":0.010355918,"about_ca_system_score_codex":0.0016168222,"about_ca_system_score_gemma":0.0018045943,"threshold_uncertainty_score":0.034644008},"labels":[],"label_agreement":null},{"id":"W2139113130","doi":"10.1136/amiajnl-2014-002825","title":"Effects of librarian-provided services in healthcare settings: a systematic review","year":2014,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; University of Toronto; St. Michael's Hospital","funders":"","keywords":"CINAHL; Health care; Inclusion (mineral); MEDLINE; Cochrane Library; Medicine; Medical education; Health professionals; Grey literature; Intervention (counseling); Nursing; Family medicine; Psychological intervention; Psychology; Alternative medicine","score_opus":0.033895253397748584,"score_gpt":0.4627500756711298,"score_spread":0.42885482227338123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139113130","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005265653,0.9936138,0.00008058446,0.00023561182,0.00007647103,0.0002673804,0.0001696686,0.0000078259,0.0002830644],"genre_scores_gemma":[0.08048327,0.9157901,0.0013667285,0.0008865519,0.00012969422,0.00097221654,0.00024245048,0.000008429571,0.00012056259],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9765569,0.012014191,0.006222032,0.0012795351,0.003447498,0.00047990168],"domain_scores_gemma":[0.9022755,0.07344658,0.017698249,0.0009707759,0.0042633843,0.0013455002],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013064696,0.0011044409,0.0075157257,0.009121939,0.00088960433,0.0035329673,0.0020198596,0.001900628,0.0055992045],"category_scores_gemma":[0.08258172,0.001006346,0.0059585604,0.0138234645,0.0011976549,0.0033701134,0.0019248208,0.0015667739,0.00029740107],"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":[0.0011666134,0.0001672407,0.003205132,0.91303635,0.0148397265,0.000113930626,0.0005749157,0.00007296972,0.00010613811,0.00013312373,0.00094350526,0.06564052],"study_design_scores_gemma":[0.0020931147,0.0016793606,0.01864663,0.8623711,0.09858516,0.00070482533,0.0016523812,0.00013983078,0.0003809443,0.00024142099,0.013422719,0.00008250977],"about_ca_topic_score_codex":0.0057774275,"about_ca_topic_score_gemma":0.017272336,"teacher_disagreement_score":0.9869353,"about_ca_system_score_codex":0.0047363173,"about_ca_system_score_gemma":0.0116271945,"threshold_uncertainty_score":0.069093525},"labels":[],"label_agreement":null},{"id":"W2139808481","doi":"10.1136/amiajnl-2011-000793","title":"Application of change point analysis to daily influenza-like illness emergency department visits","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Centers for Disease Control and Prevention","keywords":"Emergency department; Pandemic; Influenza pandemic; Medicine; Influenza-like illness; Medical emergency; Emergency medicine; Coronavirus disease 2019 (COVID-19); Disease monitoring; Health care; Disease; Intensive care medicine; Infectious disease (medical specialty); Virology; Internal medicine; Nursing; Virus","score_opus":0.017168203468145692,"score_gpt":0.3239154762609535,"score_spread":0.30674727279280783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139808481","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":[0.7609845,0.0005336672,0.23424678,0.0003302115,0.00009503454,0.00050456275,0.0010021077,0.00073797343,0.0015651754],"genre_scores_gemma":[0.9373777,0.00009636566,0.061412275,0.000047533253,0.000041508378,0.00017609198,0.0006271617,0.00003277607,0.00018867066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9933281,0.0042559616,0.00034016324,0.00088427187,0.0010320247,0.000159468],"domain_scores_gemma":[0.96226937,0.030006895,0.0024279275,0.0015058495,0.003295018,0.00049492123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075823814,0.0007208507,0.0008417704,0.004544213,0.0003752307,0.0009300772,0.0009514119,0.0007865484,0.0009203544],"category_scores_gemma":[0.044505306,0.00030492654,0.0010474304,0.0028581715,0.0004128601,0.0006739988,0.000859921,0.0012246199,0.00022687111],"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":[0.0013399852,0.0007697357,0.5379229,0.00029675872,0.0013861574,0.0005540134,0.00085709296,0.17224275,0.0029908665,0.001867808,0.0022421146,0.27752972],"study_design_scores_gemma":[0.00006292384,0.00062613026,0.10912314,0.000025545685,0.0001185613,0.00025858212,0.00025856486,0.8847036,0.0013710443,0.002449207,0.0009585651,0.000044199136],"about_ca_topic_score_codex":0.010887805,"about_ca_topic_score_gemma":0.005392815,"teacher_disagreement_score":0.010887805,"about_ca_system_score_codex":0.0008206016,"about_ca_system_score_gemma":0.0010920288,"threshold_uncertainty_score":0.04009992},"labels":[],"label_agreement":null},{"id":"W2140055604","doi":"10.1136/amiajnl-2011-000100","title":"A secure protocol for protecting the identity of providers when disclosing data for disease surveillance","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; Ontario Institute for Cancer Research; McGill University; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"National Institute for Health and Care Research; Canadian Institutes of Health Research; Ontario Institute for Cancer Research; National Institutes of Health; U.S. National Library of Medicine; Natural Resources Canada; National Health Research Institutes","keywords":"Paillier cryptosystem; Protocol (science); Confidentiality; Computer science; Computer security; Masking (illustration); Public health; Patient privacy; Internet privacy; Cryptosystem; Medicine; Health care; Cryptography; Nursing","score_opus":0.05889504255153467,"score_gpt":0.3667707181943421,"score_spread":0.30787567564280743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140055604","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":[0.021189215,0.00005185398,0.97403866,0.0007521196,0.00006472247,0.0010753943,0.00013864701,0.0008512316,0.0018382123],"genre_scores_gemma":[0.33540246,0.00011621091,0.6597018,0.00027602393,0.00010527405,0.0018512311,0.00037319993,0.0000973636,0.0020764978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9843982,0.0075193886,0.0020167015,0.0012371469,0.0042434554,0.0005852005],"domain_scores_gemma":[0.9555109,0.022125037,0.004716491,0.011969096,0.0046945447,0.0009839956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016767194,0.00053748,0.00057405554,0.0011380418,0.0015463679,0.0026592833,0.0015688324,0.0016964088,0.0024278874],"category_scores_gemma":[0.044062044,0.0006366922,0.00092641305,0.0008562012,0.0028527076,0.0041506602,0.0045077377,0.0023058974,0.0011543884],"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":[0.0027089864,0.0011320458,0.016163578,0.00086812687,0.00032961761,0.0018934546,0.005225534,0.06192411,0.077189416,0.45679575,0.015786368,0.3599831],"study_design_scores_gemma":[0.0019414899,0.002014577,0.0034441212,0.0003312746,0.00028437693,0.0028291794,0.0007675988,0.63848376,0.116515286,0.1738248,0.059288528,0.00027493844],"about_ca_topic_score_codex":0.0009236993,"about_ca_topic_score_gemma":0.00048349312,"teacher_disagreement_score":0.016767194,"about_ca_system_score_codex":0.0015010951,"about_ca_system_score_gemma":0.0057500694,"threshold_uncertainty_score":0.088674426},"labels":[],"label_agreement":null},{"id":"W2140749571","doi":"10.1136/amiajnl-2011-000678","title":"Intensive care unit nurses' information needs and recommendations for integrated displays to improve nurses' situation awareness","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nursing; Intensive care unit; Unit (ring theory); Medical emergency; Medical education; Medicine; Psychology; Intensive care medicine","score_opus":0.022994955722224434,"score_gpt":0.3612348780998635,"score_spread":0.33823992237763906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140749571","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":[0.84798324,0.011018638,0.030396271,0.08733461,0.0004644209,0.002713265,0.00060720835,0.0010304198,0.018452032],"genre_scores_gemma":[0.88964117,0.0066005695,0.09513565,0.0040390166,0.00010234374,0.0021647552,0.0006028175,0.00004683699,0.0016668644],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.98757356,0.00682764,0.0017924791,0.00049629994,0.0026264396,0.0006835944],"domain_scores_gemma":[0.9272757,0.04699148,0.0072727734,0.0015259493,0.013061408,0.003872688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017272497,0.0005306724,0.00057394995,0.0018518646,0.0017670762,0.0031248766,0.0020058001,0.0024285747,0.0027165324],"category_scores_gemma":[0.08865799,0.00048463026,0.0007832294,0.0013543939,0.00078134076,0.006407396,0.002607909,0.0015889162,0.00046585343],"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":[0.0006113442,0.0028350465,0.11065233,0.011870324,0.00014727881,0.0009585704,0.086064465,0.0024125015,0.0038198924,0.0020888112,0.02626461,0.7522748],"study_design_scores_gemma":[0.0008066216,0.005891032,0.36558357,0.046270877,0.00085837033,0.0018281181,0.42489383,0.02656306,0.008308006,0.017114885,0.101362,0.00051964255],"about_ca_topic_score_codex":0.0035692388,"about_ca_topic_score_gemma":0.0043760682,"teacher_disagreement_score":0.017272497,"about_ca_system_score_codex":0.0024651869,"about_ca_system_score_gemma":0.008039677,"threshold_uncertainty_score":0.0913468},"labels":[],"label_agreement":null},{"id":"W2142141579","doi":"10.1093/jamia/ocu047","title":"Technology-mediated interventions for enhancing medication adherence","year":2015,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; McMaster University; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Psychological intervention; Intervention (counseling); Medication adherence; MEDLINE; Alternative medicine; Family medicine; Intensive care medicine; Physical therapy; Nursing; Internal medicine; Pathology","score_opus":0.0877252042943465,"score_gpt":0.4321631382966105,"score_spread":0.344437934002264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142141579","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":[0.0062200427,0.9845711,0.0011595059,0.0016052098,0.00036632232,0.00050600717,0.0001868676,0.00004234635,0.0053426507],"genre_scores_gemma":[0.08975548,0.89689523,0.008769553,0.0017035839,0.00049488875,0.0014341742,0.00016706772,0.0000103760785,0.0007696014],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9908454,0.006023686,0.0011475632,0.00037601052,0.0014487694,0.00015865237],"domain_scores_gemma":[0.96929854,0.027382957,0.0022375335,0.00030901865,0.00064119615,0.00013077586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068944455,0.00073880795,0.0016355319,0.0049098535,0.00041341345,0.0018406573,0.00095633813,0.001551553,0.008106671],"category_scores_gemma":[0.03303907,0.00030484196,0.0033166856,0.0031301961,0.0006388747,0.0016734034,0.0011195766,0.0013214981,0.00034829878],"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":[0.0004482408,0.00043129042,0.0018423848,0.3109558,0.0038827192,0.00009564959,0.00069231016,0.0005011862,0.0004863407,0.0035753853,0.0035702998,0.6735185],"study_design_scores_gemma":[0.0022319676,0.00302452,0.025335317,0.78710693,0.024589086,0.0009922176,0.0009884523,0.0010877742,0.0027334047,0.0056224796,0.14618592,0.00010200208],"about_ca_topic_score_codex":0.0014577417,"about_ca_topic_score_gemma":0.002528908,"teacher_disagreement_score":0.008106671,"about_ca_system_score_codex":0.0014509679,"about_ca_system_score_gemma":0.0027904662,"threshold_uncertainty_score":0.03646171},"labels":[],"label_agreement":null},{"id":"W2142459005","doi":"10.1197/jamia.m2297","title":"Response to Corrao et al.: Improving Efficacy of PubMed Clinical Queries for Retrieving Scientifically Strong Studies on Treatment","year":2007,"lang":"en","type":"letter","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"U.S. National Library of Medicine","keywords":"Computer science; Information retrieval; MEDLINE; Chemistry","score_opus":0.5819441063789622,"score_gpt":0.5653746592277806,"score_spread":0.016569447151181582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142459005","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037840026,0.0006566959,0.00013932254,0.98793656,0.009682414,0.00007373027,0.00017827359,0.000059097558,0.00089549914],"genre_scores_gemma":[0.0022107712,0.0007048469,0.0006096546,0.984082,0.010363555,0.00016130683,0.000059548052,0.00003858673,0.0017697711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9820386,0.0051148986,0.00433331,0.0017120759,0.005952605,0.0008485702],"domain_scores_gemma":[0.87647825,0.07700506,0.006219302,0.0026305667,0.032570224,0.005096618],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024592,0.0009959228,0.0025761935,0.00224014,0.004251598,0.005135251,0.003883705,0.048728798,0.01133715],"category_scores_gemma":[0.1548359,0.0011622144,0.0022328289,0.0026614643,0.0030598538,0.004154762,0.0021390854,0.029419208,0.008935501],"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":[0.00012968284,0.000039310667,0.000520231,0.00024998907,0.000034665183,0.00044746898,0.0001491458,0.000052749525,0.00015373791,0.0007618131,0.9879038,0.009557546],"study_design_scores_gemma":[0.00068119954,0.00036723784,0.0041053607,0.0019765054,0.00019560257,0.0035669028,0.0015142055,0.0014632705,0.00070240704,0.0049604066,0.9802256,0.00024124065],"about_ca_topic_score_codex":0.007522042,"about_ca_topic_score_gemma":0.009936629,"teacher_disagreement_score":0.975408,"about_ca_system_score_codex":0.0113345375,"about_ca_system_score_gemma":0.007999115,"threshold_uncertainty_score":0.13005644},"labels":[],"label_agreement":null},{"id":"W2144316471","doi":"10.1136/amiajnl-2014-002768","title":"A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Pulmonary embolism; Deep vein; Internal medicine; Electronic health record; Venous thrombosis; Receiver operating characteristic; Venous thromboembolism; Thrombosis; Predictive value; Surgery; Health care","score_opus":0.027108456405946953,"score_gpt":0.3615146955703141,"score_spread":0.3344062391643672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144316471","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":[0.31734714,0.0019138033,0.6639061,0.0013183686,0.000348406,0.0010745691,0.006621501,0.0047385925,0.002731515],"genre_scores_gemma":[0.59493655,0.00039980077,0.40014446,0.00028855816,0.00021625409,0.00056369015,0.002731374,0.00006708592,0.0006522316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99260944,0.002160177,0.0011909233,0.0020064448,0.001840873,0.0001921337],"domain_scores_gemma":[0.9638689,0.024022216,0.0046151932,0.002190548,0.004972523,0.00033070124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009369848,0.0009706985,0.0011588291,0.005444674,0.0004612801,0.0019850235,0.0011024694,0.0014742517,0.0012319689],"category_scores_gemma":[0.043256838,0.00035843646,0.001226132,0.00270573,0.00046674686,0.0027011866,0.0010052889,0.0010253144,0.0010201819],"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":[0.0008768724,0.00085995725,0.3195753,0.0008997495,0.00078466174,0.0004431016,0.0005420161,0.014439924,0.014281136,0.0015761607,0.007291914,0.6384292],"study_design_scores_gemma":[0.00021971343,0.0008256626,0.16820405,0.00028994624,0.00041964345,0.0031126142,0.00034787392,0.8021084,0.012196679,0.005022803,0.007070158,0.00018238366],"about_ca_topic_score_codex":0.0023558089,"about_ca_topic_score_gemma":0.0043492196,"teacher_disagreement_score":0.009369848,"about_ca_system_score_codex":0.0007705909,"about_ca_system_score_gemma":0.0014537174,"threshold_uncertainty_score":0.049553096},"labels":[],"label_agreement":null},{"id":"W2144892760","doi":"10.1136/amiajnl-2011-000105","title":"Personal health records: a scoping review","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":605,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Medical record; Health records; Electronic medical record; Electronic health record; Health care; Medical emergency; Nursing","score_opus":0.12404089687551045,"score_gpt":0.5225281940826884,"score_spread":0.3984872972071779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144892760","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":[0.00013970882,0.9985084,0.0001766341,0.00023460502,0.00008031032,0.00012585473,0.000115946124,0.000008007046,0.0006106294],"genre_scores_gemma":[0.0012561809,0.9976337,0.00048717944,0.00015493865,0.00003651174,0.00014619496,0.00013588741,0.0000035522917,0.00014581649],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9927644,0.0021349515,0.00242972,0.0004676265,0.0020135483,0.00018971185],"domain_scores_gemma":[0.9606295,0.028902724,0.004272011,0.0007392835,0.0051196003,0.00033691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01033927,0.0015360826,0.0046425182,0.018378075,0.0009964254,0.002960991,0.0025767828,0.002719631,0.008612066],"category_scores_gemma":[0.03934282,0.0011347277,0.0033236614,0.024937151,0.0012895893,0.0044438844,0.0020326048,0.0014539805,0.0017209625],"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":[0.00009151448,0.000047103127,0.00046371133,0.5509446,0.00085467845,0.00018692756,0.00031545843,0.00017076364,0.00016092908,0.0010019428,0.012679025,0.43308333],"study_design_scores_gemma":[0.000051402767,0.000081803126,0.0017263843,0.8460318,0.003104235,0.00062865467,0.00036599193,0.0000870129,0.00022649209,0.0007007631,0.14696689,0.000028514476],"about_ca_topic_score_codex":0.008307171,"about_ca_topic_score_gemma":0.014589679,"teacher_disagreement_score":0.018378075,"about_ca_system_score_codex":0.0034813262,"about_ca_system_score_gemma":0.013717687,"threshold_uncertainty_score":0.05467999},"labels":[],"label_agreement":null},{"id":"W2145361723","doi":"10.1197/jamia.m2902","title":"Evaluating Predictors of Geographic Area Population Size Cut-offs to Manage Re-identification Risk","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Public Health Agency of Canada; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Geographic information system; Identification (biology); Location; Geography; Population; GIS and public health; Cartography; Environmental health; Medicine","score_opus":0.018686306430003848,"score_gpt":0.317223060104526,"score_spread":0.29853675367452215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145361723","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":[0.9535843,0.0007881225,0.04018953,0.0018511927,0.000075224365,0.0003855265,0.00096323784,0.00031730498,0.0018454375],"genre_scores_gemma":[0.9854047,0.00007913417,0.013568723,0.00007821775,0.000018003488,0.00012381896,0.00057095103,0.00001970493,0.00013688086],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908512,0.0062672044,0.00058050203,0.0007636172,0.0010624417,0.00047486293],"domain_scores_gemma":[0.825757,0.14890063,0.011710322,0.0036025175,0.007297524,0.0027319933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031871226,0.0010383258,0.00090981845,0.0032811752,0.00085358514,0.0018392735,0.0013179114,0.0011851145,0.0017089987],"category_scores_gemma":[0.15138505,0.00039064055,0.0012540062,0.0025739365,0.00085447094,0.0021634847,0.0014498767,0.0018942497,0.0003061873],"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":[0.00034526057,0.00021809916,0.95546913,0.000058300684,0.00020595621,0.000058731384,0.00023971788,0.018033758,0.000083960025,0.00061758026,0.0015910243,0.023078378],"study_design_scores_gemma":[0.0001457518,0.0009874459,0.44757795,0.0002761728,0.0003711588,0.00022674767,0.0017099946,0.5395467,0.0014872794,0.006096871,0.0014908916,0.000083061765],"about_ca_topic_score_codex":0.012155445,"about_ca_topic_score_gemma":0.010917084,"teacher_disagreement_score":0.031871226,"about_ca_system_score_codex":0.0014635525,"about_ca_system_score_gemma":0.003484075,"threshold_uncertainty_score":0.16855317},"labels":[],"label_agreement":null},{"id":"W2147784953","doi":"10.1136/amiajnl-2011-000371","title":"Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Programs for Assessment of Technology in Health Research Institute; McMaster University; Centre for Health Evaluation and Outcome Sciences; St. Joseph’s Healthcare Hamilton","funders":"","keywords":"Medicine; Cost effectiveness; Medical record; Health care; Clinical decision support system; Intervention (counseling); Univariate; Cost–benefit analysis; Randomized controlled trial; Decision support system; Physical therapy; Computer science; Risk analysis (engineering); Statistics; Data mining; Nursing; Surgery; Mathematics","score_opus":0.0576462264103988,"score_gpt":0.41541914868998436,"score_spread":0.35777292227958557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147784953","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":[0.9880993,0.0012893555,0.004212951,0.00085219723,0.00006371755,0.0004914605,0.0013094438,0.000086562024,0.0035950826],"genre_scores_gemma":[0.9978898,0.00016989234,0.0013408161,0.00004602458,0.0000074615446,0.000085181135,0.00022463997,0.0000027395379,0.00023334213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.995589,0.00310802,0.00020839807,0.00030589648,0.0005237004,0.00026486316],"domain_scores_gemma":[0.98556364,0.011688083,0.0012576634,0.00034305436,0.00068607996,0.00046137706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062909396,0.0009855105,0.0008169315,0.0009613833,0.000315445,0.0012674719,0.0010938331,0.0011142625,0.0033762178],"category_scores_gemma":[0.025819313,0.00043207448,0.0019826358,0.00096631085,0.000566489,0.0013725478,0.00092291104,0.0007907889,0.00011981123],"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":[0.028137628,0.003509848,0.05339379,0.0018298171,0.0030008524,0.00027862383,0.00019326065,0.8114676,0.0010669576,0.0029759256,0.0019824381,0.09216329],"study_design_scores_gemma":[0.00642164,0.012894432,0.050211288,0.0004243433,0.004067017,0.00019665486,0.00023619634,0.9198765,0.0017032059,0.0023984935,0.0014489383,0.00012120826],"about_ca_topic_score_codex":0.09718585,"about_ca_topic_score_gemma":0.044996984,"teacher_disagreement_score":0.09718585,"about_ca_system_score_codex":0.0135954255,"about_ca_system_score_gemma":0.0047507314,"threshold_uncertainty_score":0.19324034},"labels":[],"label_agreement":null},{"id":"W2149291998","doi":"10.1197/jamia.m2563","title":"Impact of Research-based Synopses Delivered as Daily E-mail: A Prospective Observational Study","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Social Media in Health Education","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"College of Family Physicians of Canada; McGill University","funders":"","keywords":"Observational study; Computer science; Medicine; Internal medicine","score_opus":0.15393561496741492,"score_gpt":0.5228881553771285,"score_spread":0.3689525404097136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149291998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986985,0.00022044097,0.000180413,0.000050657534,0.0000068953696,0.00010273645,0.0003260113,0.0000076612105,0.00040673488],"genre_scores_gemma":[0.99849343,0.00026836008,0.00035679515,0.00007876728,0.000025989935,0.00013055549,0.00045043553,0.0000059851286,0.00018975812],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99447787,0.0027434838,0.0008387945,0.0004298957,0.0011338884,0.0003759987],"domain_scores_gemma":[0.9714802,0.009341755,0.012010378,0.0018260359,0.0023804826,0.0029611688],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0052230535,0.000462427,0.00064134976,0.0014576196,0.00078581227,0.0012373887,0.00046506696,0.0007823294,0.0014045681],"category_scores_gemma":[0.025042417,0.00057035487,0.00078199105,0.0015854654,0.0005003948,0.0014667406,0.0008977191,0.0015163334,0.00048304678],"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":[0.00036895915,0.0011244703,0.99173146,0.00009204384,0.00011116062,0.00015445225,0.001346795,0.000056627767,0.00021094678,0.000023297085,0.00022060973,0.0045591895],"study_design_scores_gemma":[0.00005787434,0.0036935573,0.993054,0.000062889376,0.00008119427,0.0002727396,0.001759166,0.00027927855,0.00015006395,0.000037587393,0.0005238241,0.000027888344],"about_ca_topic_score_codex":0.004006507,"about_ca_topic_score_gemma":0.003964256,"teacher_disagreement_score":0.99477696,"about_ca_system_score_codex":0.0006538916,"about_ca_system_score_gemma":0.00094149035,"threshold_uncertainty_score":0.027622461},"labels":[],"label_agreement":null},{"id":"W2149995297","doi":"10.1136/amiajnl-2012-001422","title":"Measuring value for money: a scoping review on economic evaluation of health information systems","year":2013,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Canadian Institutes of Health Research","keywords":"Documentation; Economic evaluation; Health information exchange; Information system; Health care; Computer science; Value for money; Knowledge management; Medicine; Actuarial science; Health information; Business; Economics; Engineering","score_opus":0.21050013463414075,"score_gpt":0.5173681594651649,"score_spread":0.30686802483102416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149995297","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":[0.0013836056,0.9924529,0.0012703225,0.0017617936,0.00040083632,0.001079652,0.00025006046,0.000012870789,0.0013879411],"genre_scores_gemma":[0.034976684,0.9551558,0.0045312033,0.0012548885,0.00036299994,0.0031921263,0.00030403686,0.000024483119,0.00019773569],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.8299183,0.10298869,0.035740487,0.0030941851,0.026250172,0.002008174],"domain_scores_gemma":[0.4490189,0.48825803,0.0325027,0.00451235,0.024691943,0.0010160591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1554259,0.0036333639,0.013088235,0.029779809,0.0019719265,0.012760647,0.0037744916,0.0054721306,0.005735944],"category_scores_gemma":[0.44931015,0.0022852144,0.014886204,0.027536336,0.0033442972,0.009515548,0.0043680146,0.0048014815,0.00045005162],"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":[0.0008953632,0.00011637747,0.0013973891,0.7771459,0.01721534,0.00011810165,0.0005801954,0.0013906109,0.00010699735,0.005878539,0.003643889,0.19151126],"study_design_scores_gemma":[0.00024996116,0.00028988926,0.002132723,0.9530969,0.02407415,0.00015731122,0.0003712281,0.00053051213,0.00022671877,0.003721565,0.015091152,0.000057900987],"about_ca_topic_score_codex":0.006638908,"about_ca_topic_score_gemma":0.010081836,"teacher_disagreement_score":0.1554259,"about_ca_system_score_codex":0.019717233,"about_ca_system_score_gemma":0.024648944,"threshold_uncertainty_score":0.8219806},"labels":[],"label_agreement":null},{"id":"W2152560313","doi":"10.1197/jamia.m1700","title":"The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review","year":2005,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":909,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre; University of Toronto","funders":"","keywords":"Documentation; Medicine; Health records; Electronic health record; Health care; Medical emergency; Computer science","score_opus":0.02206268442127346,"score_gpt":0.4683611891254321,"score_spread":0.44629850470415866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152560313","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":[0.002805015,0.99528414,0.00029953278,0.00034933805,0.00014560882,0.00042758344,0.00037743716,0.000009318097,0.00030194945],"genre_scores_gemma":[0.03245054,0.9639614,0.0015572968,0.00054321706,0.00009519024,0.000998131,0.00026582138,0.00000848508,0.00011987702],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96471167,0.01253926,0.013702629,0.0017449395,0.0067229737,0.0005784864],"domain_scores_gemma":[0.8729819,0.09640118,0.018429069,0.0015436519,0.009843249,0.0008009315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02538091,0.0015473142,0.007411228,0.017582482,0.00093823223,0.003873032,0.0020252187,0.0020506333,0.0022704147],"category_scores_gemma":[0.1150136,0.0014008918,0.0071061957,0.01869656,0.0012392044,0.0038009176,0.0020348663,0.0012022915,0.00022999235],"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":[0.00017249087,0.000028642733,0.001502262,0.9418016,0.007344717,0.00012934984,0.00039304857,0.00012213181,0.00012342566,0.00018570045,0.0008515566,0.047345124],"study_design_scores_gemma":[0.00016416312,0.0002571172,0.005685039,0.93620265,0.045629703,0.00036779392,0.00058272807,0.00010145745,0.00023173353,0.00019902612,0.0105417045,0.00003677434],"about_ca_topic_score_codex":0.009150169,"about_ca_topic_score_gemma":0.0250045,"teacher_disagreement_score":0.02538091,"about_ca_system_score_codex":0.0063167643,"about_ca_system_score_gemma":0.021433877,"threshold_uncertainty_score":0.1342287},"labels":[],"label_agreement":null},{"id":"W2152587002","doi":"10.1136/amiajnl-2010-000033","title":"Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":134,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Engineering and Physical Sciences Research Council","keywords":"Workflow; Computer science; Process (computing); Process management; Information system; Health care; Data science; Knowledge management; Engineering; Database; Political science","score_opus":0.1502176086215256,"score_gpt":0.5017520037620948,"score_spread":0.3515343951405692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152587002","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":[0.00025022047,0.9956483,0.0019165474,0.0013474587,0.00008904825,0.000039507722,0.000012237673,0.000009478129,0.00068714167],"genre_scores_gemma":[0.005188531,0.98941225,0.0047699627,0.00033445947,0.00008484402,0.0000930973,0.000031086944,0.000005980494,0.000079864876],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9845071,0.008015828,0.0026923239,0.00074328546,0.0037922764,0.00024911007],"domain_scores_gemma":[0.90656567,0.08221295,0.0035261605,0.0011870704,0.006210812,0.00029735515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024763532,0.001114847,0.0026082948,0.008550642,0.0008103993,0.004374141,0.0025800904,0.0026194581,0.0015832961],"category_scores_gemma":[0.05072506,0.0009700019,0.0018686133,0.010018757,0.003251754,0.006830635,0.0015763163,0.002770174,0.00048765138],"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":[0.00004309429,0.00006975229,0.00060248224,0.11066209,0.00032374708,0.00009800515,0.0016130704,0.0018388827,0.0003441456,0.025567746,0.004381883,0.85445523],"study_design_scores_gemma":[0.00007055598,0.00036802058,0.0039468165,0.32105523,0.0013960341,0.0013607772,0.0046648625,0.0026407687,0.0015054448,0.031070285,0.6317485,0.00017272266],"about_ca_topic_score_codex":0.0085065635,"about_ca_topic_score_gemma":0.0086023575,"teacher_disagreement_score":0.024763532,"about_ca_system_score_codex":0.005503506,"about_ca_system_score_gemma":0.017583726,"threshold_uncertainty_score":0.13096368},"labels":[],"label_agreement":null},{"id":"W2153852338","doi":"10.1197/jamia.m1995","title":"Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Ottawa Hospital; Natural Sciences and Engineering Research Council; University of Ottawa","funders":"Canadian Institutes of Health Research; Institute of Musculoskeletal Health and Arthritis; Physicians' Services Incorporated Foundation","keywords":"Artificial intelligence; Support vector machine; Wrist; Categorization; Computer science; Naive Bayes classifier; Benchmark (surveying); Machine learning; Artificial neural network; Set (abstract data type); Cross-validation; Pattern recognition (psychology); Medicine; Radiology","score_opus":0.012624214612709775,"score_gpt":0.3214392851727318,"score_spread":0.308815070560022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153852338","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":[0.9839549,0.00060843694,0.012243798,0.00030992308,0.000060344264,0.0001601515,0.00072362757,0.00037628945,0.0015625803],"genre_scores_gemma":[0.98208034,0.00017886756,0.015661571,0.00009903133,0.000085317115,0.000071345785,0.0012798294,0.000021021822,0.00052267103],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967662,0.001270753,0.0006124612,0.00044442408,0.0007552183,0.00015093134],"domain_scores_gemma":[0.9773078,0.014316011,0.0020949591,0.0010933887,0.0048745763,0.0003132826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004434321,0.00042154896,0.0006868391,0.0033966817,0.00034498356,0.0011352682,0.00062672526,0.0008984631,0.0014042405],"category_scores_gemma":[0.031374026,0.00024178889,0.00047801397,0.0010853125,0.00020313257,0.0010596163,0.00035110037,0.00038513856,0.0011539425],"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":[0.0013144362,0.00046619252,0.7735995,0.00017571737,0.000144123,0.00027791804,0.00041230902,0.0016788814,0.004811099,0.000119436365,0.0025728133,0.21442758],"study_design_scores_gemma":[0.000537841,0.0015501737,0.8110798,0.00015036674,0.00037116674,0.0038922005,0.001084524,0.15918429,0.017280305,0.0008175046,0.0039097713,0.00014206789],"about_ca_topic_score_codex":0.0020317943,"about_ca_topic_score_gemma":0.0021702151,"teacher_disagreement_score":0.004434321,"about_ca_system_score_codex":0.00027926045,"about_ca_system_score_gemma":0.00043865902,"threshold_uncertainty_score":0.023451209},"labels":[],"label_agreement":null},{"id":"W2154161656","doi":"10.1197/jamia.m2178","title":"Finding Leading Indicators for Disease Outbreaks: Filtering, Cross-correlation, and Caveats","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Institut National de Santé Publique du Québec; McGill University Health Centre","funders":"","keywords":"Scale (ratio); Econometrics; Sample (material); Computer science; Data science; Outbreak; Statistics; Medicine; Geography; Mathematics; Cartography","score_opus":0.055298832802206675,"score_gpt":0.4032694074711659,"score_spread":0.34797057466895925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154161656","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":[0.06507995,0.0105892755,0.6813197,0.20233534,0.0064143743,0.0014341439,0.003041148,0.0018492813,0.027936758],"genre_scores_gemma":[0.56708854,0.0029532914,0.36962956,0.040997993,0.0067619537,0.0021019306,0.0011879306,0.0006317253,0.0086470125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9209734,0.04957109,0.010004629,0.00845932,0.009840179,0.0011514488],"domain_scores_gemma":[0.3349175,0.60057783,0.01821622,0.03300583,0.0121667925,0.0011158681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1639393,0.0018783011,0.0027409235,0.005107232,0.005286747,0.007206478,0.009449456,0.0062774173,0.0091977],"category_scores_gemma":[0.57268286,0.0010808496,0.005230655,0.0067436136,0.00881747,0.012162972,0.0042443555,0.00741086,0.0012726439],"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":[0.00096224685,0.00035443067,0.1655614,0.0024301223,0.0024011168,0.0034112595,0.008195976,0.020173643,0.00070071744,0.47319347,0.09205608,0.2305596],"study_design_scores_gemma":[0.00044041045,0.00046675495,0.041427977,0.0027661375,0.0012366155,0.004247401,0.0033358685,0.11622855,0.00231272,0.7791041,0.04791127,0.00052211795],"about_ca_topic_score_codex":0.045287788,"about_ca_topic_score_gemma":0.028481022,"teacher_disagreement_score":0.1639393,"about_ca_system_score_codex":0.0020584958,"about_ca_system_score_gemma":0.0045091785,"threshold_uncertainty_score":0.8670042},"labels":[],"label_agreement":null},{"id":"W2154352790","doi":"10.1136/jamia.2010.004325","title":"A new algorithm for reducing the workload of experts in performing systematic reviews","year":2010,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Workload; Computer science; Complement (music); Naive Bayes classifier; Systematic review; Classifier (UML); Machine learning; Bayes' theorem; Algorithm; Inclusion (mineral); Artificial intelligence; Systematic error; MEDLINE; Mathematics; Support vector machine; Bayesian probability; Statistics; Psychology","score_opus":0.3147686100222522,"score_gpt":0.4838588630158001,"score_spread":0.1690902529935479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154352790","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":[0.019031566,0.0012515385,0.9730291,0.00078412634,0.00019373001,0.0013161409,0.00062443485,0.0031100956,0.00065933657],"genre_scores_gemma":[0.0378883,0.0001652702,0.96013176,0.00018430686,0.00007208274,0.0008032968,0.00039811854,0.00006264681,0.00029421996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99104637,0.003535041,0.0014960833,0.0016512097,0.0020214883,0.00024984995],"domain_scores_gemma":[0.9532339,0.033137843,0.0024346353,0.0020879146,0.008566113,0.00053957856],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014124864,0.0016132977,0.0020679524,0.005489855,0.0010292606,0.0018954589,0.0018523759,0.0021212073,0.0029403456],"category_scores_gemma":[0.07227685,0.0008838822,0.0016603715,0.0033116655,0.00060165324,0.0027648308,0.001157138,0.0013786763,0.00089911476],"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":[0.00086500234,0.00028093188,0.008878644,0.0010615456,0.0006106874,0.0001124101,0.00026567015,0.03787031,0.004853015,0.0019706343,0.009040549,0.93419063],"study_design_scores_gemma":[0.0008415675,0.0005471802,0.0053169844,0.00032589768,0.0005395129,0.00042958008,0.000104492785,0.967389,0.0058711567,0.009171715,0.0093837455,0.00007928072],"about_ca_topic_score_codex":0.0068713324,"about_ca_topic_score_gemma":0.010188914,"teacher_disagreement_score":0.9858751,"about_ca_system_score_codex":0.00201236,"about_ca_system_score_gemma":0.0070306826,"threshold_uncertainty_score":0.074700356},"labels":[],"label_agreement":null},{"id":"W2154595335","doi":"10.1136/amiajnl-2011-000233","title":"Retrieval of diagnostic and treatment studies for clinical use through PubMed and PubMed's Clinical Queries filters","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Information retrieval; Computer science; MEDLINE; Quality (philosophy); Medicine; Medical physics","score_opus":0.8369815575185015,"score_gpt":0.5776694385083737,"score_spread":0.25931211901012774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154595335","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":[0.2576828,0.11567099,0.2037294,0.031269025,0.004807539,0.21677172,0.11486918,0.012075121,0.043124206],"genre_scores_gemma":[0.15271099,0.022978907,0.62796867,0.005591664,0.0021232073,0.15090527,0.031706497,0.0009829438,0.0050318977],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.84052485,0.052607745,0.08504702,0.005105426,0.015209809,0.001505084],"domain_scores_gemma":[0.43819427,0.41377163,0.06384894,0.01756535,0.06280676,0.003813031],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1600676,0.0025467447,0.005781474,0.056878343,0.0027530326,0.007774876,0.0028983185,0.003121965,0.013025934],"category_scores_gemma":[0.4314594,0.0016498247,0.0037345754,0.034548756,0.0018524852,0.006659556,0.005207683,0.0013059512,0.003299989],"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":[0.007725627,0.0005456139,0.03209859,0.22733425,0.0029757107,0.0024118237,0.011562815,0.0009313363,0.026040556,0.005335412,0.06894207,0.61409616],"study_design_scores_gemma":[0.014911968,0.0068740966,0.19222127,0.15342377,0.018125663,0.0052736625,0.011607526,0.008248311,0.030330509,0.018372728,0.5388702,0.0017402773],"about_ca_topic_score_codex":0.004067,"about_ca_topic_score_gemma":0.008996696,"teacher_disagreement_score":0.8399324,"about_ca_system_score_codex":0.0048728203,"about_ca_system_score_gemma":0.029814236,"threshold_uncertainty_score":0.84652853},"labels":[],"label_agreement":null},{"id":"W2155655790","doi":"10.1197/jamia.m1201","title":"Electronically Screening Discharge Summaries for Adverse Medical Events","year":2003,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"U.S. Public Health Service","keywords":"Adverse effect; Medicine; Medical information; Medical emergency; Intensive care medicine; Family medicine; Internal medicine","score_opus":0.02634890056207311,"score_gpt":0.3915247094490465,"score_spread":0.3651758088869734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155655790","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":[0.9591324,0.0059510726,0.009735347,0.0019773135,0.00021887293,0.0041485634,0.011117743,0.0008209054,0.0068979026],"genre_scores_gemma":[0.9642128,0.0032791325,0.023442961,0.0006822463,0.0003133334,0.0019222373,0.0049935705,0.000029837256,0.0011238036],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9858155,0.0072391406,0.0032996754,0.00082489447,0.00255499,0.00026577036],"domain_scores_gemma":[0.8566487,0.062599674,0.06102839,0.005063606,0.012638435,0.0020212124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008860096,0.00051930884,0.0005851313,0.00413996,0.0003798329,0.0010403051,0.0006305162,0.0005734212,0.0033024475],"category_scores_gemma":[0.08743501,0.0002771153,0.0003653463,0.0029586693,0.0004196417,0.0011393565,0.0011285057,0.0003874872,0.001057081],"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":[0.0024324255,0.0007932749,0.7234326,0.0025957203,0.00026513365,0.0004877896,0.0011638715,0.0005148033,0.0026079956,0.00039621894,0.012074111,0.25323606],"study_design_scores_gemma":[0.0012225319,0.0052939695,0.9556499,0.002238508,0.00037379426,0.002122512,0.001228967,0.0026836735,0.009376995,0.0007816358,0.018886143,0.00014123587],"about_ca_topic_score_codex":0.00070363976,"about_ca_topic_score_gemma":0.0010093412,"teacher_disagreement_score":0.008860096,"about_ca_system_score_codex":0.0006023785,"about_ca_system_score_gemma":0.0017939174,"threshold_uncertainty_score":0.046857238},"labels":[],"label_agreement":null},{"id":"W2156517549","doi":"10.1136/amiajnl-2011-000304","title":"The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; McMaster University","funders":"U.S. Public Health Service; Agency for Healthcare Research and Quality; McMaster University; U.S. Department of Health and Human Services","keywords":"Randomized controlled trial; Health information technology; Agency (philosophy); Medicine; Health care; Quality management; Quality (philosophy); Systematic review; MEDLINE; Computer science; Operations management; Management system; Engineering","score_opus":0.04499522037677051,"score_gpt":0.4774516020011701,"score_spread":0.4324563816243996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156517549","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":[0.003482931,0.99138474,0.00057640596,0.00036012512,0.00027337027,0.003271201,0.00034686242,0.000029737252,0.00027459522],"genre_scores_gemma":[0.078206435,0.89944893,0.006691694,0.0015368455,0.0004077547,0.012959785,0.00048063672,0.000030720512,0.00023711515],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9331491,0.029903335,0.024598515,0.0039220382,0.007617281,0.00080977066],"domain_scores_gemma":[0.8619378,0.09637462,0.031020436,0.0022596878,0.0072858483,0.0011216032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.049085364,0.0028399995,0.023858953,0.011232718,0.0012145264,0.00441539,0.003440773,0.0036201526,0.0055986308],"category_scores_gemma":[0.14519735,0.0024375627,0.01968744,0.011026573,0.0023829115,0.0045701903,0.0021268406,0.0026417598,0.00041370912],"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":[0.0031529544,0.00006915153,0.0007296933,0.9202482,0.050429557,0.000053199816,0.00013373922,0.00015816341,0.00014499392,0.00014328693,0.00046167913,0.024275454],"study_design_scores_gemma":[0.010122821,0.0018995246,0.0036475551,0.6325147,0.34506425,0.00019152235,0.0002642168,0.000294599,0.0004075573,0.00046005464,0.00504989,0.00008329279],"about_ca_topic_score_codex":0.0057242457,"about_ca_topic_score_gemma":0.012951018,"teacher_disagreement_score":0.049085364,"about_ca_system_score_codex":0.008187825,"about_ca_system_score_gemma":0.01400657,"threshold_uncertainty_score":0.25959134},"labels":[],"label_agreement":null},{"id":"W2157048891","doi":"10.1136/amiajnl-2011-000179","title":"Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study","year":2011,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's College Hospital","funders":"Agency for Healthcare Research and Quality","keywords":"Medicine; Intensive care medicine; Clinical decision support system; Drug; Emergency medicine; Clinical trial; Indirect costs; Economic evaluation; Chronic renal insufficiency; Decision support system; Finance; Medical emergency; Internal medicine; Renal function; Business; Accounting; Pharmacology; Computer science","score_opus":0.05552174515970908,"score_gpt":0.4748067183996302,"score_spread":0.4192849732399211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157048891","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":[0.993849,0.0011372863,0.0012811358,0.0008906862,0.0000910184,0.00111763,0.0002509286,0.000019143085,0.0013632574],"genre_scores_gemma":[0.99672467,0.00035243086,0.0015334155,0.00022722529,0.00010017598,0.0007562509,0.00011220976,0.0000025984789,0.00019103402],"study_design_codex":"randomized_trial","study_design_gemma":"observational","domain_scores_codex":[0.9689171,0.025999539,0.0012779724,0.00083319604,0.0017492624,0.0012229495],"domain_scores_gemma":[0.91230977,0.07155571,0.008613706,0.0030115838,0.0025185195,0.0019907306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016007502,0.001048707,0.0010737,0.00086692674,0.0011228432,0.0014638306,0.0010564533,0.0023610361,0.0033674622],"category_scores_gemma":[0.05616013,0.000515234,0.0038017211,0.0008987829,0.0013064196,0.0012246782,0.0010580871,0.0021268195,0.00028134332],"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":[0.39702305,0.16050343,0.19519767,0.0062401392,0.015377438,0.0049245707,0.001154569,0.02453265,0.0022527392,0.0065238,0.004029377,0.18224052],"study_design_scores_gemma":[0.17446159,0.63134253,0.112179585,0.0011883401,0.00945958,0.0036084906,0.0017246169,0.049674455,0.007260268,0.003303896,0.005403568,0.0003930293],"about_ca_topic_score_codex":0.0030226058,"about_ca_topic_score_gemma":0.002482455,"teacher_disagreement_score":0.016007502,"about_ca_system_score_codex":0.0036636302,"about_ca_system_score_gemma":0.0033216795,"threshold_uncertainty_score":0.084656775},"labels":[],"label_agreement":null},{"id":"W2157052691","doi":"10.1136/jamia.2000.0070109","title":"Informatics at NIH","year":2000,"lang":"en","type":"letter","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Informatics; Data science; Political science","score_opus":0.0039018212278837346,"score_gpt":0.20202101344203865,"score_spread":0.19811919221415492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157052691","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014727528,0.001964945,0.00012260048,0.94290626,0.04342745,0.000012115625,0.000046527104,0.00005509382,0.011317792],"genre_scores_gemma":[0.0014387879,0.001209407,0.00010888464,0.9456852,0.028458906,0.000022046239,0.000026330741,0.00002692217,0.023023527],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959453,0.00077053386,0.000503965,0.00061208167,0.0015439431,0.00062415213],"domain_scores_gemma":[0.9900553,0.005121786,0.00045344676,0.0005723132,0.0019947386,0.0018024485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004022369,0.00075619714,0.0006929804,0.00064038316,0.004351863,0.0072276746,0.002162043,0.04868516,0.025969235],"category_scores_gemma":[0.026053045,0.00060624065,0.00084911985,0.000778689,0.0027609605,0.004716121,0.0022501359,0.03795144,0.021191329],"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":[0.000008974944,0.000009185438,0.00007772135,0.000022723514,0.0000024515757,0.00021121815,0.000048522663,0.000010729223,0.000040958967,0.0024113017,0.9911964,0.0059598414],"study_design_scores_gemma":[0.000016477581,0.000009963153,0.00023854173,0.00011330985,0.0000044765875,0.00024685313,0.00010500757,0.000041371928,0.000053559274,0.0018801702,0.9972791,0.000011286848],"about_ca_topic_score_codex":0.004210773,"about_ca_topic_score_gemma":0.007682858,"teacher_disagreement_score":0.04868516,"about_ca_system_score_codex":0.0035993028,"about_ca_system_score_gemma":0.0052346834,"threshold_uncertainty_score":0.08687574},"labels":[],"label_agreement":null},{"id":"W2157544090","doi":"10.1197/jamia.m2005","title":"Viewpoint: A Pragmatic Approach to Constructing a Minimum Data Set for Care of Patients with HIV in Developing Countries","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Oak Ridge Institute for Science and Education; Centers for Disease Control and Prevention; National Institutes of Health; Rockefeller Foundation; World Health Organization; Fogarty International Center; U.S. Department of Energy","keywords":"Minimum Data Set; Developing country; Health informatics; Computer science; Human immunodeficiency virus (HIV); Health care; Data set; Data quality; Informatics; Set (abstract data type); Data management; Data science; Data mining; Medicine; Family medicine; Nursing; Artificial intelligence; Public health; Business; Political science; Marketing; Economic growth","score_opus":0.01582941528731677,"score_gpt":0.3172752463072548,"score_spread":0.301445831019938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157544090","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008448834,0.0021254902,0.53118926,0.42107418,0.0037973241,0.0034386672,0.003131901,0.00037125425,0.026423136],"genre_scores_gemma":[0.07538974,0.00085775915,0.84417796,0.06772815,0.0017508551,0.0063202726,0.0012478561,0.00016227229,0.0023652273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.83075655,0.12642963,0.014648837,0.006392731,0.020324148,0.0014482025],"domain_scores_gemma":[0.7456126,0.15425088,0.012187757,0.024689207,0.056405228,0.0068543344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17481834,0.0012953996,0.0027948404,0.007897392,0.004579373,0.011498363,0.007669862,0.007858201,0.0056523965],"category_scores_gemma":[0.31381053,0.001538865,0.003360107,0.0058720154,0.008716051,0.011475595,0.009594697,0.015613206,0.0019970692],"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":[0.00039542885,0.0003003166,0.0064016976,0.0021647227,0.00034638486,0.000664232,0.006987197,0.0033355595,0.0010519944,0.74567187,0.14081396,0.091866784],"study_design_scores_gemma":[0.0004807941,0.0003867388,0.0050126943,0.0036773223,0.00025544097,0.0010606667,0.007441161,0.0126058785,0.0014210112,0.6433292,0.3239974,0.00033170707],"about_ca_topic_score_codex":0.0083787935,"about_ca_topic_score_gemma":0.011243172,"teacher_disagreement_score":0.17481834,"about_ca_system_score_codex":0.010744459,"about_ca_system_score_gemma":0.02892987,"threshold_uncertainty_score":0.9245388},"labels":[],"label_agreement":null},{"id":"W2157583816","doi":"10.1197/jamia.m2012","title":"A Risk Assessment of Two Interorganizational Clinical Information Systems","year":2006,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Risk analysis (engineering); Risk management; Computer science; Quality (philosophy); IT risk management; Project risk management; Process management; Scale (ratio); Risk assessment; Risk management framework; Order (exchange); Test (biology); Information system; Knowledge management; Project management; Business; Project management triangle; Computer security; Systems engineering; Engineering","score_opus":0.018961618515348465,"score_gpt":0.4551641405522256,"score_spread":0.43620252203687715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157583816","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":[0.73429215,0.0009876556,0.24152601,0.0019199685,0.00004479887,0.0033159724,0.00042632635,0.00024090955,0.017246187],"genre_scores_gemma":[0.91688186,0.00020971365,0.08096368,0.00005017384,0.000012180783,0.00076314795,0.0001766126,0.00001763274,0.0009250843],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9723369,0.015822323,0.0016737279,0.0014489661,0.0071405475,0.0015775549],"domain_scores_gemma":[0.92259526,0.047154054,0.013843091,0.0035347955,0.010761447,0.0021113728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0279466,0.0014990646,0.0008389789,0.010543916,0.0021737248,0.0066025606,0.0016319295,0.0019777657,0.002239862],"category_scores_gemma":[0.06951099,0.00077978615,0.002289859,0.004204028,0.0023255013,0.0055265543,0.0048206793,0.0015855319,0.00019754615],"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":[0.0013027384,0.0018408562,0.28064275,0.001373771,0.001475533,0.0014159472,0.01548187,0.24419257,0.005249295,0.24700816,0.0022697498,0.19774678],"study_design_scores_gemma":[0.00021540384,0.0029442606,0.10897397,0.00078905583,0.0006713012,0.00095163786,0.015376995,0.79437035,0.0037194265,0.062169634,0.00952525,0.0002927174],"about_ca_topic_score_codex":0.009212682,"about_ca_topic_score_gemma":0.005002531,"teacher_disagreement_score":0.0279466,"about_ca_system_score_codex":0.009673468,"about_ca_system_score_gemma":0.009678316,"threshold_uncertainty_score":0.14779752},"labels":[],"label_agreement":null},{"id":"W2158288668","doi":"10.1136/jamia.2000.0070361","title":"Building a Virtual Network in a Community Health Research Training Program","year":2000,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Community Health and Development","field":"Health Professions","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Fondation pour la Recherche Médicale","keywords":"The Internet; Computer science; CLARITY; Participatory action research; Community health; Medical education; Knowledge management; World Wide Web; Medicine; Nursing; Public health","score_opus":0.1620289523554033,"score_gpt":0.5344640465684061,"score_spread":0.3724350942130028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158288668","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":[0.92654926,0.00034760093,0.014427154,0.007988408,0.000332291,0.0013735892,0.00007939416,0.00040839682,0.04849391],"genre_scores_gemma":[0.97208,0.00018701518,0.017256206,0.000808218,0.00005097967,0.00046826637,0.00009262815,0.000038547147,0.0090180645],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9897305,0.007064769,0.00008775526,0.00044749206,0.0006949604,0.0019745715],"domain_scores_gemma":[0.9837472,0.003076911,0.00072048587,0.00079596316,0.00096136506,0.010698044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010449754,0.00047421377,0.00021146676,0.00070012937,0.012756508,0.0037107575,0.0032445306,0.0011780267,0.005692532],"category_scores_gemma":[0.01055051,0.0003925154,0.00026514308,0.0005214449,0.00455998,0.0026589609,0.010158418,0.0018261726,0.00060629356],"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":[0.0008594383,0.0069575384,0.063856006,0.0007570884,0.00006120319,0.0064055943,0.47599915,0.0036933383,0.007153045,0.014122973,0.03359392,0.38654074],"study_design_scores_gemma":[0.00034716417,0.004430119,0.043104813,0.000781191,0.000074138785,0.0023610846,0.5137453,0.0063478467,0.0026146714,0.0057843267,0.42024475,0.00016450102],"about_ca_topic_score_codex":0.05214405,"about_ca_topic_score_gemma":0.11905917,"teacher_disagreement_score":0.05214405,"about_ca_system_score_codex":0.0068502924,"about_ca_system_score_gemma":0.021944953,"threshold_uncertainty_score":0.10368109},"labels":[],"label_agreement":null},{"id":"W2158718916","doi":"10.1136/amiajnl-2011-000735","title":"A secure distributed logistic regression protocol for the detection of rare adverse drug events","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; McGill University; Memorial University of Newfoundland; Agricultural Research Institute of Ontario; University of Ottawa","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; Canada Research Chairs; Ontario Institute for Cancer Research; National Institutes of Health; National Science Foundation","keywords":"Computer science; Pooling; Protocol (science); Logistic regression; Data mining; Population; False positive paradox; Artificial intelligence; Machine learning; Medicine","score_opus":0.07351165846192852,"score_gpt":0.45950530468421863,"score_spread":0.3859936462222901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158718916","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":[0.008553476,0.00008713959,0.98671037,0.0006991744,0.00007353392,0.00062063895,0.0001779622,0.0015652922,0.001512385],"genre_scores_gemma":[0.5868473,0.00029838248,0.40174088,0.0006975416,0.00018195696,0.004284038,0.0009313269,0.00024035126,0.0047783265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9857972,0.0067628315,0.0014155321,0.0018682858,0.0033726285,0.0007835817],"domain_scores_gemma":[0.97675294,0.011775142,0.002418802,0.005794811,0.0027268974,0.0005314738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014341484,0.0008973222,0.00112076,0.0010632079,0.0014634577,0.0024524366,0.002743028,0.0018575037,0.004595312],"category_scores_gemma":[0.034372266,0.00068998267,0.0011116762,0.0008100202,0.0019030718,0.004305555,0.006597193,0.0027350632,0.0016716699],"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":[0.0040407726,0.00070413074,0.008674922,0.0009817407,0.0004335013,0.0036842301,0.0023727398,0.19819447,0.04825136,0.397833,0.026882524,0.30794662],"study_design_scores_gemma":[0.00064460066,0.00047656504,0.0007010379,0.00010662575,0.000095189,0.0010523804,0.00020684158,0.8541173,0.020379057,0.10533648,0.016756019,0.00012786184],"about_ca_topic_score_codex":0.00087177305,"about_ca_topic_score_gemma":0.00053835724,"teacher_disagreement_score":0.014341484,"about_ca_system_score_codex":0.0015024408,"about_ca_system_score_gemma":0.0048495685,"threshold_uncertainty_score":0.07584596},"labels":[],"label_agreement":null},{"id":"W2160996683","doi":"10.1093/jamia/ocu002","title":"Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Triage; Medicine; Emergency department; Confidence interval; Emergency medicine; Influenza-like illness; Early warning score; Pediatrics; Medical emergency; Internal medicine","score_opus":0.04153490928077576,"score_gpt":0.3493337837975727,"score_spread":0.30779887451679694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160996683","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":[0.9831024,0.0013705507,0.010611643,0.0006120008,0.000068973706,0.000109380926,0.0027765501,0.00016802455,0.001180412],"genre_scores_gemma":[0.9924016,0.00027066306,0.005236333,0.00007551967,0.000044809425,0.000030151583,0.0016958107,0.0000065586937,0.00023848671],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976138,0.001398182,0.00016483197,0.00045384705,0.00024567105,0.0001236804],"domain_scores_gemma":[0.9905524,0.0043302593,0.003296951,0.0005709342,0.0008977739,0.00035160687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060065472,0.00095989363,0.0007347899,0.0017151019,0.00024174163,0.0008680757,0.00067473046,0.0005867841,0.0009215868],"category_scores_gemma":[0.021080034,0.00033712713,0.0009907212,0.0013275911,0.00018956787,0.00096855714,0.0007304459,0.00072973,0.0002449672],"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":[0.00013837623,0.00009868436,0.98371106,0.000037899903,0.0003118092,0.000026133144,0.000042599495,0.004333642,0.00011893767,0.00004926617,0.00040920972,0.010722343],"study_design_scores_gemma":[0.000066208544,0.0006644781,0.87104905,0.00010217475,0.00035745848,0.0001148049,0.00009357351,0.12598315,0.00038092467,0.00029420535,0.0008631003,0.000030839263],"about_ca_topic_score_codex":0.029964177,"about_ca_topic_score_gemma":0.0415957,"teacher_disagreement_score":0.029964177,"about_ca_system_score_codex":0.000792245,"about_ca_system_score_gemma":0.0014510384,"threshold_uncertainty_score":0.05957955},"labels":[],"label_agreement":null},{"id":"W2161071286","doi":"10.1197/jamia.m1752","title":"Optimal Search Strategies for Detecting Clinically Sound Prognostic Studies in EMBASE: An Analytic Survey","year":2005,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"MEDLINE; Medicine; Information retrieval; Sensitivity (control systems); Computer science; Filter (signal processing); Medical physics","score_opus":0.684319073171744,"score_gpt":0.5993694279373544,"score_spread":0.08494964523438964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161071286","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":[0.64888346,0.2564613,0.046716698,0.0064008753,0.00021181986,0.020539252,0.01181829,0.00045283482,0.008515554],"genre_scores_gemma":[0.85766387,0.038747367,0.08337514,0.0013062796,0.00019974395,0.012744976,0.0055515477,0.00012092621,0.00029007625],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.65704525,0.1751521,0.12464631,0.007608969,0.03323179,0.00231553],"domain_scores_gemma":[0.18272226,0.7030662,0.06587526,0.011305418,0.035771083,0.0012598886],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.21087858,0.0016447571,0.00502787,0.05465361,0.0013275727,0.005684511,0.0018991692,0.0021614116,0.002449714],"category_scores_gemma":[0.65001917,0.0012293361,0.006340487,0.03623222,0.0018951936,0.0074330117,0.0037230242,0.00092714594,0.0007972228],"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":[0.007859914,0.00060601835,0.54533315,0.086892374,0.012886001,0.0012609282,0.008211867,0.0019354406,0.0029234162,0.0030321802,0.0050193085,0.32403946],"study_design_scores_gemma":[0.004804649,0.008211507,0.7439948,0.076043725,0.061465207,0.009305483,0.017566493,0.020697689,0.0069867373,0.015767764,0.034413375,0.00074256846],"about_ca_topic_score_codex":0.0021183507,"about_ca_topic_score_gemma":0.0038694774,"teacher_disagreement_score":0.7891214,"about_ca_system_score_codex":0.0036849,"about_ca_system_score_gemma":0.0074011115,"threshold_uncertainty_score":0.9731272},"labels":[],"label_agreement":null},{"id":"W2162881374","doi":"10.1136/amiajnl-2012-001011","title":"Privacy by Design at Population Data BC: a case study describing the technical, administrative, and physical controls for privacy-sensitive secondary use of personal information for research in the public interest","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Internet privacy; Information privacy; Privacy policy; Due diligence; Population; Privacy by Design; Privacy law; Computer security; Computer science; Business; Public relations; Environmental health; Medicine; Political science","score_opus":0.40374764617016207,"score_gpt":0.4772901401040093,"score_spread":0.07354249393384721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162881374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7763766,0.0019210948,0.089390434,0.05368075,0.00030879513,0.0017639897,0.00029839735,0.00017192858,0.07608806],"genre_scores_gemma":[0.9418274,0.0015445177,0.03850624,0.005454599,0.00007557387,0.0009673128,0.000089345,0.00011040791,0.011424583],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.923812,0.060776178,0.001708748,0.0025174692,0.0068552354,0.0043305084],"domain_scores_gemma":[0.9513617,0.03456442,0.0036311836,0.004494799,0.0026456579,0.0033022726],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.042840123,0.0008869126,0.0007689569,0.002294949,0.023058636,0.0083662,0.0032635613,0.009163698,0.0028188708],"category_scores_gemma":[0.055736873,0.0010575015,0.0013706877,0.003140569,0.016787937,0.0067987195,0.009565709,0.007765362,0.00048138257],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019578086,0.0013597143,0.035456628,0.00052680867,0.000081188504,0.046857927,0.69050825,0.0016812922,0.0024133325,0.15554836,0.012156514,0.05321414],"study_design_scores_gemma":[0.00011841189,0.0009948902,0.01176017,0.0015578916,0.0001429049,0.047462724,0.6048962,0.0057022166,0.0066735386,0.040617466,0.2798541,0.0002195376],"about_ca_topic_score_codex":0.029996514,"about_ca_topic_score_gemma":0.045695703,"teacher_disagreement_score":0.99673647,"about_ca_system_score_codex":0.014049183,"about_ca_system_score_gemma":0.017952686,"threshold_uncertainty_score":0.22656292},"labels":[],"label_agreement":null},{"id":"W2169918581","doi":"10.1197/jamia.m2299","title":"The Use of Wireless E-Mail to Improve Healthcare Team Communication","year":2009,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; St. Michael's Hospital; Sunnybrook Health Science Centre; University of Toronto; Trillium Health Centre","funders":"","keywords":"Likert scale; Medicine; Health care; Intensive care unit; Medical emergency; Psychology","score_opus":0.03321093735355562,"score_gpt":0.40974853596581534,"score_spread":0.3765375986122597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169918581","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":[0.9950181,0.0006391535,0.0011789015,0.00043206938,0.00004362177,0.0001803087,0.000051131574,0.00006710563,0.0023894988],"genre_scores_gemma":[0.99483806,0.0004095318,0.003791399,0.00020282154,0.00011953788,0.00016166965,0.000061660176,0.0000081512035,0.00040716314],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99649376,0.0023679351,0.000252144,0.00013568826,0.0005447396,0.0002057076],"domain_scores_gemma":[0.9883366,0.007121912,0.0025315315,0.00043754268,0.0008966272,0.0006757748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036377232,0.0003685605,0.00020153997,0.0005441235,0.0002298025,0.00059365126,0.00038868087,0.00044357456,0.0026231022],"category_scores_gemma":[0.017478602,0.00011208832,0.0002443325,0.00033369398,0.00022880273,0.0008721619,0.0005366829,0.00036908456,0.0003743209],"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":[0.0029217943,0.014481958,0.31575918,0.0010685212,0.00025005342,0.00016882189,0.0014215651,0.0008699132,0.0054695266,0.0000922877,0.0017020659,0.65579426],"study_design_scores_gemma":[0.0010601318,0.07086305,0.90604985,0.0005393072,0.0005230725,0.00082536467,0.0018069092,0.003431527,0.008003851,0.00016522474,0.006686774,0.000045058714],"about_ca_topic_score_codex":0.00022368984,"about_ca_topic_score_gemma":0.00032084843,"teacher_disagreement_score":0.0036377232,"about_ca_system_score_codex":0.00021826557,"about_ca_system_score_gemma":0.00044517472,"threshold_uncertainty_score":0.019238293},"labels":[],"label_agreement":null},{"id":"W2170242722","doi":"10.1136/amiajnl-2011-000307","title":"Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and healthcare providers","year":2012,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Usability; Diabetes mellitus; Health care; Medicine; Web application; Sustainability; Computer science; World Wide Web; Human–computer interaction","score_opus":0.05131557824087894,"score_gpt":0.4205619524205157,"score_spread":0.36924637417963674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170242722","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":[0.0033692268,0.990954,0.00040819406,0.000339755,0.00022488442,0.0034648862,0.0006434689,0.00002226483,0.0005732256],"genre_scores_gemma":[0.047663126,0.93919826,0.0033654834,0.00092912005,0.00014941534,0.007918852,0.00047947862,0.000016058028,0.0002801041],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9665033,0.015642203,0.0105863,0.0012523851,0.005481162,0.00053466845],"domain_scores_gemma":[0.9311543,0.04742489,0.012985525,0.0010540381,0.0066716345,0.00070957845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026070558,0.0023149897,0.014185677,0.013907393,0.0012017585,0.0042202803,0.0027045785,0.0024424791,0.0057394775],"category_scores_gemma":[0.10310222,0.0013355305,0.009697177,0.012766733,0.001324878,0.0036306283,0.0020659203,0.0016612847,0.0004068325],"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":[0.0003091639,0.000025185727,0.00041558934,0.9686968,0.011815898,0.000053091277,0.00019566175,0.000056386114,0.000073630625,0.0000933267,0.0005429534,0.01772236],"study_design_scores_gemma":[0.0007388675,0.0004031607,0.0026422746,0.91156703,0.07698517,0.00014348085,0.00036835126,0.000100665,0.00015389522,0.00016619565,0.0066969767,0.00003392854],"about_ca_topic_score_codex":0.008576829,"about_ca_topic_score_gemma":0.026466763,"teacher_disagreement_score":0.026070558,"about_ca_system_score_codex":0.0073245047,"about_ca_system_score_gemma":0.018926775,"threshold_uncertainty_score":0.13787591},"labels":[],"label_agreement":null},{"id":"W2171004512","doi":"10.1136/amiajnl-2010-000019","title":"The impact of the electronic medical record on structure, process, and outcomes within primary care: a systematic review of the evidence: Figure 1","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":231,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital; University of Calgary; Alberta Health Services","funders":"","keywords":"Primary care; Electronic medical record; Medicine; Electronic health record; Process (computing); Medical record; MEDLINE; Computer science; Data science; Medical emergency; Family medicine; Health care; Internal medicine; Political science","score_opus":0.0318055965183648,"score_gpt":0.44810981570285463,"score_spread":0.41630421918448984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171004512","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":[0.00066472555,0.99820125,0.0001050149,0.0002507678,0.00009745925,0.00028145767,0.00011913952,0.0000036928454,0.0002765806],"genre_scores_gemma":[0.01000872,0.98784,0.0010318639,0.0003139597,0.00006732461,0.0005041426,0.00011231117,0.000002533741,0.000119153265],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9837509,0.0063402643,0.0046586795,0.0011670474,0.00366538,0.00041785525],"domain_scores_gemma":[0.9479121,0.03938542,0.0073053623,0.00053185393,0.0044975705,0.00036763668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015040177,0.0018247527,0.006429016,0.01550926,0.00093815045,0.0037780937,0.0018754211,0.0027107191,0.00564338],"category_scores_gemma":[0.062813595,0.0014766277,0.0063278936,0.01831972,0.0011304886,0.004037368,0.001662492,0.0014775672,0.00041852792],"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":[0.00017142929,0.000028212273,0.0010035896,0.9212488,0.00406367,0.00010036575,0.00019435212,0.00009217741,0.00015884507,0.00027837744,0.0014134247,0.07124677],"study_design_scores_gemma":[0.00022077272,0.00024086867,0.0052034636,0.94793946,0.030922232,0.00039098746,0.0003395405,0.00008725807,0.00020749749,0.0002374016,0.014184276,0.000026357184],"about_ca_topic_score_codex":0.009877952,"about_ca_topic_score_gemma":0.032044232,"teacher_disagreement_score":0.01550926,"about_ca_system_score_codex":0.0057206047,"about_ca_system_score_gemma":0.014300143,"threshold_uncertainty_score":0.07954097},"labels":[],"label_agreement":null},{"id":"W2171191802","doi":"10.1136/jamia.2001.0080401","title":"A Four-Dimensional Probabilistic Atlas of the Human Brain","year":2001,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":392,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Cancer Institute; National Institute of Mental Health; Pierson-Lovelace Foundation; Ahmanson Foundation","keywords":"Atlas (anatomy); Probabilistic logic; Human brain; Brain atlas; Computer science; Neuroimaging; Population; Data science; Artificial intelligence; Psychology; Neuroscience; Medicine","score_opus":0.02604953511988005,"score_gpt":0.2824344816634265,"score_spread":0.25638494654354643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171191802","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":[0.007357312,0.00095980905,0.96703833,0.00091959955,0.00017063973,0.00045440305,0.0070463307,0.002588902,0.013464681],"genre_scores_gemma":[0.08296879,0.0022345567,0.88891536,0.00028138456,0.0001161758,0.001217278,0.01067409,0.0006565845,0.012935824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994935,0.00015469475,0.000055884455,0.000087821216,0.00017195854,0.000036159603],"domain_scores_gemma":[0.99940133,0.00017799591,0.00006510979,0.0001741227,0.00013421224,0.000047070982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010297092,0.0005266555,0.00044046453,0.0028852306,0.0007035754,0.0018323873,0.0011520435,0.0007663962,0.011251206],"category_scores_gemma":[0.0020298508,0.0005751355,0.00087282486,0.0031023414,0.00065644545,0.0012079823,0.0015790857,0.0011537008,0.004071717],"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":[0.00037787695,0.00014694582,0.008035217,0.00078505196,0.00028282386,0.0011584404,0.0012469401,0.054824643,0.02828441,0.1401852,0.12713255,0.63753986],"study_design_scores_gemma":[0.00010383995,0.00026752608,0.024968553,0.0002382328,0.00016195897,0.009791392,0.00030840878,0.12400827,0.0112026185,0.16324766,0.6654472,0.00025430275],"about_ca_topic_score_codex":0.0053429115,"about_ca_topic_score_gemma":0.0091714645,"teacher_disagreement_score":0.011251206,"about_ca_system_score_codex":0.0006208198,"about_ca_system_score_gemma":0.002079454,"threshold_uncertainty_score":0.037639022},"labels":[],"label_agreement":null},{"id":"W2179115332","doi":"10.1093/jamia/ocv135","title":"The vulnerabilities of computerized physician order entry systems: a qualitative study","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Workaround; Computerized physician order entry; Workflow; Usability; Order entry; SAFER; Computer science; Medicine; Test (biology); Medical emergency; Computer security; Database; Human–computer interaction","score_opus":0.04877696156344904,"score_gpt":0.4600342598501145,"score_spread":0.41125729828666546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179115332","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":[0.9903976,0.0004578005,0.002829984,0.0029236474,0.000038161194,0.00045072468,0.00016122994,0.000023188686,0.002717698],"genre_scores_gemma":[0.99563235,0.0005153426,0.0017265634,0.00080608914,0.000010019965,0.00039174102,0.000050346975,0.000026465641,0.0008411265],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9358018,0.05023932,0.0022923185,0.0020203893,0.005526303,0.0041198274],"domain_scores_gemma":[0.77624387,0.18720089,0.012119686,0.002634378,0.016723046,0.005078218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056886,0.00069581345,0.0011304672,0.0027001116,0.007506558,0.005253955,0.0023611046,0.0023626497,0.0022871187],"category_scores_gemma":[0.13491023,0.0013279737,0.00070232403,0.002127769,0.009453344,0.0070087817,0.005717392,0.0034029554,0.00029816304],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027400954,0.00007106867,0.0072870404,0.00029611978,0.000007697687,0.00041053662,0.9872115,0.000046078814,0.00034702316,0.0005378997,0.0003168284,0.0034407822],"study_design_scores_gemma":[0.0000082557635,0.00012010834,0.002940143,0.00036320806,0.000007073337,0.00020346089,0.99201596,0.00024183594,0.00029103959,0.00024146121,0.0035469155,0.00002048859],"about_ca_topic_score_codex":0.0132928,"about_ca_topic_score_gemma":0.0129643865,"teacher_disagreement_score":0.056886,"about_ca_system_score_codex":0.009796611,"about_ca_system_score_gemma":0.012967884,"threshold_uncertainty_score":0.3008455},"labels":[],"label_agreement":null},{"id":"W2181456117","doi":"10.1093/jamia/ocv053","title":"Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists, and genetic counselors","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of Victoria; Child and Family Research Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome British Columbia; Genome Canada","keywords":"Computer science; Software; Personalization; Graphical user interface; User interface; Flexibility (engineering); Data science; Human–computer interaction; World Wide Web","score_opus":0.042982088836250834,"score_gpt":0.36539148624497436,"score_spread":0.32240939740872354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181456117","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":[0.65849805,0.00072304293,0.31945035,0.00849852,0.000065475695,0.0007091825,0.00015123087,0.0022185459,0.0096856],"genre_scores_gemma":[0.7452741,0.00037697522,0.25017977,0.0011195429,0.000034413548,0.00060046685,0.00024615243,0.00042350788,0.0017450585],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.97573394,0.017946001,0.0011220173,0.0016755406,0.002749636,0.00077294774],"domain_scores_gemma":[0.924595,0.060320217,0.0028635294,0.0029242754,0.0067757596,0.0025212711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037334666,0.00075974624,0.00038259377,0.0018781304,0.002192198,0.006747961,0.002662329,0.0015241918,0.002474155],"category_scores_gemma":[0.072613806,0.0006395235,0.0005845708,0.00088768016,0.002743662,0.003805653,0.0036835277,0.0013882938,0.00076290837],"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":[0.001056078,0.0012795845,0.16463459,0.001403851,0.00015130448,0.0018240166,0.21565504,0.011421771,0.02762448,0.026956085,0.01164198,0.53635126],"study_design_scores_gemma":[0.0013247128,0.005185304,0.12730043,0.0028542976,0.0006582959,0.008587837,0.25970903,0.20257929,0.060793303,0.12065483,0.2095492,0.00080338895],"about_ca_topic_score_codex":0.0019784772,"about_ca_topic_score_gemma":0.0021976766,"teacher_disagreement_score":0.037334666,"about_ca_system_score_codex":0.0024185553,"about_ca_system_score_gemma":0.0053161196,"threshold_uncertainty_score":0.19744694},"labels":[],"label_agreement":null},{"id":"W2184549704","doi":"10.1136/amiajnl-2012-000929","title":"Discretization of continuous features in clinical datasets","year":2012,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Center for Research Resources; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Discretization; Computer science; Artificial intelligence; Unsupervised learning; Machine learning; Consistency (knowledge bases); Pattern recognition (psychology); Naive Bayes classifier; Decision tree; Class (philosophy); Data mining; Mathematics; Support vector machine","score_opus":0.010720324583806746,"score_gpt":0.3505241223265846,"score_spread":0.33980379774277786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184549704","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":[0.37335935,0.0020908157,0.6125432,0.0018596853,0.00018591461,0.00055775663,0.0056457897,0.0013383919,0.0024190003],"genre_scores_gemma":[0.65953934,0.0004080473,0.3337531,0.0002666564,0.00010644316,0.00045660228,0.00514686,0.00006829262,0.00025459714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940036,0.0030537006,0.00089444267,0.0009000397,0.0009822781,0.00016600829],"domain_scores_gemma":[0.9592677,0.03251792,0.0029134739,0.0034509168,0.0016131221,0.00023687637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008268117,0.00044264467,0.00060228555,0.00209813,0.00039052212,0.0017535265,0.00087731954,0.0006680587,0.00080678135],"category_scores_gemma":[0.036518235,0.00024785145,0.00088763115,0.002381885,0.000996105,0.0010167452,0.0010686661,0.0012607565,0.0002767689],"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":[0.001174237,0.000430708,0.1489884,0.0011032566,0.0005641008,0.00055554777,0.0012338886,0.3484147,0.007341281,0.011023733,0.0067249048,0.47244516],"study_design_scores_gemma":[0.00023056503,0.0005925148,0.053682905,0.00047561774,0.00016233142,0.0011647659,0.0007931931,0.85145795,0.010987675,0.069506705,0.010826631,0.00011921457],"about_ca_topic_score_codex":0.0016149468,"about_ca_topic_score_gemma":0.0015878765,"teacher_disagreement_score":0.008268117,"about_ca_system_score_codex":0.00093060324,"about_ca_system_score_gemma":0.0010589452,"threshold_uncertainty_score":0.043726504},"labels":[],"label_agreement":null},{"id":"W2255904424","doi":"10.1093/jamia/ocv055","title":"Improving vaccine registries through mobile technologies: a vision for mobile enhanced Immunization information systems","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto; Ottawa Hospital; University of Ottawa","funders":"","keywords":"Immunization; Mobile technology; Business; Health care; Internet privacy; Equity (law); Computer science; Information system; Public relations; Mobile computing; Medicine; Telecommunications; Political science; Immunology","score_opus":0.01002930879454604,"score_gpt":0.3050477776758954,"score_spread":0.29501846888134936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255904424","genre_codex":"commentary","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":[0.018024705,0.045133587,0.13895011,0.71884125,0.0071528587,0.00079905026,0.0009482023,0.0024178107,0.06773239],"genre_scores_gemma":[0.23056826,0.08859312,0.5614182,0.07859102,0.012855273,0.0010933245,0.0020954048,0.00038722332,0.024398312],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928652,0.0040960177,0.00049908634,0.0005023054,0.0013820811,0.00065530906],"domain_scores_gemma":[0.9673342,0.013497784,0.0025398592,0.0027831043,0.008594806,0.005250344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023805734,0.0010936304,0.00072097377,0.0037199904,0.0015921481,0.009834447,0.003217949,0.007273949,0.00786925],"category_scores_gemma":[0.031101787,0.00063387986,0.001185845,0.0024181677,0.002768795,0.019364534,0.006180457,0.005816402,0.0030031707],"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":[0.00017871459,0.00069081644,0.0067812586,0.0013585035,0.00013031183,0.00028750632,0.0018987049,0.0021654223,0.003477117,0.23809986,0.13882604,0.6061058],"study_design_scores_gemma":[0.000112528134,0.00066788285,0.0045093833,0.0021090538,0.000098177334,0.0005628688,0.0027620133,0.005417195,0.0019827422,0.13139829,0.85024023,0.00013968258],"about_ca_topic_score_codex":0.002824299,"about_ca_topic_score_gemma":0.0024696717,"teacher_disagreement_score":0.023805734,"about_ca_system_score_codex":0.0024682623,"about_ca_system_score_gemma":0.010301006,"threshold_uncertainty_score":0.1258983},"labels":[],"label_agreement":null},{"id":"W2273639774","doi":"10.1093/jamia/ocv171","title":"Potential benefit of electronic pharmacy claims data to prevent medication history errors and resultant inpatient order errors","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; University Health Network","funders":"National Center for Advancing Translational Sciences; National Institute on Aging","keywords":"Medicine; Pharmacy; Emergency medicine; Family medicine","score_opus":0.03939793719492121,"score_gpt":0.39895221677933357,"score_spread":0.35955427958441233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273639774","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":[0.9465232,0.00842791,0.01969138,0.01025717,0.0003341243,0.00074531353,0.0025861377,0.0003255366,0.0111092385],"genre_scores_gemma":[0.9888109,0.0007087303,0.008478938,0.0007420969,0.00029374793,0.00009266684,0.0006026359,0.000011998701,0.00025828034],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9802468,0.011833925,0.0021991478,0.0012353661,0.004086769,0.00039794712],"domain_scores_gemma":[0.7646034,0.16527855,0.039283566,0.013030648,0.015509737,0.002294186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024397073,0.00059961074,0.00057855033,0.0029994438,0.0003579973,0.0016450001,0.0011161132,0.001175644,0.0020938644],"category_scores_gemma":[0.17044729,0.00032166432,0.0008157705,0.0016687091,0.0005183474,0.0019706797,0.0021141556,0.0008465173,0.00024201261],"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":[0.0014085947,0.00097243924,0.7684597,0.0006940818,0.00074934034,0.00020586196,0.00026595712,0.0028623834,0.00045660496,0.00058559695,0.0014355858,0.22190392],"study_design_scores_gemma":[0.0013429204,0.0058425716,0.947619,0.002159511,0.0016937567,0.0014718897,0.00050351827,0.023019861,0.0046703615,0.0022796576,0.009287072,0.000109837885],"about_ca_topic_score_codex":0.0012284138,"about_ca_topic_score_gemma":0.0017699912,"teacher_disagreement_score":0.024397073,"about_ca_system_score_codex":0.0005578126,"about_ca_system_score_gemma":0.002318683,"threshold_uncertainty_score":0.12902558},"labels":[],"label_agreement":null},{"id":"W2274070914","doi":"10.1136/amiajnl-2014-002660","title":"An iterative evaluation of two shortened systematic review formats for clinicians: a focus group study","year":2014,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Focus group; CLARITY; Usability; Set (abstract data type); Focus (optics); Presentation (obstetrics); Medical education; Computer science; Medicine; Psychology; Human–computer interaction; Surgery","score_opus":0.4244247581500135,"score_gpt":0.5624457607647049,"score_spread":0.13802100261469136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274070914","genre_codex":"protocol","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3548289,0.007560861,0.21462739,0.009520071,0.0016530532,0.40302122,0.0016583975,0.0015922877,0.0055377614],"genre_scores_gemma":[0.11047201,0.0011476121,0.6459988,0.0011129654,0.00012074506,0.24017155,0.00040294387,0.00014235254,0.00043110937],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.32078427,0.56291616,0.064848706,0.011365357,0.036832415,0.003253079],"domain_scores_gemma":[0.14455391,0.63502973,0.039037134,0.049813613,0.12713055,0.0044350545],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.638427,0.00357169,0.004450724,0.010051562,0.0058865636,0.008071351,0.007726295,0.0048170662,0.002710571],"category_scores_gemma":[0.79541826,0.0048468746,0.0066547818,0.0071456167,0.0050837956,0.011209181,0.0122393435,0.0045898487,0.00067292975],"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":[0.009378371,0.003244985,0.0149312,0.10521239,0.0042653866,0.0012961561,0.26933354,0.004755257,0.012577913,0.005681991,0.009769976,0.55955285],"study_design_scores_gemma":[0.050387427,0.090140946,0.07941134,0.20836553,0.02825327,0.00487703,0.1968425,0.0368303,0.054608632,0.035257507,0.20988424,0.005141234],"about_ca_topic_score_codex":0.0023668692,"about_ca_topic_score_gemma":0.008243696,"teacher_disagreement_score":0.36157298,"about_ca_system_score_codex":0.02083946,"about_ca_system_score_gemma":0.049463525,"threshold_uncertainty_score":0.44588393},"labels":[],"label_agreement":null},{"id":"W2281090488","doi":"10.1136/amiajnl-2011-000089","title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","year":2011,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":861,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Engineering and Physical Sciences Research Council","keywords":"Automation; Computer science; Medicine; Psychology; Data science; Engineering","score_opus":0.022168874878972872,"score_gpt":0.37211180920015124,"score_spread":0.3499429343211784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281090488","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":[0.0009140507,0.9966672,0.00046662553,0.0003231444,0.000113854316,0.00060909026,0.0005285515,0.000012454019,0.00036493785],"genre_scores_gemma":[0.013943441,0.9809558,0.0015933875,0.0008437479,0.00010314638,0.0019390368,0.0004116668,0.000016353593,0.00019338472],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.97047573,0.010019476,0.012324925,0.0018785353,0.0048151696,0.00048624183],"domain_scores_gemma":[0.8388371,0.13300344,0.017219821,0.0019700218,0.008264789,0.0007049327],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02814198,0.0022986084,0.010039972,0.016750883,0.0011563001,0.005319113,0.002832228,0.0026846156,0.006202923],"category_scores_gemma":[0.12873116,0.0014626518,0.010705089,0.01817118,0.001639667,0.00500596,0.0029070047,0.0017611326,0.0006662085],"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":[0.00014466116,0.000013546523,0.0006392429,0.96307516,0.005872647,0.00008989937,0.00027401434,0.00005926999,0.00009450023,0.00031147792,0.0009021069,0.028523436],"study_design_scores_gemma":[0.00013038047,0.00013488389,0.002531997,0.9378685,0.044081572,0.0002530757,0.00033680254,0.000052053827,0.00016634508,0.00046915465,0.013941744,0.00003350463],"about_ca_topic_score_codex":0.008418994,"about_ca_topic_score_gemma":0.025289949,"teacher_disagreement_score":0.971858,"about_ca_system_score_codex":0.005876601,"about_ca_system_score_gemma":0.021385457,"threshold_uncertainty_score":0.14883077},"labels":[],"label_agreement":null},{"id":"W2290883466","doi":"10.1093/jamia/ocv101","title":"Design and feasibility of integrating personalized PRO dashboards into prostate cancer care","year":2015,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"U.S. National Library of Medicine; National Cancer Institute; National Institutes of Health","keywords":"Dashboard; Focus group; Medicine; Prostate cancer; Health care; Computer science; Cancer; Data science","score_opus":0.07321026347519058,"score_gpt":0.39488790947548613,"score_spread":0.3216776460002956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290883466","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":[0.6046406,0.00039377515,0.36249858,0.0017241713,0.00037105434,0.013086131,0.0009274753,0.009950036,0.006408164],"genre_scores_gemma":[0.4060572,0.00020102448,0.5857413,0.00039966046,0.000051488623,0.005119888,0.0007745355,0.00022213969,0.0014327706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98734957,0.007858799,0.0012766526,0.0013933562,0.0015252029,0.0005963824],"domain_scores_gemma":[0.96773785,0.01778515,0.0022561804,0.0044240183,0.005973607,0.0018231976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017388947,0.0012701215,0.00045714158,0.0008312414,0.00076302176,0.0023576533,0.0022001602,0.0010586673,0.00392443],"category_scores_gemma":[0.046083238,0.0007186469,0.0007621232,0.0005864294,0.0007221754,0.0022467556,0.0017872533,0.001165686,0.00058145646],"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":[0.004565005,0.011377743,0.06255917,0.0033463489,0.00043137066,0.0010263873,0.01249867,0.026815834,0.041908704,0.0041579576,0.011685849,0.81962705],"study_design_scores_gemma":[0.009365818,0.06998358,0.13681552,0.003309395,0.0022536078,0.0026079742,0.017866354,0.41169107,0.14809044,0.015567365,0.18104516,0.0014037229],"about_ca_topic_score_codex":0.0013349857,"about_ca_topic_score_gemma":0.0017296763,"teacher_disagreement_score":0.017388947,"about_ca_system_score_codex":0.00094987935,"about_ca_system_score_gemma":0.0028754033,"threshold_uncertainty_score":0.091962695},"labels":[],"label_agreement":null},{"id":"W2291361172","doi":"10.1093/jamia/ocv178","title":"Computers in the clinical encounter: a scoping review and thematic analysis","year":2016,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"McGill University","keywords":"Psychological intervention; Thematic analysis; Medicine; Medical education; Nursing; Psychology; Qualitative research","score_opus":0.09933936167878539,"score_gpt":0.556939058567157,"score_spread":0.4575996968883716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291361172","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":[0.02164425,0.940242,0.0067023444,0.0049230983,0.00073017564,0.01472719,0.002455555,0.00005022668,0.008525176],"genre_scores_gemma":[0.089743115,0.86835283,0.014499865,0.0015227882,0.00018515725,0.023417039,0.0012030586,0.00004878539,0.0010273905],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96070004,0.020885931,0.010337917,0.0012976067,0.0058660046,0.0009124454],"domain_scores_gemma":[0.89496076,0.08470065,0.008159027,0.0017632939,0.009961946,0.00045427313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046009682,0.0014896019,0.00479531,0.04322167,0.0033067209,0.0057659103,0.0024004048,0.0024596024,0.0033804805],"category_scores_gemma":[0.118825756,0.001189476,0.004784697,0.046269692,0.0032224045,0.0063878973,0.0064509716,0.0018118306,0.00040058015],"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":[0.00016297917,0.00006856463,0.0029173417,0.74930584,0.0017513408,0.000833588,0.04779458,0.00046836186,0.00046330475,0.006572012,0.0070926673,0.18256943],"study_design_scores_gemma":[0.000031088202,0.000070827475,0.0026977193,0.92388797,0.0033393116,0.00038448052,0.030174611,0.00022878329,0.00025828672,0.001657409,0.037226867,0.000042637737],"about_ca_topic_score_codex":0.008233192,"about_ca_topic_score_gemma":0.014501473,"teacher_disagreement_score":0.046009682,"about_ca_system_score_codex":0.010744976,"about_ca_system_score_gemma":0.029107234,"threshold_uncertainty_score":0.24332535},"labels":[],"label_agreement":null},{"id":"W2312279212","doi":"10.1093/jamia/ocw026","title":"Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Reproductive tract infections research","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; McGill University Health Centre","funders":"","keywords":"Medicine; Disease; Sexually transmitted disease; Virology; Internal medicine; Human immunodeficiency virus (HIV)","score_opus":0.005691257050701939,"score_gpt":0.25919622119436136,"score_spread":0.2535049641436594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312279212","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":[0.9931757,0.00015795219,0.0032017399,0.00046022132,0.000020941226,0.00008582227,0.0015853215,0.00007542333,0.0012368589],"genre_scores_gemma":[0.99590343,0.00009886677,0.0014576738,0.000026318587,0.000009541593,0.00004303984,0.0012457455,0.0000075105963,0.0012079997],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995427,0.00018269308,0.000018197674,0.00010824765,0.000050957755,0.000097203905],"domain_scores_gemma":[0.9979087,0.0011181318,0.00030202154,0.00008116297,0.00036030504,0.00022963314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025487212,0.00090185273,0.0003030254,0.00092475204,0.0005221887,0.0011765716,0.00094264955,0.0005276607,0.00414789],"category_scores_gemma":[0.0050461055,0.00035518254,0.00081834284,0.0005875729,0.00034029194,0.00035936732,0.00071323063,0.0007027351,0.00043733692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023845246,0.00016080798,0.9635575,0.000022178117,0.00017490984,0.0001798398,0.00015804409,0.027150366,0.00014736093,0.00028137863,0.0015858326,0.006343364],"study_design_scores_gemma":[0.00007996949,0.00033546955,0.43308577,0.000059991387,0.00018387359,0.00011944875,0.00033269706,0.56409186,0.00016258296,0.0004425452,0.0010701962,0.00003559915],"about_ca_topic_score_codex":0.5430753,"about_ca_topic_score_gemma":0.30971476,"teacher_disagreement_score":0.5430753,"about_ca_system_score_codex":0.002844604,"about_ca_system_score_gemma":0.0027042762,"threshold_uncertainty_score":0.9192312},"labels":[],"label_agreement":null},{"id":"W2337688125","doi":"10.1093/jamia/ocw011","title":"Electronic medical record phenotyping using the anchor and learn framework","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; U.S. Army Medical Research Acquisition Activity; National Institutes of Health; Consortia for Improving Medicine with Innovation and Technology","keywords":"Computer science; Electronic medical record; Electronic health record; Data science; Medical record; Artificial intelligence; Medicine; Internet privacy; Health care; Political science","score_opus":0.009773237003428725,"score_gpt":0.3026507515659937,"score_spread":0.29287751456256494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337688125","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":[0.09065841,0.0004347016,0.8826726,0.0012517686,0.000068642956,0.00044416054,0.008505753,0.014156113,0.0018078052],"genre_scores_gemma":[0.51248574,0.0002727774,0.46769848,0.0003568657,0.00007609581,0.0005488892,0.01716528,0.00022877814,0.0011670612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775285,0.0008557029,0.00019485704,0.00061461556,0.0004705839,0.00011150539],"domain_scores_gemma":[0.99511945,0.002836521,0.0005049937,0.0006509375,0.0006918032,0.0001962735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003407899,0.00083302427,0.0007437032,0.0031888508,0.0004984879,0.0015698912,0.0011635565,0.0010034111,0.0017130125],"category_scores_gemma":[0.012316669,0.00035070177,0.0013792469,0.0017003412,0.00053787616,0.0014137189,0.0017862703,0.0012777268,0.0010334861],"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":[0.0007243953,0.0008561452,0.14235489,0.00036911143,0.00049024925,0.00095378055,0.0006942483,0.2265034,0.004486708,0.011953139,0.017015139,0.5935987],"study_design_scores_gemma":[0.000063822765,0.00015500955,0.010333891,0.00006239059,0.000058876027,0.00022920377,0.00013438366,0.9579271,0.0020855176,0.023382416,0.005537147,0.00003016712],"about_ca_topic_score_codex":0.008913936,"about_ca_topic_score_gemma":0.0088126,"teacher_disagreement_score":0.008913936,"about_ca_system_score_codex":0.0010567573,"about_ca_system_score_gemma":0.0012746863,"threshold_uncertainty_score":0.018022895},"labels":[],"label_agreement":null},{"id":"W2338047412","doi":"10.1093/jamia/ocw013","title":"Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Manitoba Health; George & Fay Yee Centre for Healthcare Innovation; University of Manitoba","funders":"University of Manitoba","keywords":"Medicine; Medical diagnosis; Medical record; Asthma; Diabetes mellitus; Disease; Coronary artery disease; Emergency medicine; Family medicine; Internal medicine; Pathology","score_opus":0.1272788945748575,"score_gpt":0.49317949427318963,"score_spread":0.3659005996983321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338047412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906523,0.0014036868,0.0011817275,0.0009218157,0.000017102402,0.00007879787,0.0047124443,0.00002582962,0.00100628],"genre_scores_gemma":[0.99604577,0.00032003026,0.0009571736,0.0001649645,0.000016073138,0.00003063006,0.002314565,0.000006017628,0.00014480497],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9917601,0.002581381,0.0010760889,0.0007824224,0.0032461246,0.0005538534],"domain_scores_gemma":[0.9588167,0.010920714,0.016982054,0.001976442,0.010125016,0.0011789787],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0061908597,0.00030403043,0.00042826234,0.0032575948,0.0008880677,0.0014572234,0.0012107579,0.00039985563,0.0007748577],"category_scores_gemma":[0.046700526,0.0003925689,0.0002948064,0.008793802,0.00084171304,0.0008594949,0.0014111244,0.00040988327,0.00014767714],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007251172,0.0000042092342,0.99832803,0.000022356193,0.00002790431,0.000013881594,0.00016106671,0.000049493257,0.00002975445,0.00001618536,0.00012649556,0.0012134665],"study_design_scores_gemma":[0.0000017823094,0.000009677426,0.99892175,0.000036945123,0.000012626317,0.000046059748,0.00032876144,0.00031678492,0.00004063262,0.000015731921,0.00026681638,0.000002362832],"about_ca_topic_score_codex":0.5839733,"about_ca_topic_score_gemma":0.61600035,"teacher_disagreement_score":0.9996002,"about_ca_system_score_codex":0.00598156,"about_ca_system_score_gemma":0.0074867723,"threshold_uncertainty_score":0.8369535},"labels":[],"label_agreement":null},{"id":"W2341742916","doi":"10.1093/jamia/ocw031","title":"An in silico framework for integrating epidemiologic and genetic evidence with health care applications: ventilation-related pneumothorax as a case illustration","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Child and Family Research Institute; BC Children's Hospital; University of British Columbia","funders":"","keywords":"Medicine; Pneumothorax; Health care; Intensive care medicine; Emergency medicine; Database; Surgery","score_opus":0.022337358486827646,"score_gpt":0.3749011330132267,"score_spread":0.35256377452639903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341742916","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":[0.01638182,0.0009957283,0.9640955,0.008263281,0.00018758488,0.00077830936,0.0019748842,0.00046898844,0.0068539614],"genre_scores_gemma":[0.15868562,0.0011293254,0.83423424,0.0014001089,0.0002593196,0.0012389121,0.0014233238,0.000093061186,0.0015361256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9815076,0.014704286,0.0010028776,0.001256454,0.0012578712,0.00027082048],"domain_scores_gemma":[0.86393833,0.1265697,0.0034169434,0.0031052148,0.0022666978,0.0007030543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028476082,0.0016571877,0.0012410708,0.0060499758,0.0012837945,0.006054608,0.0034481648,0.0037312098,0.0067985426],"category_scores_gemma":[0.092301294,0.0012118743,0.0036951993,0.0028664907,0.0032295117,0.0026434425,0.0057972115,0.0028372558,0.0006960419],"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":[0.00053548435,0.0004877984,0.053316128,0.0018556315,0.0016729075,0.011802198,0.002444293,0.3439212,0.002896412,0.5167044,0.0055271056,0.05883652],"study_design_scores_gemma":[0.00021029581,0.00023128062,0.002704219,0.00075436407,0.00074232003,0.0035744337,0.0008361987,0.57712907,0.0012387254,0.3854096,0.027055683,0.00011381311],"about_ca_topic_score_codex":0.008704009,"about_ca_topic_score_gemma":0.0077399905,"teacher_disagreement_score":0.028476082,"about_ca_system_score_codex":0.0019060948,"about_ca_system_score_gemma":0.004700732,"threshold_uncertainty_score":0.15059769},"labels":[],"label_agreement":null},{"id":"W2356882517","doi":"10.1093/jamia/ocw028","title":"Learning statistical models of phenotypes using noisy labeled training data","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":165,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Human Genome Research Institute","keywords":"Computer science; Scalability; Machine learning; Artificial intelligence; Logistic regression; Feature (linguistics); Phenotype; Feature engineering; Implementation; Predictive modelling; Data mining; Deep learning; Database","score_opus":0.04244371233329072,"score_gpt":0.31999571476336974,"score_spread":0.27755200243007905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356882517","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":[0.101875655,0.00018483626,0.8935858,0.000583356,0.000029435025,0.0001464031,0.0014603428,0.001462473,0.0006716987],"genre_scores_gemma":[0.67323565,0.00016916892,0.31737295,0.00040808553,0.00006600975,0.0006786002,0.0070422185,0.00015924372,0.0008681259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994936,0.0027078516,0.00032585624,0.0013210678,0.0005832041,0.00012611819],"domain_scores_gemma":[0.9611462,0.029558424,0.0032042966,0.003392693,0.002368602,0.00032989413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009586131,0.0013053176,0.0010123999,0.0018734024,0.00049926824,0.0018918872,0.0017222614,0.0011564454,0.0008825135],"category_scores_gemma":[0.04012385,0.0005809961,0.0011338784,0.0012296828,0.0011064075,0.0017439075,0.0011991485,0.0017337837,0.0005646524],"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":[0.00027446484,0.00033083803,0.043759216,0.00018642572,0.0002939466,0.000339393,0.0004146907,0.8568134,0.0019685498,0.0062000584,0.0033079688,0.08611118],"study_design_scores_gemma":[0.000024694828,0.000049973965,0.0022650815,0.000036034155,0.000027294747,0.000045004435,0.000042928463,0.9855378,0.00089625694,0.010549677,0.0005133454,0.000011918496],"about_ca_topic_score_codex":0.0038886652,"about_ca_topic_score_gemma":0.006047883,"teacher_disagreement_score":0.009586131,"about_ca_system_score_codex":0.0014320879,"about_ca_system_score_gemma":0.0017196283,"threshold_uncertainty_score":0.05069691},"labels":[],"label_agreement":null},{"id":"W2418347845","doi":"10.1093/jamia/ocw064","title":"An electronic documentation system improves the quality of admission notes: a randomized trial","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Digital Imaging in Medicine","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's College Hospital; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Documentation; Medicine; Quality (philosophy); Electronic database; Electronic medical record; Medical emergency; Computer science; Information retrieval","score_opus":0.00981897768430176,"score_gpt":0.349343254285914,"score_spread":0.3395242766016122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418347845","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":[0.9853673,0.0047788154,0.000608157,0.0006680085,0.0007563275,0.0066030505,0.0003255837,0.000080019985,0.00081268145],"genre_scores_gemma":[0.98059297,0.0030379738,0.0030914822,0.0008099981,0.0010086258,0.0099293785,0.00033431593,0.000016539328,0.0011787527],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.993583,0.0042193877,0.00075754966,0.0006792137,0.00044117038,0.00031955488],"domain_scores_gemma":[0.9881207,0.005940447,0.0036488655,0.0005996115,0.000516244,0.0011741157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005733622,0.0013100086,0.002578763,0.00078750984,0.00069881184,0.0017688324,0.0013027844,0.0029840064,0.007591878],"category_scores_gemma":[0.013938869,0.0008569578,0.002542435,0.00085680024,0.001793669,0.0017271166,0.0009170906,0.0024978302,0.0006845174],"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":[0.95694864,0.021895403,0.0013712243,0.0012488539,0.0010973593,0.00002798083,0.00007664558,0.00018982848,0.00038967238,0.00006442594,0.00027836164,0.016411642],"study_design_scores_gemma":[0.87690705,0.119205266,0.0021886271,0.00017252154,0.0006750292,0.000016494623,0.000040086506,0.0003012991,0.00019117164,0.00007644652,0.00021190499,0.000014076547],"about_ca_topic_score_codex":0.0008579686,"about_ca_topic_score_gemma":0.0009903819,"teacher_disagreement_score":0.007591878,"about_ca_system_score_codex":0.001420219,"about_ca_system_score_gemma":0.002056974,"threshold_uncertainty_score":0.030322671},"labels":[],"label_agreement":null},{"id":"W2509759502","doi":"10.1093/jamia/ocw130","title":"Technology and tuberculosis control: the OUT-TB Web experience","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Public Health; Institute for Work & Health; University of Regina; University of Toronto; Public Health Ontario","funders":"","keywords":"Dashboard; Computer science; Web application; Public health surveillance; Context (archaeology); Tuberculosis; World Wide Web; Public health; Data science; Medicine; Geography","score_opus":0.01087338088009185,"score_gpt":0.31537071196433436,"score_spread":0.3044973310842425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509759502","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":[0.411637,0.02640151,0.01893079,0.1474419,0.0016674952,0.00035316483,0.000826452,0.001588728,0.39115298],"genre_scores_gemma":[0.8173629,0.032211315,0.023101948,0.021143503,0.0010969137,0.00021643718,0.0010924955,0.0006994592,0.10307502],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99714524,0.0013130602,0.00011876472,0.0001599926,0.00079048186,0.0004724745],"domain_scores_gemma":[0.99397993,0.0022707821,0.0002473639,0.00025095543,0.00082367315,0.0024273186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005105977,0.00039328568,0.00026246044,0.0006140387,0.002677772,0.0067330175,0.00088758,0.0016378204,0.009738366],"category_scores_gemma":[0.0065720715,0.00023509726,0.00027769504,0.0010756534,0.0020824485,0.005313032,0.0039787875,0.0014736805,0.0013718329],"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":[0.00026312302,0.0022156683,0.05992437,0.0013437554,0.000048831826,0.0037414015,0.10297892,0.00043123923,0.0054599466,0.009895765,0.22460642,0.58909065],"study_design_scores_gemma":[0.00004313502,0.00045104846,0.022579681,0.0006742217,0.000034226385,0.0022926107,0.03620902,0.00057897135,0.0015227373,0.0025477689,0.93301314,0.00005347225],"about_ca_topic_score_codex":0.021749035,"about_ca_topic_score_gemma":0.04093677,"teacher_disagreement_score":0.021749035,"about_ca_system_score_codex":0.0027325011,"about_ca_system_score_gemma":0.0038364395,"threshold_uncertainty_score":0.04324484},"labels":[],"label_agreement":null},{"id":"W2516286349","doi":"10.1093/jamia/ocw111","title":"International health IT benchmarking: learning from cross-country comparisons","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Health Infoway; University of Victoria","funders":"Joint Research Centre; European Commission","keywords":"Benchmarking; Computer science; Cross country; Artificial intelligence; Data science; Business; Marketing; Economics; Demographic economics","score_opus":0.034816539094813366,"score_gpt":0.43718744729559256,"score_spread":0.4023709082007792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516286349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30424592,0.03772226,0.37302384,0.11072193,0.0045909015,0.0045515057,0.005872412,0.0015867698,0.15768446],"genre_scores_gemma":[0.80656326,0.010919211,0.16874328,0.004240188,0.0007790406,0.003113569,0.0040582465,0.00037548912,0.0012077818],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.8614256,0.114314795,0.0067464532,0.0047639487,0.009827284,0.002921944],"domain_scores_gemma":[0.7882241,0.13591464,0.015815943,0.028180994,0.028196182,0.0036681418],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.17469221,0.0015152444,0.0014836434,0.008856366,0.0021000786,0.008816966,0.0030649696,0.0017619368,0.004235117],"category_scores_gemma":[0.25554562,0.0005946094,0.0011319164,0.013900627,0.0037921204,0.019850442,0.013225031,0.003256055,0.0007002869],"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":[0.0002016118,0.000682764,0.089068584,0.0029728694,0.0007798261,0.00018690835,0.02101867,0.00692698,0.00037436202,0.073497966,0.032143686,0.7721458],"study_design_scores_gemma":[0.0003987925,0.0026644852,0.20038044,0.039125573,0.0010812912,0.00042269132,0.11845189,0.024751097,0.0041327532,0.4105515,0.19755784,0.0004817233],"about_ca_topic_score_codex":0.0039791563,"about_ca_topic_score_gemma":0.004381683,"teacher_disagreement_score":0.8253078,"about_ca_system_score_codex":0.0051563247,"about_ca_system_score_gemma":0.007911396,"threshold_uncertainty_score":0.92387176},"labels":[],"label_agreement":null},{"id":"W2517640635","doi":"10.1093/jamia/ocw107","title":"Using mobile devices for inpatient rounding and handoffs: an innovative application developed and rapidly adopted by clinicians in a pediatric hospital","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Hospital Admissions and Outcomes","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Documentation; Rounding; Audit; Mobile device; Medical emergency; Medicine; Computer science; Intensive care; Handover; World Wide Web; Telecommunications; Business","score_opus":0.014882123708942853,"score_gpt":0.33199030732980583,"score_spread":0.31710818362086296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517640635","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":[0.96036506,0.0020986532,0.025000228,0.0020327785,0.00018764858,0.0013451377,0.0008095583,0.0020487215,0.0061122305],"genre_scores_gemma":[0.88509434,0.0033459235,0.105080985,0.0012910909,0.00023935358,0.0010094043,0.0005423152,0.00014159015,0.003254931],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99871075,0.0006774549,0.00012606682,0.00011321278,0.00023992072,0.00013255811],"domain_scores_gemma":[0.99680734,0.001628645,0.0006053704,0.0001832881,0.00030772624,0.0004677235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017070466,0.00050917955,0.0002294765,0.0007482204,0.0006407144,0.00087683555,0.0005060821,0.0006852461,0.0019611174],"category_scores_gemma":[0.0059414017,0.00021321034,0.00039784712,0.00031221015,0.00038344308,0.0011351693,0.0013771161,0.00049134233,0.00051676657],"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":[0.0009850885,0.0010569525,0.090609655,0.0015792403,0.00005538542,0.0044701016,0.022369066,0.00075851154,0.017311942,0.00056174624,0.016058402,0.844184],"study_design_scores_gemma":[0.0006319532,0.020096267,0.5361132,0.006752293,0.00055953837,0.037519787,0.06865158,0.010930849,0.030963447,0.0021561794,0.28476843,0.0008564583],"about_ca_topic_score_codex":0.0008063187,"about_ca_topic_score_gemma":0.0020884243,"teacher_disagreement_score":0.0019611174,"about_ca_system_score_codex":0.00040770834,"about_ca_system_score_gemma":0.0010896408,"threshold_uncertainty_score":0.009027839},"labels":[],"label_agreement":null},{"id":"W2560421163","doi":"10.1093/jamia/ocw173","title":"Embedding Nursing Interventions into the World Health Organization’s International Classification of Health Interventions (ICHI)","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychological intervention; Terminology; Nursing Interventions Classification; Coding (social sciences); Computer science; Source code; Medicine; Data mining; Nursing; Linguistics; Mathematics","score_opus":0.02568647586048872,"score_gpt":0.40337231757784536,"score_spread":0.37768584171735664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560421163","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":[0.12353261,0.0152567355,0.6042023,0.033625565,0.010948625,0.03150979,0.03624173,0.0024148573,0.14226791],"genre_scores_gemma":[0.20660928,0.003949645,0.73602635,0.0022147873,0.0005349195,0.024220273,0.022479469,0.00034519183,0.0036201524],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9642793,0.01558624,0.008708238,0.0017694695,0.008473228,0.0011834932],"domain_scores_gemma":[0.9188621,0.036382824,0.009113253,0.008080635,0.02617332,0.0013878894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02899289,0.0012063134,0.001604941,0.013667245,0.0020580834,0.0039700423,0.0026977682,0.001538533,0.0062559634],"category_scores_gemma":[0.0992783,0.000342198,0.0023811846,0.012051372,0.0038036108,0.004921455,0.0035642681,0.0031050842,0.0013730347],"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":[0.0005320377,0.00067949115,0.07444875,0.010489826,0.00030328013,0.00025769972,0.013706714,0.0029885708,0.003017403,0.1675531,0.082394496,0.6436286],"study_design_scores_gemma":[0.0005022273,0.0015325345,0.22979881,0.025082337,0.0007036383,0.0011417893,0.028922018,0.024893664,0.005818615,0.17558353,0.5055255,0.0004952872],"about_ca_topic_score_codex":0.013401213,"about_ca_topic_score_gemma":0.013654659,"teacher_disagreement_score":0.02899289,"about_ca_system_score_codex":0.009586027,"about_ca_system_score_gemma":0.026276924,"threshold_uncertainty_score":0.15333092},"labels":[],"label_agreement":null},{"id":"W2563006377","doi":"10.1093/jamia/ocw169","title":"Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; The Quebec Population Health Research Network","funders":"Agency for Healthcare Research and Quality","keywords":"Respondent; Test (biology); Computer science; Health literacy; Numeracy; Graphics; Table (database); Affect (linguistics); Perception; Psychology; Health care; Literacy; Data mining; Communication; Computer graphics (images)","score_opus":0.01903462415663413,"score_gpt":0.3618722026524899,"score_spread":0.3428375784958558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563006377","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":[0.92720854,0.004251787,0.022527652,0.008929862,0.00045879485,0.00051733654,0.0015436298,0.003474441,0.031087855],"genre_scores_gemma":[0.97255963,0.0015946946,0.021016557,0.0017004731,0.0002830748,0.0001660981,0.00036624464,0.000091257694,0.002221815],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998998,0.0006421974,0.000081128455,0.0000731556,0.00013409782,0.00007142989],"domain_scores_gemma":[0.9908201,0.006522144,0.0016462436,0.00027113108,0.00038898637,0.00035143294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012851268,0.0007009696,0.00028673158,0.00054125034,0.0002389747,0.0012678638,0.00040309917,0.00090250344,0.02078582],"category_scores_gemma":[0.019862479,0.00022312302,0.0005118369,0.0002764751,0.00027904648,0.0015230639,0.0009573254,0.00060809794,0.0018317166],"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":[0.0071540475,0.002254678,0.14915763,0.0024372928,0.00021444814,0.00087727606,0.0042707343,0.0020915058,0.023396863,0.0017681009,0.048289675,0.75808775],"study_design_scores_gemma":[0.0038372343,0.024226157,0.676585,0.003959374,0.0016546261,0.008405778,0.014408024,0.021199256,0.055781145,0.015445021,0.17361116,0.0008871926],"about_ca_topic_score_codex":0.00031559033,"about_ca_topic_score_gemma":0.00054817897,"teacher_disagreement_score":0.02078582,"about_ca_system_score_codex":0.00021271792,"about_ca_system_score_gemma":0.00024249165,"threshold_uncertainty_score":0.069535434},"labels":[],"label_agreement":null},{"id":"W2583765458","doi":"10.1093/jamia/ocw172","title":"Shared decision-making using personal health record technology: a scoping review at the crossroads","year":2016,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Scope (computer science); MEDLINE; Knowledge management; Scarcity; Health care; Health technology; Process (computing); Medicine; Computer science; Psychology; Nursing; Medical education; Political science","score_opus":0.18654081072103568,"score_gpt":0.5295981223537732,"score_spread":0.34305731163273756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583765458","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":[0.0009783682,0.99231887,0.0014336002,0.0023341095,0.00060884474,0.00044478851,0.00018228692,0.000017362096,0.0016818913],"genre_scores_gemma":[0.011405641,0.9817124,0.003665801,0.001227533,0.00041874003,0.0010708045,0.0002455506,0.000015347821,0.00023814294],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.94155204,0.028460518,0.017440315,0.002473672,0.009044978,0.0010284939],"domain_scores_gemma":[0.6800782,0.2705069,0.015746938,0.0048595713,0.0278331,0.0009753163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05103506,0.0018409955,0.005377085,0.03305856,0.0028659427,0.009290199,0.0029242602,0.0056483406,0.004639954],"category_scores_gemma":[0.17909119,0.0017002167,0.005080574,0.035107985,0.0036465917,0.01140404,0.0050963527,0.004148481,0.0011423451],"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":[0.000097411124,0.00008125571,0.0013669672,0.58951145,0.0015555584,0.00030492622,0.0035695962,0.00046027187,0.0003861929,0.006470522,0.00887323,0.38732252],"study_design_scores_gemma":[0.000016809618,0.00006696248,0.0012338451,0.9485131,0.0017983561,0.00030139537,0.0022290992,0.00016593757,0.00027102092,0.002188067,0.043181434,0.000033880962],"about_ca_topic_score_codex":0.0060370434,"about_ca_topic_score_gemma":0.01063752,"teacher_disagreement_score":0.05103506,"about_ca_system_score_codex":0.0060524796,"about_ca_system_score_gemma":0.030252777,"threshold_uncertainty_score":0.2699024},"labels":[],"label_agreement":null},{"id":"W2584843344","doi":"10.1093/jamia/ocw178","title":"To act or not to act: responses to electronic health record prompts by family medicine clinicians","year":2016,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health","keywords":"Mental health; Psychological intervention; Workflow; Referral; Medicine; Standardization; Government (linguistics); Health care; Patient safety; Electronic health record; Nursing; Medical emergency; Psychiatry; Computer science","score_opus":0.05838529903677843,"score_gpt":0.4792312731216505,"score_spread":0.42084597408487207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584843344","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":[0.96522677,0.001178928,0.001744638,0.02555964,0.0003277077,0.0002893459,0.0003169955,0.00009859399,0.005257489],"genre_scores_gemma":[0.98806375,0.0009093397,0.0030433543,0.0063645574,0.00010616359,0.00023781,0.00014191309,0.000031348274,0.0011018133],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9736824,0.01871871,0.0025023404,0.001282346,0.0020811337,0.0017329602],"domain_scores_gemma":[0.8214768,0.13364482,0.029147625,0.0022910894,0.008453776,0.0049859113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02191859,0.0004106759,0.00045910693,0.0009884471,0.0032871533,0.0033994447,0.0010002414,0.002785201,0.0033289618],"category_scores_gemma":[0.18316127,0.00045004758,0.00036958416,0.0008987312,0.0011139457,0.0028547447,0.0034825935,0.0023606757,0.0005862254],"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":[0.0010449421,0.00053527666,0.33285168,0.0012307895,0.00009459212,0.0039624763,0.52305144,0.00050526846,0.0025594237,0.0014103866,0.0293099,0.10344385],"study_design_scores_gemma":[0.0001339415,0.00063412474,0.18898876,0.0020069818,0.00008301546,0.002154653,0.75939333,0.002405371,0.002322973,0.0019416047,0.03968961,0.00024566497],"about_ca_topic_score_codex":0.0071761208,"about_ca_topic_score_gemma":0.008357553,"teacher_disagreement_score":0.02191859,"about_ca_system_score_codex":0.0045170384,"about_ca_system_score_gemma":0.0042300024,"threshold_uncertainty_score":0.11591798},"labels":[],"label_agreement":null},{"id":"W2589527681","doi":"10.1093/jamia/ocw179","title":"Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Roche (Canada)","funders":"U.S. National Library of Medicine; National Institute on Aging; National Institutes of Health","keywords":"Certification; Medical education; Workforce; Informatics; Credentialing; Knowledge management; Professional development; Medicine; Psychology; Computer science; Political science","score_opus":0.46376266794838283,"score_gpt":0.6286304942229903,"score_spread":0.1648678262746075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589527681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"incentives","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"incentives","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98405474,0.0002753789,0.005699955,0.0024911114,0.00003927056,0.00042978537,0.00013996029,0.000022242379,0.006847579],"genre_scores_gemma":[0.993993,0.00007985205,0.0051892926,0.00013046364,0.000007292934,0.00020356408,0.00009054577,0.0000034381474,0.00030252352],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9911011,0.004629572,0.0008170557,0.00049165887,0.0020621251,0.00089848536],"domain_scores_gemma":[0.8898687,0.06734728,0.01486792,0.0036220662,0.01842193,0.0058720093],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026350794,0.00021442334,0.00021536503,0.0020494363,0.001130795,0.0017151721,0.00070618413,0.00057767506,0.002523561],"category_scores_gemma":[0.10345271,0.0001961258,0.00042505638,0.0010521205,0.0014037351,0.002172143,0.0026646738,0.0012615409,0.00032457197],"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":[0.00014036545,0.000590265,0.8744197,0.00034171366,0.000026679429,0.00011303933,0.017788267,0.00055872113,0.0008053982,0.0031838296,0.0016443762,0.10038765],"study_design_scores_gemma":[0.00002642149,0.00033512156,0.95590484,0.0005839398,0.000016022006,0.00021849034,0.032324888,0.0035079953,0.00086866366,0.0020203236,0.004164872,0.000028513488],"about_ca_topic_score_codex":0.0057317326,"about_ca_topic_score_gemma":0.007121021,"teacher_disagreement_score":0.9736492,"about_ca_system_score_codex":0.002903717,"about_ca_system_score_gemma":0.009855592,"threshold_uncertainty_score":0.13935798},"labels":[],"label_agreement":null},{"id":"W2604449159","doi":"10.1093/jamia/ocx029","title":"Using electronic medical record notes to measure ICU telemedicine utilization","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute on Aging; Office of Research and Development; Health Services Research and Development; U.S. Department of Veterans Affairs","keywords":"Telemedicine; Medicine; Intensive care unit; Medical emergency; MEDLINE; Health care; Intensive care; Health records; Medical record; Emergency medicine; Intensive care medicine","score_opus":0.07126310542178901,"score_gpt":0.41284004837606236,"score_spread":0.3415769429542733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604449159","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":[0.98224705,0.0006773995,0.0047194273,0.00034841298,0.000041165953,0.0010301053,0.0052056657,0.00010557757,0.0056251707],"genre_scores_gemma":[0.9847007,0.0005022712,0.00941154,0.00023628002,0.00008040402,0.0009811524,0.0034119475,0.000019212166,0.0006563809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98445165,0.005511489,0.0048410185,0.0010800608,0.0036159384,0.0004998473],"domain_scores_gemma":[0.91847485,0.026360167,0.040689234,0.0033644598,0.009395816,0.0017153958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009762215,0.000425259,0.00041089026,0.006248254,0.0005356999,0.00144608,0.0010976922,0.0005785876,0.001955198],"category_scores_gemma":[0.044013284,0.00034726187,0.00061549677,0.004624149,0.00034410524,0.0022185578,0.0013545977,0.0007623676,0.00050702604],"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":[0.00014878993,0.0002622483,0.9710271,0.00016967714,0.00011099113,0.000040474908,0.0006500783,0.00023986617,0.0003212404,0.000081070146,0.0006711258,0.02627732],"study_design_scores_gemma":[0.000025308467,0.0004203281,0.9948797,0.00010565742,0.00004158344,0.00016589607,0.000990559,0.0015536101,0.0006501641,0.00006952761,0.0010769761,0.000020724185],"about_ca_topic_score_codex":0.005005846,"about_ca_topic_score_gemma":0.008181651,"teacher_disagreement_score":0.009762215,"about_ca_system_score_codex":0.0012602366,"about_ca_system_score_gemma":0.0014422423,"threshold_uncertainty_score":0.051628113},"labels":[],"label_agreement":null},{"id":"W2752015129","doi":"10.1093/jamia/ocx078","title":"Assessing the quality of administrative data for research: a framework from the Manitoba Centre for Health Policy","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Institute for Clinical Evaluative Sciences; University of Manitoba; Manitoba Health","funders":"","keywords":"Data quality; Quality (philosophy); Health data; Political science; Data science; Computer science; Public administration; Business; Health care","score_opus":0.5106963147270723,"score_gpt":0.5625947614232785,"score_spread":0.051898446696206135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752015129","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":[0.008211501,0.024848389,0.21898435,0.7021182,0.0014971629,0.0029064803,0.0024746645,0.00027779202,0.03868144],"genre_scores_gemma":[0.19026323,0.016407268,0.7284574,0.04905158,0.0018019252,0.007615402,0.0018857543,0.00025601414,0.0042613153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.582476,0.29428858,0.027847653,0.01255195,0.075607866,0.0072278967],"domain_scores_gemma":[0.5534731,0.24341509,0.024816103,0.029545393,0.12690865,0.02184173],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.37566283,0.0019913104,0.0044812975,0.029071435,0.01720452,0.04830616,0.011924231,0.011176472,0.0017489619],"category_scores_gemma":[0.32331395,0.0036650402,0.0036437307,0.03950589,0.04708635,0.014656574,0.026081463,0.016212566,0.0005194796],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004238306,0.000070975424,0.010068805,0.00074495055,0.00021399086,0.00024099505,0.007973424,0.0026863194,0.00012681949,0.9316387,0.01981028,0.026382357],"study_design_scores_gemma":[0.00015611741,0.00017143216,0.018514529,0.013735544,0.00036179533,0.00041526862,0.00977601,0.010336588,0.0005442931,0.6524312,0.29312357,0.00043371905],"about_ca_topic_score_codex":0.47643942,"about_ca_topic_score_gemma":0.4580916,"teacher_disagreement_score":0.8914958,"about_ca_system_score_codex":0.108504176,"about_ca_system_score_gemma":0.33591995,"threshold_uncertainty_score":0.94733244},"labels":[],"label_agreement":null},{"id":"W2764220466","doi":"10.1093/jamia/ocx107","title":"Improving patient safety and efficiency of medication reconciliation through the development and adoption of a computer-assisted tool with automated electronic integration of population-based community drug data: the RightRx project","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa; McGill University Health Centre; Centre Hospitalier de l’Université de Montréal; McGill University","funders":"","keywords":"Medicine; Population; Intervention (counseling); Medical emergency; Accreditation; Patient safety; Workload; Medical record; Nursing; Health care; Medical education; Computer science; Surgery","score_opus":0.051914777885041445,"score_gpt":0.3618892308160921,"score_spread":0.30997445293105064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764220466","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":[0.9611254,0.00040175664,0.028906927,0.0014203554,0.000056988138,0.0058630444,0.00026541261,0.00072393165,0.0012362777],"genre_scores_gemma":[0.75310063,0.00053409464,0.23868303,0.00059009047,0.00006498481,0.0056653526,0.00044321647,0.00009517785,0.0008234706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9652604,0.027587984,0.001853798,0.001281985,0.0033100767,0.0007058547],"domain_scores_gemma":[0.96018374,0.023899995,0.005519616,0.004041211,0.0039558345,0.0023995184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03572,0.0007145529,0.0006732473,0.0009168875,0.0005068434,0.0016685502,0.0015488944,0.00076095195,0.0017416155],"category_scores_gemma":[0.056085676,0.00052785943,0.0012471795,0.0005902824,0.0009550087,0.0015959722,0.0031478445,0.0010399865,0.0002602502],"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":[0.010545789,0.024665162,0.061530445,0.002037785,0.0007260183,0.00021294049,0.004031478,0.0084807845,0.0087370565,0.0007000589,0.0036741483,0.8746583],"study_design_scores_gemma":[0.06895892,0.2664629,0.394437,0.0027673081,0.0040796762,0.0021172876,0.0061579337,0.13559255,0.0768332,0.0030300685,0.03869833,0.00086482835],"about_ca_topic_score_codex":0.0021138105,"about_ca_topic_score_gemma":0.0014871194,"teacher_disagreement_score":0.03572,"about_ca_system_score_codex":0.00088836934,"about_ca_system_score_gemma":0.0058646905,"threshold_uncertainty_score":0.18890768},"labels":[],"label_agreement":null},{"id":"W2766934786","doi":"10.1093/jamia/ocx125","title":"Using machine learning for sequence-level automated MRI protocol selection in neuroradiology","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Neuroradiology; Protocol (science); Computer science; Sequence (biology); Selection (genetic algorithm); Artificial intelligence; Medical physics; Machine learning; Medicine; Neurology; Pathology","score_opus":0.07076211044507233,"score_gpt":0.4110584039466168,"score_spread":0.34029629350154444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766934786","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":[0.1783672,0.0011164653,0.8125268,0.00093845197,0.00014779813,0.00040700383,0.0004507681,0.004901586,0.0011438701],"genre_scores_gemma":[0.5090228,0.00034929224,0.48806393,0.00033177508,0.00014377039,0.00021169611,0.0008656815,0.00017681243,0.0008342766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660504,0.0021304195,0.00028165444,0.0003528836,0.0005220606,0.00010790821],"domain_scores_gemma":[0.98580617,0.009416586,0.0014374374,0.0010292927,0.0021194087,0.00019102074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052996823,0.0007515025,0.0007199308,0.0020370726,0.00048430724,0.0009582406,0.0008874235,0.0008693264,0.0007206035],"category_scores_gemma":[0.023598548,0.00034583677,0.0004656445,0.0011017874,0.0004160663,0.0011704498,0.0005444589,0.00097540335,0.0008005557],"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":[0.0006833926,0.0005998767,0.025391428,0.00022683987,0.00011260831,0.00039911654,0.00022992038,0.14720964,0.020119479,0.001445108,0.006257756,0.7973248],"study_design_scores_gemma":[0.000047251342,0.00025517514,0.0038819518,0.000040056784,0.00003474185,0.00028689456,0.000056677014,0.9663621,0.019197885,0.00751947,0.0022762318,0.00004155629],"about_ca_topic_score_codex":0.0024107804,"about_ca_topic_score_gemma":0.003739127,"teacher_disagreement_score":0.0052996823,"about_ca_system_score_codex":0.00065521157,"about_ca_system_score_gemma":0.00223016,"threshold_uncertainty_score":0.028027773},"labels":[],"label_agreement":null},{"id":"W2791162515","doi":"10.1093/jamia/ocx153","title":"A snapshot of health information exchange across five nations: an investigation of frontline clinician experiences in emergency care","year":2017,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Medical School, University of Michigan; University of Michigan","keywords":"Health information exchange; Medicine; SAFER; Thematic analysis; Health care; Medical emergency; Information exchange; Nursing; Family medicine; Qualitative research; Health information","score_opus":0.057061704132300974,"score_gpt":0.4790597728240273,"score_spread":0.4219980686917263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791162515","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":[0.99289435,0.00060850853,0.00044675422,0.0024896823,0.00003189993,0.000049427563,0.00010919169,0.000011580261,0.00335858],"genre_scores_gemma":[0.99704427,0.0006329513,0.0005724445,0.0010597944,0.000011837995,0.000054276006,0.00008443456,0.000011716089,0.00052814145],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.98750055,0.009352434,0.0006695198,0.00050176505,0.00065318076,0.0013225038],"domain_scores_gemma":[0.9780578,0.0118461065,0.0036858728,0.00083904393,0.0021303915,0.003440808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011363557,0.0003409443,0.00046865674,0.0017128119,0.008715041,0.005850862,0.001326887,0.0018487542,0.003132204],"category_scores_gemma":[0.026787499,0.000765375,0.00031198908,0.0020196105,0.004784209,0.0076947226,0.009471257,0.002725085,0.00027473347],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003452231,0.000033238844,0.014495041,0.00010828954,0.000005936242,0.0010899897,0.9789661,0.000018161089,0.00018874275,0.00049834495,0.00075381435,0.0038078553],"study_design_scores_gemma":[0.0000026092562,0.0000375035,0.006460852,0.000098116325,0.000002590838,0.00034779124,0.9897854,0.000032462147,0.00005100987,0.0000768567,0.003096172,0.000008734486],"about_ca_topic_score_codex":0.019054389,"about_ca_topic_score_gemma":0.033471458,"teacher_disagreement_score":0.019054389,"about_ca_system_score_codex":0.0060127084,"about_ca_system_score_gemma":0.005951975,"threshold_uncertainty_score":0.06009692},"labels":[],"label_agreement":null},{"id":"W2792567537","doi":"10.1093/jamia/ocy015","title":"Usage and accuracy of medication data from nationwide health information exchange in Quebec, Canada","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; McGill University Health Centre; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Health information exchange; Medicine; Health data; Health information; Computer science; Family medicine; Health care; Political science","score_opus":0.037684282822052745,"score_gpt":0.40987550285156227,"score_spread":0.37219122002950955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792567537","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":[0.94259506,0.0037700364,0.0011510677,0.0032656032,0.000044126824,0.0002663242,0.03677773,0.00013539751,0.011994491],"genre_scores_gemma":[0.98612994,0.0013200627,0.0014152753,0.0005130314,0.000019535715,0.00006162644,0.008440814,0.000021802416,0.0020778812],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99437296,0.0008791606,0.0006540916,0.00048470203,0.0030449969,0.00056403055],"domain_scores_gemma":[0.966076,0.00575011,0.0075165774,0.0010700385,0.01779711,0.0017902198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003966267,0.00027076012,0.00039559347,0.0029754725,0.0015959645,0.0020493008,0.0014046156,0.0003854412,0.0025893373],"category_scores_gemma":[0.021382563,0.0002279446,0.00036917833,0.010525723,0.00053874584,0.00093455333,0.0008869049,0.00054940145,0.00033063497],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011059244,0.000051653613,0.9589289,0.00020928595,0.00008052418,0.00009186482,0.0013126084,0.0003544514,0.00022472408,0.0001454803,0.004321944,0.034167897],"study_design_scores_gemma":[0.000005721489,0.00002484363,0.9950494,0.00011117887,0.00001639707,0.00004212014,0.00087594864,0.0007470499,0.00012371382,0.000010412787,0.0029796653,0.000013489766],"about_ca_topic_score_codex":0.99140537,"about_ca_topic_score_gemma":0.9925644,"teacher_disagreement_score":0.035016302,"about_ca_system_score_codex":0.035016302,"about_ca_system_score_gemma":0.03547933,"threshold_uncertainty_score":0.25406224},"labels":[],"label_agreement":null},{"id":"W2797078590","doi":"10.1093/jamia/ocy021","title":"UMLS to DBPedia link discovery through circular resolution","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unified Medical Language System; Computer science; Information retrieval; Annotation; Set (abstract data type); Simple Knowledge Organization System; Natural language processing; Ontology; Thesaurus; Artificial intelligence; Semantic Web; RDF; SPARQL","score_opus":0.010433604294806196,"score_gpt":0.291618565749867,"score_spread":0.2811849614550608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797078590","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":[0.007386687,0.0008117158,0.9570989,0.0009760852,0.0003802781,0.00044305963,0.00720324,0.016620727,0.00907934],"genre_scores_gemma":[0.04573123,0.00075698976,0.9171794,0.0007810955,0.00011531794,0.00042320363,0.02780839,0.002219431,0.0049849623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9876715,0.003998433,0.0013182622,0.0030029297,0.0035863996,0.0004225342],"domain_scores_gemma":[0.9795257,0.0073436643,0.0014950025,0.006428531,0.004753102,0.00045408015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010729731,0.0018603994,0.0011090371,0.012053199,0.003137649,0.0066426634,0.0035221498,0.0016634278,0.006526298],"category_scores_gemma":[0.03908367,0.0014964252,0.0028787586,0.009357738,0.0015107032,0.007344742,0.011173941,0.0033625814,0.0060562184],"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":[0.00050865207,0.00048307382,0.0070653297,0.0026245902,0.0007922096,0.0020382241,0.004877807,0.027663764,0.012033193,0.101608045,0.17617318,0.66413194],"study_design_scores_gemma":[0.00012473039,0.00009552791,0.0035254434,0.000987405,0.0004446152,0.0012924494,0.0032405464,0.24945459,0.055528812,0.16049545,0.5245087,0.0003018166],"about_ca_topic_score_codex":0.019135382,"about_ca_topic_score_gemma":0.020416338,"teacher_disagreement_score":0.019135382,"about_ca_system_score_codex":0.0017776773,"about_ca_system_score_gemma":0.0057005994,"threshold_uncertainty_score":0.056744933},"labels":[],"label_agreement":null},{"id":"W2809924027","doi":"10.1093/jamia/ocy074","title":"A systematic assessment of the availability and clinical drug information coverage of machine-readable clinical drug data sources for building knowledge translation products","year":2018,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"Canadian Institutes of Health Research; Memorial University of Newfoundland; Diabetes Canada","keywords":"Computer science; Drug; Information retrieval; Data source; Information source (mathematics); Knowledge translation; Quality (philosophy); Medicine; Knowledge management; Pharmacology","score_opus":0.07106297838884242,"score_gpt":0.4324555816785575,"score_spread":0.3613926032897151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809924027","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":[0.16991395,0.75733,0.013868561,0.006560697,0.00063512917,0.0077639935,0.0352371,0.0003692137,0.0083213905],"genre_scores_gemma":[0.6939887,0.19684169,0.0679181,0.0025798036,0.00032902736,0.012884837,0.024708616,0.00021088621,0.00053843047],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.80588,0.068097025,0.08433777,0.0074357027,0.033161584,0.0010879264],"domain_scores_gemma":[0.24594533,0.5887712,0.09388058,0.017613797,0.052023835,0.0017652761],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1660717,0.0010297294,0.004729469,0.07635746,0.0014128314,0.0058456417,0.0033282596,0.00235592,0.0021982484],"category_scores_gemma":[0.5018284,0.0013980502,0.004944848,0.04066631,0.002810975,0.008450396,0.0067269676,0.0010981587,0.00038819725],"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":[0.0015123171,0.00017081405,0.077130266,0.574831,0.013493618,0.00073870487,0.0077076475,0.0007044531,0.0018126107,0.0020794272,0.0074783172,0.3123409],"study_design_scores_gemma":[0.00075463305,0.0007626868,0.11798416,0.7574155,0.04461031,0.0017902973,0.004779914,0.0021327685,0.0038883486,0.0017624884,0.06386676,0.00025222846],"about_ca_topic_score_codex":0.0045545353,"about_ca_topic_score_gemma":0.012627743,"teacher_disagreement_score":0.8339283,"about_ca_system_score_codex":0.004413328,"about_ca_system_score_gemma":0.0154416375,"threshold_uncertainty_score":0.8782816},"labels":[],"label_agreement":null},{"id":"W2888271319","doi":"10.1093/jamia/ocy094","title":"Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review","year":2018,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of British Columbia; University of Alberta; University of Calgary","funders":"","keywords":"Metric (unit); MEDLINE; Medicine; Diagnosis code; Medical record; Identification (biology); Systematic review; Primary care; Electronic medical record; Data mining; Computer science; Database; Information retrieval; Family medicine; Population","score_opus":0.19899050549928676,"score_gpt":0.5017041764263859,"score_spread":0.3027136709270991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888271319","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":[0.0043590036,0.98503774,0.0014122174,0.0007107353,0.00027058495,0.0036490196,0.0037843448,0.000036745976,0.00073954376],"genre_scores_gemma":[0.06184936,0.91297966,0.008186495,0.0016422843,0.0002320736,0.010699872,0.004158228,0.00004087328,0.00021119541],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9147155,0.027289834,0.04146996,0.0041873534,0.011459509,0.0008777754],"domain_scores_gemma":[0.64933115,0.25957087,0.06038695,0.0077946666,0.021996533,0.00091979123],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04721065,0.001667379,0.009257782,0.025806926,0.0013485304,0.004137782,0.0040687523,0.0027859742,0.004571926],"category_scores_gemma":[0.2631785,0.0016080667,0.009127945,0.022817686,0.002294459,0.0063203513,0.0032552625,0.0015925234,0.0005095502],"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":[0.000119462406,0.000016597935,0.002991964,0.9605569,0.00524603,0.000099995894,0.00038158076,0.00008156758,0.00008532083,0.00036892729,0.0018777407,0.02817396],"study_design_scores_gemma":[0.00013827004,0.000104462415,0.006211652,0.96087664,0.021484468,0.00032162332,0.0004399773,0.000107697866,0.00017706338,0.00029729327,0.009798477,0.000042373078],"about_ca_topic_score_codex":0.009509595,"about_ca_topic_score_gemma":0.024218531,"teacher_disagreement_score":0.95278937,"about_ca_system_score_codex":0.007471343,"about_ca_system_score_gemma":0.023693308,"threshold_uncertainty_score":0.24967676},"labels":[],"label_agreement":null},{"id":"W2895296143","doi":"10.1093/jamia/ocy114","title":"Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"U.S. National Library of Medicine; National Institutes of Health; National Cancer Institute; Medical Research Council; Engineering and Physical Sciences Research Council; China Scholarship Council","keywords":"Normalization (sociology); Computer science; Social media; F1 score; Artificial intelligence; Machine learning; Identifier; Task (project management); Natural language processing; Context (archaeology); World Wide Web","score_opus":0.07127753204526234,"score_gpt":0.3749801132463712,"score_spread":0.3037025812011089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895296143","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":[0.56510705,0.0038700544,0.17170909,0.008664081,0.0022704538,0.0080083925,0.15110122,0.07682817,0.01244148],"genre_scores_gemma":[0.39714444,0.00054798264,0.282807,0.0010867299,0.0006360415,0.0059340936,0.30502766,0.0015799316,0.0052361153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891244,0.0036082698,0.0012307062,0.0027335428,0.0026236381,0.00067943305],"domain_scores_gemma":[0.9813225,0.0074885176,0.0013044697,0.0039135823,0.004768488,0.0012024243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011738509,0.0030967603,0.0016829089,0.0035542077,0.0025324805,0.002063532,0.0033449202,0.0033557955,0.004516674],"category_scores_gemma":[0.028815836,0.0008730295,0.0025458473,0.0022861713,0.0010382032,0.0042167674,0.006458457,0.0030513736,0.00702969],"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":[0.0038533795,0.0057306136,0.073819585,0.003755862,0.0010382418,0.0013857476,0.002847623,0.02836529,0.058630776,0.002927263,0.23008624,0.5875594],"study_design_scores_gemma":[0.0013041801,0.00275989,0.09335748,0.00062844885,0.0007864939,0.0014123211,0.0027871681,0.6000148,0.1403638,0.011423813,0.14451535,0.0006462212],"about_ca_topic_score_codex":0.012610263,"about_ca_topic_score_gemma":0.016809737,"teacher_disagreement_score":0.012610263,"about_ca_system_score_codex":0.002425002,"about_ca_system_score_gemma":0.003786204,"threshold_uncertainty_score":0.062079906},"labels":[],"label_agreement":null},{"id":"W2902257721","doi":"10.1093/jamia/ocy145","title":"Physician stress and burnout: the impact of health information technology","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare professionals’ stress and burnout","field":"Health Professions","cited_by":571,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Rhode Island Department of Health","keywords":"Burnout; Medicine; Odds; Logistic regression; Respondent; Electronic health record; Odds ratio; Family medicine; Demographics; Health care; Demography; Clinical psychology; Internal medicine","score_opus":0.018405318717675617,"score_gpt":0.4177124173925757,"score_spread":0.3993070986749001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902257721","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":[0.9954521,0.0012973598,0.0002794195,0.0009334686,0.00002697654,0.000017137394,0.00012529983,0.000006542284,0.0018616761],"genre_scores_gemma":[0.9991999,0.00032089432,0.00014693702,0.00008638409,0.00003555011,0.000008970013,0.00006970856,0.0000013074258,0.00013028069],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981152,0.0010857746,0.00012775353,0.000119404955,0.00035046574,0.0002015152],"domain_scores_gemma":[0.9934123,0.0020622322,0.0030329074,0.00013183385,0.00055810757,0.00080249546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015974994,0.00023385159,0.00016060767,0.00053237187,0.00034892178,0.00073303544,0.00017930985,0.0004442906,0.0018720502],"category_scores_gemma":[0.007949666,0.00015281627,0.000338001,0.00039553276,0.00037111682,0.0004030212,0.00083401037,0.00045704466,0.00025570273],"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":[0.00012598615,0.00013830395,0.98844224,0.00007664832,0.00009977186,0.00005531095,0.00042845198,0.00013380178,0.00034827885,0.000042616197,0.00032591925,0.009782568],"study_design_scores_gemma":[0.000003171428,0.00012632538,0.998857,0.00004472305,0.000015526828,0.00008080277,0.00030588024,0.00020056272,0.000059787806,0.00003078161,0.0002720763,0.000003285314],"about_ca_topic_score_codex":0.00326959,"about_ca_topic_score_gemma":0.002845179,"teacher_disagreement_score":0.00326959,"about_ca_system_score_codex":0.0004630428,"about_ca_system_score_gemma":0.0006099031,"threshold_uncertainty_score":0.008448482},"labels":[],"label_agreement":null},{"id":"W2904745162","doi":"10.1093/jamia/ocy153","title":"Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; BC Children's Hospital; Michael Smith Health Research BC; BC Children’s Hospital Foundation; Children's Hospital Foundation; Canadian Institutes of Health Research; Genome Canada","keywords":"Workflow; Computer science; Prioritization; Cognition; Exome sequencing; Precision medicine; Artificial intelligence; Data science; Machine learning; Bioinformatics; Medicine; Phenotype; Gene; Genetics; Biology; Database; Management science; Engineering","score_opus":0.008921778719840325,"score_gpt":0.26729900106886256,"score_spread":0.2583772223490222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904745162","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":[0.87341267,0.00008966448,0.116126925,0.00022673335,0.00004538037,0.0028881798,0.00052546826,0.0051867347,0.0014981673],"genre_scores_gemma":[0.74880207,0.000088449386,0.2467985,0.00016783191,0.000013698753,0.0019630757,0.00096438406,0.0002482634,0.0009537696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953934,0.0028228746,0.00043934767,0.0006231011,0.00048979156,0.00023143827],"domain_scores_gemma":[0.9765968,0.015947316,0.00086682715,0.002205113,0.0032018085,0.0011821339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01242234,0.0014838305,0.00063730025,0.0007691958,0.00057724904,0.0017948416,0.00221228,0.0011183704,0.0038086565],"category_scores_gemma":[0.02715352,0.0005148582,0.00073808874,0.00036802996,0.00067754055,0.0011007393,0.0014507882,0.0006805511,0.00085352786],"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":[0.022228753,0.018000986,0.09833225,0.0046943524,0.000673543,0.0031666427,0.03561648,0.06781657,0.15515456,0.0027873393,0.010500095,0.58102846],"study_design_scores_gemma":[0.007588609,0.039012752,0.11302451,0.0013274498,0.0010913532,0.0036248816,0.01292126,0.6337088,0.1424903,0.0068684756,0.037017558,0.001324184],"about_ca_topic_score_codex":0.0024439667,"about_ca_topic_score_gemma":0.0018680735,"teacher_disagreement_score":0.01242234,"about_ca_system_score_codex":0.0009552253,"about_ca_system_score_gemma":0.0018094642,"threshold_uncertainty_score":0.06569636},"labels":[],"label_agreement":null},{"id":"W2905733110","doi":"10.1093/jamia/ocy143","title":"Designing a medication timeline for patients and physicians","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"Agency for Healthcare Research and Quality; University of Missouri; California Health Care Foundation","keywords":"Timeline; Computer science; Visualization; Process (computing); Multidisciplinary approach; Data visualization","score_opus":0.019163149795894018,"score_gpt":0.4016454906940264,"score_spread":0.3824823408981324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905733110","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":[0.12855521,0.00081298477,0.8375469,0.0050882204,0.0005967452,0.002239701,0.00049294083,0.014320923,0.010346451],"genre_scores_gemma":[0.19391863,0.00037873455,0.79850346,0.00084953394,0.000175026,0.0013488154,0.0004045462,0.000520418,0.0039007463],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9950389,0.0028562902,0.00047487667,0.0005812365,0.00080797577,0.00024066657],"domain_scores_gemma":[0.98135006,0.01044008,0.0017758101,0.0015552782,0.0036407106,0.0012379477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073490995,0.001044671,0.00035304308,0.0006674118,0.0011934373,0.0025883964,0.0014119531,0.0016103182,0.006653651],"category_scores_gemma":[0.028749181,0.0007558755,0.0006615606,0.00049355347,0.000854077,0.0040731463,0.0018159454,0.0011234616,0.0012450811],"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":[0.0018825174,0.0013640668,0.023010943,0.0034441403,0.00016243981,0.0017117539,0.032182258,0.0085769575,0.07195233,0.013213213,0.046571728,0.79592764],"study_design_scores_gemma":[0.002660962,0.011418985,0.038021185,0.0031937757,0.00065113977,0.0066989306,0.017141912,0.085830495,0.09565528,0.020111287,0.71791947,0.00069657055],"about_ca_topic_score_codex":0.0010197073,"about_ca_topic_score_gemma":0.0013306449,"teacher_disagreement_score":0.0073490995,"about_ca_system_score_codex":0.0008602676,"about_ca_system_score_gemma":0.0022725058,"threshold_uncertainty_score":0.038866222},"labels":[],"label_agreement":null},{"id":"W2906777138","doi":"10.1093/jamia/ocy164","title":"Enrichment sampling for a multi-site patient survey using electronic health records and census data","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Human Genome Research Institute; Canada Excellence Research Chairs, Government of Canada; National Heart, Lung, and Blood Institute; Cincinnati Children's Hospital Medical Center; Children's Hospital of Philadelphia","keywords":"Census; Sampling frame; Stratified sampling; Sampling (signal processing); Sampling design; Sample size determination; Demography; Ethnic group; Sample (material); Survey data collection; American Community Survey; Medicine; Survey sampling; Geography; Health equity; Gerontology; Statistics; Environmental health; Computer science; Public health; Population; Mathematics; Political science; Sociology; Pathology","score_opus":0.3533000150091042,"score_gpt":0.5169291976826397,"score_spread":0.1636291826735355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906777138","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":[0.06939347,0.00026230662,0.8968433,0.0004635251,0.00013978603,0.028237866,0.0014022072,0.00058750005,0.0026699074],"genre_scores_gemma":[0.16113712,0.00013837853,0.80292314,0.00063971727,0.00010630774,0.032830708,0.0013915403,0.000044434448,0.00078867853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9089573,0.07904023,0.0027138996,0.0033758152,0.005275718,0.00063697714],"domain_scores_gemma":[0.95585793,0.023098242,0.007103501,0.008854938,0.004284544,0.0008008933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046277903,0.0008682978,0.00091737683,0.00237005,0.001336812,0.00095717126,0.0018364639,0.0008649308,0.0027773278],"category_scores_gemma":[0.08782663,0.00086635904,0.0010255445,0.0020693443,0.0011120935,0.0009711758,0.0035109012,0.00072236813,0.001016046],"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":[0.002834899,0.0015604335,0.48302314,0.002047686,0.0017539007,0.00080932846,0.005623728,0.024473095,0.010894938,0.050055005,0.016490303,0.4004335],"study_design_scores_gemma":[0.0024729103,0.010654194,0.4162766,0.0017655776,0.0013320182,0.0016966193,0.003736241,0.3555713,0.01762708,0.07132912,0.11717047,0.0003678316],"about_ca_topic_score_codex":0.004377043,"about_ca_topic_score_gemma":0.0066709947,"teacher_disagreement_score":0.046277903,"about_ca_system_score_codex":0.0014364921,"about_ca_system_score_gemma":0.002978507,"threshold_uncertainty_score":0.24474382},"labels":[],"label_agreement":null},{"id":"W2917277095","doi":"10.1093/jamia/ocy189","title":"deepBioWSD: effective deep neural word sense disambiguation of biomedical text data","year":2018,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Natural language processing; Initialization; Pipeline (software); Language model; Machine learning; Vocabulary; Word-sense disambiguation; Deep learning; Word (group theory); Artificial neural network; Lexicon; Unified Medical Language System; Classifier (UML)","score_opus":0.011372290631705834,"score_gpt":0.30435754034277795,"score_spread":0.2929852497110721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917277095","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":[0.17357253,0.009199153,0.7341462,0.0045436122,0.0018100425,0.0006663987,0.030711997,0.037683282,0.0076668453],"genre_scores_gemma":[0.47708204,0.0025043755,0.45521098,0.002395211,0.00036429343,0.0006281326,0.050677188,0.0006466548,0.01049122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993649,0.00010978229,0.00007887582,0.00022499543,0.00016766386,0.00005386183],"domain_scores_gemma":[0.9992324,0.00031153593,0.0001239702,0.00015455244,0.00012482163,0.000052633644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001191108,0.0014151211,0.0006841019,0.0019500426,0.00060267886,0.0012003033,0.0019674823,0.0011399965,0.0024603023],"category_scores_gemma":[0.003246965,0.0004811062,0.0011447748,0.0015320355,0.0007574344,0.0023176486,0.0024001913,0.0018495473,0.0016898907],"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":[0.00086311897,0.0005066109,0.008128297,0.0013222242,0.00039097303,0.0008402063,0.0005960848,0.11071963,0.0217737,0.009623638,0.0801665,0.7650689],"study_design_scores_gemma":[0.00015504113,0.0001811398,0.0023990374,0.00012558908,0.000085837775,0.00027874316,0.00025737152,0.9171486,0.024059743,0.031738594,0.023501657,0.00006864252],"about_ca_topic_score_codex":0.007474564,"about_ca_topic_score_gemma":0.011462392,"teacher_disagreement_score":0.007474564,"about_ca_system_score_codex":0.001069241,"about_ca_system_score_gemma":0.0018223616,"threshold_uncertainty_score":0.01486212},"labels":[],"label_agreement":null},{"id":"W2944497099","doi":"10.1093/jamia/ocz054","title":"Is research on patient portals attuned to health equity? A scoping review","year":2019,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Alberta","funders":"","keywords":"eHealth; Patient portal; Health equity; Equity (law); Psychological intervention; Public relations; Context (archaeology); Medicine; Social determinants of health; Position paper; Knowledge management; Business; Political science; Health care; Nursing; World Wide Web; Computer science; Public health","score_opus":0.41966393229735754,"score_gpt":0.6673747156966338,"score_spread":0.2477107833992762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944497099","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007284293,0.9883251,0.00086432125,0.0067195487,0.0010225482,0.00033711875,0.00015997884,0.000013106225,0.0018298283],"genre_scores_gemma":[0.014857839,0.9773391,0.0019357154,0.0037494916,0.0005612859,0.0010986316,0.00020661556,0.000015628551,0.00023575137],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.921198,0.04217524,0.019591548,0.0039244136,0.0114977015,0.0016130924],"domain_scores_gemma":[0.64041406,0.3073769,0.021175243,0.0065555153,0.022627963,0.001850317],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.07348578,0.0014399806,0.0060861553,0.027114563,0.002515198,0.013742644,0.0030624967,0.007583992,0.007222759],"category_scores_gemma":[0.3114305,0.0018637505,0.006046997,0.027257482,0.0057834755,0.015455809,0.0069396044,0.005001869,0.00086708413],"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":[0.00013180921,0.00006547875,0.0015105618,0.7001424,0.0024092286,0.00017100395,0.0043089455,0.0003756202,0.00019159353,0.017039394,0.0116626015,0.2619914],"study_design_scores_gemma":[0.000022058635,0.000037875012,0.0006203743,0.9564499,0.0016365563,0.000104323255,0.0013834516,0.00006610706,0.0000658455,0.0028781055,0.036718696,0.000016685783],"about_ca_topic_score_codex":0.009561964,"about_ca_topic_score_gemma":0.011140573,"teacher_disagreement_score":0.9265142,"about_ca_system_score_codex":0.010769998,"about_ca_system_score_gemma":0.046842296,"threshold_uncertainty_score":0.38863456},"labels":[],"label_agreement":null},{"id":"W2949934519","doi":"10.1093/jamia/ocx062","title":"Tissue specificity of in vitro drug sensitivity","year":2017,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute; Princess Margaret Cancer Centre; Institute of Cancer Research; Ontario Institute for Cancer Research; University of Toronto","funders":"Ontario Institute for Cancer Research; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Drug; Concordance; Medicine; Clinical trial; Gold standard (test); Drug discovery; Drug response; Pharmacology; Oncology; Computational biology; Bioinformatics; Pathology; Internal medicine; Biology","score_opus":0.04304718929301386,"score_gpt":0.38890672896226797,"score_spread":0.3458595396692541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949934519","genre_codex":"empirical","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":[0.5267826,0.24077944,0.1809227,0.0028304239,0.0005793814,0.001042624,0.03169574,0.002207536,0.013159688],"genre_scores_gemma":[0.97555566,0.0060831024,0.011092896,0.0008482933,0.00016299273,0.00021862485,0.005320052,0.00021328178,0.00050497823],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9577302,0.02275581,0.00491656,0.008179962,0.005602636,0.00081486977],"domain_scores_gemma":[0.87304795,0.09011682,0.01902041,0.0121921655,0.00505314,0.00056944037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0370447,0.0016029761,0.003696883,0.0037739412,0.00047415524,0.0048511038,0.0014944096,0.0014124843,0.0021630814],"category_scores_gemma":[0.061617713,0.0008809,0.0055588433,0.0042997412,0.0016279845,0.0017174524,0.0018338924,0.0020254161,0.00058842875],"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":[0.0046691895,0.00019475384,0.70479643,0.018835114,0.046726026,0.0013618649,0.0005188072,0.03464175,0.08160287,0.0025683248,0.003920054,0.10016478],"study_design_scores_gemma":[0.00022560735,0.0028220192,0.67637175,0.0014778282,0.05138443,0.009129215,0.00048102465,0.032247823,0.17758855,0.008848896,0.03910729,0.0003155574],"about_ca_topic_score_codex":0.0021222532,"about_ca_topic_score_gemma":0.0025004344,"teacher_disagreement_score":0.0370447,"about_ca_system_score_codex":0.0016271096,"about_ca_system_score_gemma":0.0013738543,"threshold_uncertainty_score":0.19591343},"labels":[],"label_agreement":null},{"id":"W2953342969","doi":"10.1093/jamia/ocz071","title":"A systematic approach to equity assessment for digital health interventions: case example of mobile personal health records","year":2019,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"International Development Research Centre","funders":"International Development Research Centre","keywords":"Psychological intervention; Health equity; Equity (law); Health care; Social determinants of health; Scope (computer science); Public economics; Digital health; Business; Health impact assessment; Public relations; Medicine; Political science; Economic growth; Economics; Computer science; Nursing; Public health","score_opus":0.06224933604371513,"score_gpt":0.4784899696167769,"score_spread":0.4162406335730618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953342969","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":[0.18699756,0.01351109,0.5464264,0.0539448,0.00062426674,0.052677933,0.0009958909,0.00047428883,0.1443477],"genre_scores_gemma":[0.36465755,0.0036421379,0.6153933,0.001983304,0.00007059286,0.011320885,0.00009969364,0.00004396373,0.0027884936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.80228543,0.17391413,0.0054538622,0.0018446121,0.013908913,0.0025931252],"domain_scores_gemma":[0.882351,0.09849937,0.0040188213,0.004749637,0.009390726,0.0009904538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10643954,0.0013639209,0.0014364592,0.0073484797,0.0060227513,0.006491605,0.0027758796,0.0052462765,0.0031076712],"category_scores_gemma":[0.07666409,0.0007903975,0.0024382875,0.0052481336,0.0073297624,0.0071815173,0.009890982,0.0033116515,0.0002894654],"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":[0.00056340045,0.0028367173,0.019278266,0.013052584,0.0005331274,0.004421913,0.050154086,0.019737534,0.0034968236,0.36122173,0.008675538,0.51602834],"study_design_scores_gemma":[0.0024874231,0.007679108,0.022466142,0.044596404,0.0021237948,0.005245893,0.13946287,0.080056526,0.022663392,0.381059,0.29147193,0.00068748073],"about_ca_topic_score_codex":0.01198916,"about_ca_topic_score_gemma":0.036190912,"teacher_disagreement_score":0.10643954,"about_ca_system_score_codex":0.014473778,"about_ca_system_score_gemma":0.03732281,"threshold_uncertainty_score":0.5629128},"labels":[],"label_agreement":null},{"id":"W2965219508","doi":"10.1093/jamia/ocz081","title":"Evaluation of interventions to improve inpatient hospital documentation within electronic health records: a systematic review","year":2019,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Documentation; Psychological intervention; Medicine; Standardization; Data extraction; MEDLINE; Quality management; Systematic review; Health care; Nursing; Computer science; Operations management","score_opus":0.0679061118225972,"score_gpt":0.511240038347664,"score_spread":0.44333392652506676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965219508","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037604056,0.9880167,0.0007369706,0.00047397258,0.00025987384,0.0055519463,0.00057363644,0.000032247655,0.00059414055],"genre_scores_gemma":[0.057622783,0.92396134,0.0055891764,0.0010774201,0.00017954673,0.0107342955,0.0005232939,0.00001908206,0.00029311495],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.96573514,0.017524874,0.009162359,0.0015185183,0.005391939,0.00066725496],"domain_scores_gemma":[0.939272,0.040950805,0.012422486,0.0010399265,0.0055448893,0.0007698944],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.031034153,0.0021274388,0.012387219,0.009357145,0.0010745985,0.0035141623,0.002647976,0.0022384706,0.0051558255],"category_scores_gemma":[0.0917817,0.0013924729,0.010623123,0.008722109,0.0014221903,0.003393621,0.0021970628,0.0020103354,0.00031640613],"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":[0.00044224958,0.00007077873,0.0004714687,0.9527383,0.008108904,0.000046003614,0.00022867884,0.00011379968,0.00009406322,0.00020453763,0.0006690564,0.03681229],"study_design_scores_gemma":[0.00094159326,0.0006347877,0.0024037114,0.93930596,0.04902712,0.000111843365,0.00040662673,0.00012350004,0.00023639313,0.00021551136,0.0065565114,0.000036386067],"about_ca_topic_score_codex":0.0068939263,"about_ca_topic_score_gemma":0.01816302,"teacher_disagreement_score":0.9689658,"about_ca_system_score_codex":0.008700335,"about_ca_system_score_gemma":0.02871809,"threshold_uncertainty_score":0.16412628},"labels":[],"label_agreement":null},{"id":"W2965327127","doi":"10.1093/jamia/ocz112","title":"Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise","year":2019,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; BlueDot (Canada); St. Michael's Hospital","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Infectious disease (medical specialty); Representativeness heuristic; Classifier (UML); Information extraction; Natural language processing; Disease; Medicine; Pathology","score_opus":0.00696299589376862,"score_gpt":0.3142278333592521,"score_spread":0.30726483746548344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965327127","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":[0.4653707,0.0013166476,0.43243146,0.0028728102,0.00029355174,0.005882087,0.050757382,0.03368025,0.0073951194],"genre_scores_gemma":[0.4163519,0.00032528516,0.5168261,0.00040511298,0.00012897621,0.0016049383,0.06274127,0.0002832806,0.0013330745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951278,0.0013538543,0.0009115976,0.0013187833,0.0011165289,0.00017153939],"domain_scores_gemma":[0.98228884,0.0072976686,0.0017374278,0.0024146736,0.005507162,0.000754375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008561022,0.000902816,0.001023918,0.007976286,0.000605256,0.0023622888,0.0014150053,0.00079371815,0.0013200697],"category_scores_gemma":[0.020650573,0.0003798934,0.00080204575,0.0029562032,0.00041190404,0.0032973066,0.0015469651,0.00090589304,0.0011330448],"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":[0.0006837667,0.0019692536,0.17929254,0.0012511775,0.00047592173,0.0010719806,0.0013067867,0.021194516,0.037695322,0.0020705217,0.03441988,0.7185684],"study_design_scores_gemma":[0.0003471348,0.0017703595,0.15225403,0.00041519856,0.00045464528,0.0012602243,0.002173089,0.6739021,0.10105397,0.0053655133,0.060736194,0.0002675457],"about_ca_topic_score_codex":0.011836505,"about_ca_topic_score_gemma":0.0117241135,"teacher_disagreement_score":0.011836505,"about_ca_system_score_codex":0.0015115242,"about_ca_system_score_gemma":0.0030850484,"threshold_uncertainty_score":0.04527551},"labels":[],"label_agreement":null},{"id":"W2965865809","doi":"10.1093/jamia/ocz114","title":"The machine giveth and the machine taketh away: a parrot attack on clinical text deidentified with hiding in plain sight","year":2019,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privacy Analytics (Canada)","funders":"U.S. National Library of Medicine; National Human Genome Research Institute","keywords":"Computer science; Identifier; Sight; Artificial intelligence; Masking (illustration); Natural language processing; Random forest; Computer security; Information retrieval","score_opus":0.03774819822446671,"score_gpt":0.4296206950065799,"score_spread":0.39187249678211317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965865809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75809324,0.0010241364,0.20883356,0.009182459,0.00045149526,0.0009145216,0.0015484674,0.008019745,0.011932447],"genre_scores_gemma":[0.92369497,0.00018357851,0.06918599,0.0014046876,0.0001247475,0.00014299153,0.0008249267,0.00037287467,0.004065143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.983353,0.009695238,0.0008094554,0.002569455,0.0029733267,0.00059955113],"domain_scores_gemma":[0.9665014,0.020680405,0.003923182,0.0068199,0.0014987013,0.00057636166],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0087736035,0.0011562043,0.0008153353,0.0014242438,0.0016236192,0.0025331178,0.00160078,0.0031826259,0.0018893301],"category_scores_gemma":[0.038548313,0.00081781903,0.0009088204,0.00089888787,0.004567063,0.0058464063,0.0049662185,0.0028144245,0.0014541888],"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":[0.008835626,0.0010238012,0.09826311,0.0017064135,0.0008309347,0.025264207,0.05322006,0.16631725,0.114134476,0.07910954,0.06216812,0.38912648],"study_design_scores_gemma":[0.00020255739,0.0009801731,0.017622268,0.00036871157,0.00021349892,0.008027966,0.0037298347,0.84809417,0.058279797,0.02258322,0.03959015,0.00030762437],"about_ca_topic_score_codex":0.0046046544,"about_ca_topic_score_gemma":0.0040442767,"teacher_disagreement_score":0.9912264,"about_ca_system_score_codex":0.0021103644,"about_ca_system_score_gemma":0.0013268125,"threshold_uncertainty_score":0.04639983},"labels":[],"label_agreement":null},{"id":"W2979895712","doi":"10.1093/jamia/ocz178","title":"Training medical students and residents in the use of electronic health records: a systematic review of the literature","year":2019,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Psychological intervention; Medical education; Systematic review; Medical record; MEDLINE; Medicine; Health care; Psychology; Family medicine; Nursing","score_opus":0.08578250407717684,"score_gpt":0.45643477834805984,"score_spread":0.370652274270883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979895712","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":[0.0017435797,0.99676883,0.0001691455,0.00037487366,0.00010688158,0.00042325252,0.00018168951,0.0000065977665,0.0002251984],"genre_scores_gemma":[0.023797404,0.9728458,0.0011207063,0.00075915625,0.0000961783,0.0011183564,0.00017187383,0.0000051123066,0.00008543424],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9835036,0.0058698724,0.006336431,0.0010112526,0.0028511218,0.00042776662],"domain_scores_gemma":[0.92551655,0.057279702,0.010110489,0.00079811364,0.005564106,0.00073111424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020198615,0.0014940469,0.006753729,0.014132667,0.0008823667,0.002631307,0.002183882,0.0026549997,0.0033180506],"category_scores_gemma":[0.085573435,0.0013327884,0.0061690737,0.011603503,0.0010700261,0.0036079423,0.002099653,0.0013728549,0.0002749481],"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":[0.00013717527,0.000031449104,0.00091914425,0.95384717,0.003719376,0.000088671564,0.0004427713,0.00005496897,0.00010080523,0.00014407729,0.0009279332,0.039586585],"study_design_scores_gemma":[0.0001504389,0.00021212554,0.0033110434,0.9637277,0.02318051,0.00027185763,0.00072286045,0.00004991453,0.0001452527,0.00014917125,0.008057745,0.00002145103],"about_ca_topic_score_codex":0.007728547,"about_ca_topic_score_gemma":0.024381615,"teacher_disagreement_score":0.020198615,"about_ca_system_score_codex":0.005403711,"about_ca_system_score_gemma":0.02172815,"threshold_uncertainty_score":0.106821775},"labels":[],"label_agreement":null},{"id":"W3015283621","doi":"10.1093/jamia/ocaa019","title":"A system uptake analysis and GUIDES checklist evaluation of the Electronic Asthma Management System: A point-of-care computerized clinical decision support system","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Reseau canadien de recherche respiratoire","keywords":"Checklist; Clinical decision support system; Asthma; Point of care; Decision support system; Medicine; Point (geometry); Medical emergency; Computer science; Medical physics; Nursing; Data mining; Psychology; Internal medicine","score_opus":0.0329098147602049,"score_gpt":0.4158053923726342,"score_spread":0.3828955776124293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015283621","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":[0.90147346,0.0009186626,0.045326464,0.002498288,0.00013410767,0.029359577,0.0064845164,0.0021583778,0.011646505],"genre_scores_gemma":[0.7966484,0.00058226613,0.17480674,0.0004678432,0.000054642143,0.019753473,0.0046587354,0.00019379694,0.0028341631],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9158539,0.033333592,0.016027883,0.001981776,0.031111185,0.0016915437],"domain_scores_gemma":[0.8039241,0.08074103,0.030518081,0.0056408644,0.07591406,0.0032619212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06023874,0.0012282243,0.0014651276,0.0068424847,0.0017652123,0.002231234,0.002769452,0.0008075071,0.0017141968],"category_scores_gemma":[0.13584495,0.00085444003,0.0024912592,0.003890437,0.0010039868,0.0024095317,0.0032521142,0.0010670307,0.0005610938],"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":[0.0012758505,0.0018895334,0.6247256,0.0042292895,0.0005005204,0.00034729368,0.010656645,0.0069774534,0.0032855747,0.00083772803,0.022888549,0.32238597],"study_design_scores_gemma":[0.0004584531,0.0063015693,0.9258805,0.0019878964,0.00034328332,0.00044926346,0.0068818843,0.028867297,0.0060420767,0.00040669675,0.02209098,0.000290128],"about_ca_topic_score_codex":0.034572445,"about_ca_topic_score_gemma":0.058017004,"teacher_disagreement_score":0.06023874,"about_ca_system_score_codex":0.008571765,"about_ca_system_score_gemma":0.02021661,"threshold_uncertainty_score":0.3185767},"labels":[],"label_agreement":null},{"id":"W3021380283","doi":"10.1093/jamia/ocaa038","title":"Using word embeddings to improve the privacy of clinical notes","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Institute for Clinical Evaluative Sciences; Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Readability; Security token; Word embedding; Recall; Artificial intelligence; Word (group theory); Natural language processing; Precision and recall; Machine learning; Information retrieval; Embedding; Data science; Computer security","score_opus":0.05535004671577025,"score_gpt":0.40736421484053065,"score_spread":0.3520141681247604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021380283","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":[0.106928185,0.0012744373,0.8811201,0.0021547026,0.00047280508,0.00022961077,0.0012134687,0.002418744,0.004187917],"genre_scores_gemma":[0.66494584,0.0009583417,0.32383946,0.0007787675,0.00043554942,0.00022595521,0.0031180156,0.00040072855,0.005297371],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99160284,0.0036589925,0.001012199,0.0015916562,0.0017791975,0.00035510183],"domain_scores_gemma":[0.9765589,0.009087411,0.0033004812,0.008491111,0.002217431,0.000344558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005229417,0.0012232156,0.0008064532,0.0017396996,0.0009644548,0.0027878508,0.0011464445,0.0015107075,0.0026702567],"category_scores_gemma":[0.0418205,0.0005048956,0.0008530449,0.0018343987,0.0019256904,0.008049322,0.00431833,0.002489654,0.0021067855],"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":[0.0014837371,0.0004957345,0.017032923,0.00076484523,0.00026065216,0.00065398595,0.0021753137,0.079038784,0.024585413,0.062593825,0.0127526065,0.7981623],"study_design_scores_gemma":[0.00019761904,0.0010269956,0.0060926294,0.00038627262,0.00027852412,0.0025622789,0.0018065729,0.6648151,0.062075965,0.21496205,0.045588385,0.00020757713],"about_ca_topic_score_codex":0.0010083435,"about_ca_topic_score_gemma":0.0009653953,"teacher_disagreement_score":0.005229417,"about_ca_system_score_codex":0.0007993074,"about_ca_system_score_gemma":0.0016645424,"threshold_uncertainty_score":0.027656138},"labels":[],"label_agreement":null},{"id":"W3023802345","doi":"10.1093/jamia/ocaa031","title":"Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Registered Nurses' Association of Ontario","funders":"Government of Ontario","keywords":"Data quality; Database; Quality (philosophy); Metric (unit); Index (typography); Computer science; Quality management; Data mining; Operations management; Engineering; World Wide Web","score_opus":0.46353160777092506,"score_gpt":0.5916964321350855,"score_spread":0.12816482436416043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023802345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1250956,0.0025466026,0.7628106,0.022013446,0.0010462963,0.0116726635,0.01132833,0.0035428845,0.059943683],"genre_scores_gemma":[0.28476086,0.0010995779,0.7001035,0.0010988893,0.00021402066,0.005016876,0.0052549057,0.00027586013,0.002175497],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.8266107,0.060048796,0.044699837,0.0060465625,0.060736228,0.0018578891],"domain_scores_gemma":[0.58877397,0.12596214,0.049595222,0.041426346,0.18912394,0.005118361],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14523695,0.0007421983,0.0014812696,0.01916812,0.0033352806,0.010713631,0.0034330185,0.0012072416,0.0017729704],"category_scores_gemma":[0.25966394,0.0005037277,0.0011346175,0.014137399,0.0017932899,0.007853377,0.0065290723,0.0022170425,0.0006653101],"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":[0.00039993765,0.000639027,0.21337985,0.002695876,0.00041519766,0.00041684098,0.008507279,0.006612929,0.004370944,0.093979225,0.03338852,0.63519436],"study_design_scores_gemma":[0.0003385796,0.0030082848,0.23419823,0.00766831,0.0009398398,0.0021022293,0.024511121,0.12524882,0.040014815,0.08240508,0.47831127,0.0012534293],"about_ca_topic_score_codex":0.0133792665,"about_ca_topic_score_gemma":0.010445362,"teacher_disagreement_score":0.85476303,"about_ca_system_score_codex":0.009776201,"about_ca_system_score_gemma":0.016856084,"threshold_uncertainty_score":0.7680956},"labels":[],"label_agreement":null},{"id":"W3024876123","doi":"10.1093/jamia/ocaa022","title":"Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review","year":2020,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Colorectal cancer; Healthcare delivery; Colorectal cancer screening; Health care delivery; Medicine; Health care; Computer science; Cancer; Colonoscopy; Internal medicine","score_opus":0.05923416003397195,"score_gpt":0.36770692613885014,"score_spread":0.30847276610487817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024876123","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":[0.004343804,0.9896534,0.0024858273,0.0011931498,0.00019360897,0.0008078196,0.0003739704,0.000036625865,0.0009118447],"genre_scores_gemma":[0.1344532,0.855529,0.0072536957,0.0007673963,0.0001739624,0.0013189788,0.0003640166,0.000026799324,0.00011297074],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.94421,0.035502892,0.010845829,0.0017003714,0.007294305,0.00044655218],"domain_scores_gemma":[0.59829307,0.36890236,0.017932544,0.0031864895,0.011143478,0.00054204604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05577991,0.0016648805,0.0065514133,0.009781077,0.0007309334,0.0048815752,0.0028848753,0.0029231245,0.003906697],"category_scores_gemma":[0.29992288,0.0017306053,0.014609888,0.009201416,0.0014431436,0.0041021104,0.0019120941,0.002291266,0.00028317215],"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":[0.0007455799,0.0001047865,0.00571052,0.80977076,0.026082685,0.00012767916,0.00068856304,0.0050429534,0.000064877044,0.0024425162,0.0019226366,0.14729644],"study_design_scores_gemma":[0.0006378256,0.00048688788,0.0037067863,0.9054293,0.067502506,0.000312531,0.0004915342,0.005542735,0.00025628088,0.003026294,0.012484434,0.000122952],"about_ca_topic_score_codex":0.01428494,"about_ca_topic_score_gemma":0.01692597,"teacher_disagreement_score":0.05577991,"about_ca_system_score_codex":0.0076088244,"about_ca_system_score_gemma":0.020234924,"threshold_uncertainty_score":0.2949959},"labels":[],"label_agreement":null},{"id":"W3037637030","doi":"10.1093/jamia/ocaa085","title":"Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; Vector Institute; Canadian Institute for Advanced Research; University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Operationalization; Harm; Health care; Transparency (behavior); Patient safety; Risk analysis (engineering); Quality (philosophy); Quality management; Accountability; Economic Justice; Computer science; Psychology; Medicine; Social psychology; Computer security; Political science; Economics; Operations management","score_opus":0.403025800932591,"score_gpt":0.5013514929372974,"score_spread":0.09832569200470642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037637030","genre_codex":"commentary","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":[0.010376118,0.003488886,0.3662361,0.57066673,0.0019829164,0.0006022353,0.0001258476,0.00012216218,0.046398964],"genre_scores_gemma":[0.6323206,0.0023106667,0.24409844,0.108245775,0.004684782,0.0030513692,0.0000848332,0.00018401582,0.0050194943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.64288116,0.26133743,0.016501138,0.013939846,0.060129818,0.00521065],"domain_scores_gemma":[0.46329877,0.39621615,0.034586363,0.0547493,0.044582076,0.006567258],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.31234246,0.0011717197,0.0019769357,0.0034434274,0.009268863,0.019093232,0.0052960943,0.019620955,0.0029861876],"category_scores_gemma":[0.40333623,0.0011360234,0.0021770732,0.0027147906,0.08836628,0.018094763,0.013121803,0.028590031,0.0009036797],"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":[0.00001917812,0.0000417827,0.0007443228,0.000090210786,0.000021586076,0.000041658837,0.0025220443,0.00067221036,0.00010146812,0.9842164,0.00335312,0.008176082],"study_design_scores_gemma":[0.000041828633,0.00004465499,0.00035230163,0.00039379616,0.000017695436,0.000069848385,0.0005549618,0.0015098314,0.00026442713,0.981781,0.01492649,0.000043029388],"about_ca_topic_score_codex":0.0037036212,"about_ca_topic_score_gemma":0.0032684167,"teacher_disagreement_score":0.31234246,"about_ca_system_score_codex":0.009919915,"about_ca_system_score_gemma":0.0389327,"threshold_uncertainty_score":0.84800416},"labels":[],"label_agreement":null},{"id":"W3038078723","doi":"10.1093/jamia/ocaa153","title":"Inherent privacy limitations of decentralized contact tracing apps","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto; McGill University; McGill University Health Centre; Université de Montréal","funders":"","keywords":"Contact tracing; Tracing; Internet privacy; Coronavirus disease 2019 (COVID-19); Computer science; Computer security; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus; Infectious disease (medical specialty); Medicine; Disease","score_opus":0.04687437711482731,"score_gpt":0.28821717281872017,"score_spread":0.24134279570389286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038078723","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":[0.10936678,0.005052842,0.80502003,0.021353615,0.00066553755,0.00088878,0.00064490095,0.0022423947,0.054765135],"genre_scores_gemma":[0.92404246,0.0016392035,0.06494286,0.0015959617,0.00052406255,0.00059034827,0.00024213151,0.0001363699,0.006286514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9807827,0.009268357,0.0010271355,0.0025243373,0.0052968618,0.001100569],"domain_scores_gemma":[0.9379867,0.030793944,0.0038265067,0.020459194,0.0059951493,0.00093857036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013815172,0.00058696675,0.0012067747,0.00092356466,0.0031213032,0.0048929574,0.0025054882,0.0028307175,0.0035314485],"category_scores_gemma":[0.040714923,0.00102743,0.0006995858,0.0012044746,0.0033753465,0.008510405,0.0063545858,0.0035022935,0.0016729628],"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":[0.0010721901,0.00023883172,0.010365235,0.0011190685,0.00020517636,0.001113846,0.005359928,0.0332913,0.017212497,0.5968238,0.028973386,0.30422473],"study_design_scores_gemma":[0.00029113085,0.0005333125,0.006921684,0.0005482912,0.00023436028,0.0038231586,0.0022687754,0.20965943,0.021873206,0.6040458,0.14957127,0.00022954939],"about_ca_topic_score_codex":0.0017031415,"about_ca_topic_score_gemma":0.00097449817,"teacher_disagreement_score":0.013815172,"about_ca_system_score_codex":0.001653055,"about_ca_system_score_gemma":0.0021311166,"threshold_uncertainty_score":0.07306254},"labels":[],"label_agreement":null},{"id":"W3087185831","doi":"10.1093/jamia/ocaa163","title":"Trialstreamer: A living, automatically updated database of clinical trial reports","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. National Library of Medicine; Medical Research Council; National Institutes of Health; McMaster University","keywords":"Computer science; Database; Information retrieval","score_opus":0.5557533324818889,"score_gpt":0.5552892948924444,"score_spread":0.00046403758944446594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087185831","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004135504,0.010840267,0.07395854,0.0034029102,0.000933485,0.005759644,0.8032819,0.08838131,0.009306424],"genre_scores_gemma":[0.017811326,0.0072139665,0.29389724,0.0021933194,0.0008414309,0.011562904,0.65350765,0.009557098,0.003415018],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.95180416,0.011985308,0.02349139,0.005345956,0.006726005,0.000647223],"domain_scores_gemma":[0.6795413,0.19110079,0.05148738,0.041850988,0.028368443,0.007651013],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.045788877,0.0033274575,0.0055322805,0.042337023,0.0014004236,0.012761513,0.006548661,0.0035837046,0.045082692],"category_scores_gemma":[0.25441444,0.0030161794,0.0036614344,0.028334128,0.0013094292,0.012303319,0.0096735,0.0036343941,0.031950545],"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":[0.0034753892,0.00033883916,0.0075716004,0.050308697,0.0022642121,0.0009125736,0.0014581132,0.0021172008,0.0067292615,0.013038891,0.6039138,0.30787125],"study_design_scores_gemma":[0.003560114,0.00062486564,0.011050976,0.0114805065,0.002581245,0.0013834577,0.0005208475,0.0060331994,0.010594059,0.0186635,0.9328866,0.00062055956],"about_ca_topic_score_codex":0.002354861,"about_ca_topic_score_gemma":0.0040218057,"teacher_disagreement_score":0.99345136,"about_ca_system_score_codex":0.0024086505,"about_ca_system_score_gemma":0.013211534,"threshold_uncertainty_score":0.24215758},"labels":[],"label_agreement":null},{"id":"W3087508482","doi":"10.1093/jamia/ocaa185","title":"Virtual care: a ‘Zoombie’ apocalypse?","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Telehealth; Distraction; Telepsychiatry; Perspective (graphical); Medicine; Telemedicine; Compassion; Health care; Medical emergency; Internet privacy; Nursing; Psychology; Computer science","score_opus":0.014502593634218762,"score_gpt":0.31944048495557864,"score_spread":0.3049378913213599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087508482","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036186995,0.012187805,0.00988508,0.91966504,0.014200212,0.00006509365,0.000051144216,0.00018944046,0.040137507],"genre_scores_gemma":[0.22659807,0.038007963,0.027488478,0.652473,0.018054659,0.0006001871,0.000112672715,0.00071995874,0.035945095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98188055,0.012027623,0.0006469949,0.00087504194,0.0035309552,0.0010388219],"domain_scores_gemma":[0.97075385,0.018048944,0.0014275972,0.00233875,0.002753676,0.0046772994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018345019,0.0008487703,0.0008466084,0.0014866719,0.008699813,0.018584454,0.0033720022,0.008656327,0.022044165],"category_scores_gemma":[0.045441456,0.0005176313,0.00079556124,0.0012044229,0.028568435,0.036493286,0.01737927,0.020858543,0.0040495405],"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":[0.0001020783,0.00013116817,0.0016105134,0.001006886,0.00004935864,0.0007946656,0.018489948,0.00014261599,0.00041679508,0.41033944,0.3843169,0.18259965],"study_design_scores_gemma":[0.000037760197,0.00013806087,0.0009957082,0.002889866,0.00002111729,0.0015017806,0.0384906,0.00033117473,0.00022409791,0.10659755,0.8487009,0.00007133906],"about_ca_topic_score_codex":0.0047202418,"about_ca_topic_score_gemma":0.010689128,"teacher_disagreement_score":0.022044165,"about_ca_system_score_codex":0.004799552,"about_ca_system_score_gemma":0.0065545985,"threshold_uncertainty_score":0.09701884},"labels":[],"label_agreement":null},{"id":"W3087624099","doi":"10.1093/jamia/ocaa158","title":"A rapid review of gender, sex, and sexual orientation documentation in electronic health records","year":2020,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"LGBTQ Health, Identity, and Policy","field":"Psychology","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Canadian Institutes of Health Research","keywords":"Documentation; Sexual orientation; Terminology; Health records; MEDLINE; Confidentiality; Medicine; Medical education; Health care; Psychology; Family medicine; Political science; Computer science; Social psychology","score_opus":0.04261930371247709,"score_gpt":0.4363854587637697,"score_spread":0.39376615505129264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087624099","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":[0.002374165,0.98869145,0.00081488973,0.0026182488,0.0010307927,0.000785089,0.0012748241,0.000037444264,0.0023731578],"genre_scores_gemma":[0.010060235,0.98068386,0.003953064,0.002364035,0.0003916858,0.0010948755,0.0009121305,0.000019039997,0.00052114815],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.97607887,0.006197214,0.011512716,0.0009184678,0.0048037246,0.0004890746],"domain_scores_gemma":[0.900145,0.06171446,0.016142866,0.0016465507,0.019507382,0.0008437378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018424697,0.0011192076,0.003335707,0.036789957,0.0011855271,0.0031587742,0.0018534668,0.0021715858,0.003909255],"category_scores_gemma":[0.064093,0.0008984301,0.0030830533,0.026964078,0.0012101203,0.005889243,0.0025253764,0.001406791,0.0007483306],"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":[0.00009689291,0.000028690098,0.0012863487,0.7711648,0.0006981052,0.0004947421,0.0019151109,0.00008108826,0.0008622352,0.001010021,0.013744455,0.20861748],"study_design_scores_gemma":[0.000028636581,0.00010358775,0.004153276,0.8755519,0.002475736,0.0007202549,0.0011512082,0.00004300385,0.00025876754,0.00033176018,0.115151435,0.00003045779],"about_ca_topic_score_codex":0.005644598,"about_ca_topic_score_gemma":0.015534477,"teacher_disagreement_score":0.036789957,"about_ca_system_score_codex":0.004678144,"about_ca_system_score_gemma":0.023443917,"threshold_uncertainty_score":0.0974403},"labels":[],"label_agreement":null},{"id":"W3092441581","doi":"10.1093/jamia/ocaa193","title":"User-centered design of a longitudinal care plan for children with medical complexity","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; University of Toronto","funders":"Agency for Healthcare Research and Quality","keywords":"Categorization; Content analysis; Health care; Schema (genetic algorithms); Emergency department; Computer science; Nursing; Psychology; Medicine; Information retrieval","score_opus":0.08525638266710979,"score_gpt":0.29831650046582775,"score_spread":0.21306011779871797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092441581","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":[0.4929077,0.00035766533,0.4711214,0.0024883559,0.00016866815,0.010563447,0.0006000894,0.0039980006,0.017794663],"genre_scores_gemma":[0.39556167,0.00015814454,0.59496486,0.00044111258,0.00002840713,0.0053977612,0.00041686208,0.00017975137,0.002851494],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9884763,0.009069097,0.00052352855,0.0006835951,0.00092296686,0.00032448926],"domain_scores_gemma":[0.9811716,0.01217068,0.0012076334,0.001935233,0.002460543,0.0010541929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016633071,0.0007010554,0.0002682303,0.0007954438,0.0012544307,0.002257726,0.0015781733,0.00080910913,0.0069465414],"category_scores_gemma":[0.03820318,0.0005555419,0.00057655846,0.00035662556,0.00088698353,0.0020944602,0.0020365268,0.00079482474,0.0009291158],"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":[0.0022782704,0.0045654513,0.07398365,0.0034135426,0.00020019079,0.0016487917,0.14760725,0.008568401,0.04325804,0.012939278,0.019508919,0.68202823],"study_design_scores_gemma":[0.0032227738,0.02034076,0.11441612,0.0045967596,0.0013463314,0.0048760194,0.16845442,0.1436371,0.073536605,0.032777615,0.43197438,0.0008211958],"about_ca_topic_score_codex":0.00095570186,"about_ca_topic_score_gemma":0.0020243542,"teacher_disagreement_score":0.016633071,"about_ca_system_score_codex":0.0016523816,"about_ca_system_score_gemma":0.0029810795,"threshold_uncertainty_score":0.08796519},"labels":[],"label_agreement":null},{"id":"W3095942822","doi":"10.1093/jamia/ocaa207","title":"An implementation model for managing cloud-based longitudinal care plans for children with medical complexity","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; University of Toronto","funders":"Agency for Healthcare Research and Quality; Seattle Children's Research Institute","keywords":"Cloud computing; Permission; Documentation; Health Insurance Portability and Accountability Act; Software portability; Health care; Computer science; Leverage (statistics); Interoperability; Internet privacy; Confidentiality; World Wide Web; Computer security; Artificial intelligence","score_opus":0.06445399294607312,"score_gpt":0.33706331225535646,"score_spread":0.27260931930928334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095942822","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":[0.17185771,0.0007168045,0.7505331,0.02346517,0.0003702841,0.01396626,0.00038821838,0.0025838932,0.0361185],"genre_scores_gemma":[0.22421482,0.0002226163,0.7686456,0.0009283919,0.000027462338,0.003392715,0.00033024713,0.0001562494,0.0020820263],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9587536,0.027351795,0.0029955877,0.0028108566,0.005882258,0.002205829],"domain_scores_gemma":[0.9524234,0.020700416,0.0044440217,0.006540324,0.011522953,0.004369009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047373034,0.0011789231,0.00035075698,0.0023439461,0.0037298615,0.007992519,0.0041499627,0.0028175763,0.004129685],"category_scores_gemma":[0.062076602,0.0011337872,0.001654569,0.0011158267,0.004579467,0.008711516,0.0059843673,0.0039025594,0.0011144823],"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":[0.0007730022,0.0049119196,0.08621247,0.0042373734,0.00036034337,0.0023499983,0.14052613,0.03480985,0.020124638,0.25424987,0.018484503,0.43295988],"study_design_scores_gemma":[0.0011853355,0.00593908,0.031535238,0.011143585,0.0010475355,0.0033458655,0.1185741,0.2672278,0.020652508,0.1365822,0.40190968,0.0008570319],"about_ca_topic_score_codex":0.009897559,"about_ca_topic_score_gemma":0.016639233,"teacher_disagreement_score":0.047373034,"about_ca_system_score_codex":0.010491586,"about_ca_system_score_gemma":0.030063426,"threshold_uncertainty_score":0.2505356},"labels":[],"label_agreement":null},{"id":"W3100225964","doi":"10.1093/jamia/ocaa263","title":"Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Family and Patient Care in Intensive Care Units","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Alberta Health; University of Alberta; University of Calgary; Alberta Health Services","funders":"","keywords":"Critically ill; Measure (data warehouse); Health professionals; Computer science; Mode (computer interface); Health care; Medicine; Psychology; Nursing; Intensive care medicine; Data mining; Human–computer interaction; Political science","score_opus":0.03602268872312836,"score_gpt":0.3851365086844122,"score_spread":0.3491138199612838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100225964","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":[0.8366781,0.002338014,0.12809956,0.0013583776,0.00020345753,0.0019479261,0.0230039,0.001464228,0.004906388],"genre_scores_gemma":[0.8816442,0.00066303584,0.10326383,0.0005039144,0.00019087529,0.0012755997,0.0115864845,0.00005410315,0.0008180243],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99138427,0.0038832454,0.0012872217,0.0014773376,0.0017673061,0.00020059959],"domain_scores_gemma":[0.94594455,0.032656323,0.011467906,0.002322541,0.0070901224,0.00051855796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085341055,0.0008426431,0.000521231,0.006381965,0.00039660995,0.0016105484,0.0009293752,0.00076099066,0.0011264698],"category_scores_gemma":[0.041506417,0.00023094493,0.0007844076,0.0027076271,0.0005108614,0.0009884245,0.0008411106,0.0006160517,0.0005202742],"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":[0.0003340959,0.00031405318,0.87802863,0.000622143,0.00054965715,0.00025350493,0.00085322966,0.003930475,0.004003481,0.00022767528,0.0034188882,0.10746418],"study_design_scores_gemma":[0.00006743481,0.00048245618,0.948301,0.000265471,0.00023105556,0.0009902256,0.0009692641,0.038633816,0.0046443106,0.0015080775,0.0038204992,0.00008636634],"about_ca_topic_score_codex":0.0073522343,"about_ca_topic_score_gemma":0.013379505,"teacher_disagreement_score":0.0085341055,"about_ca_system_score_codex":0.0010230746,"about_ca_system_score_gemma":0.0017248244,"threshold_uncertainty_score":0.045133173},"labels":[],"label_agreement":null},{"id":"W3104912879","doi":"10.1093/jamia/ocaa249","title":"Optimizing the synthesis of clinical trial data using sequential trees","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Computer science; Metric (unit); Variable (mathematics); Data mining; Univariate; Clinical trial; Particle swarm optimization; Replicate; Machine learning; Similarity (geometry); Synthetic data; Artificial intelligence; Multivariate statistics; Statistics; Mathematics; Medicine","score_opus":0.1580074542505678,"score_gpt":0.3968032561351933,"score_spread":0.23879580188462549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104912879","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":[0.06152705,0.00033612258,0.9356934,0.00040245676,0.000032641852,0.000353052,0.0002565887,0.00036626917,0.0010325611],"genre_scores_gemma":[0.59631693,0.0002541468,0.4009171,0.00018053997,0.00003566364,0.0006684258,0.00074323255,0.000086110056,0.00079782127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970432,0.0017478977,0.00021850898,0.00036416514,0.0004967593,0.00012957542],"domain_scores_gemma":[0.97793454,0.018559491,0.0013166218,0.000821223,0.0010097662,0.00035831612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007635862,0.000822297,0.0011053539,0.0010607778,0.00031358266,0.0009392147,0.0007257492,0.00071112125,0.0015710328],"category_scores_gemma":[0.030165255,0.0005081049,0.0010269035,0.0009105348,0.0007034628,0.0012475734,0.0009689055,0.0011326041,0.00022369261],"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":[0.00014614068,0.000053805237,0.0011928316,0.00011047807,0.00004003327,0.00003443995,0.00005100867,0.9608467,0.0009306944,0.002913616,0.0003292511,0.03335092],"study_design_scores_gemma":[0.000035443547,0.00012293778,0.00021462252,0.000014403145,0.000012545885,0.00001185158,0.000012117472,0.9925925,0.00088986097,0.005694382,0.0003945681,0.0000046241757],"about_ca_topic_score_codex":0.0022643392,"about_ca_topic_score_gemma":0.0020518787,"teacher_disagreement_score":0.007635862,"about_ca_system_score_codex":0.0012941831,"about_ca_system_score_gemma":0.0031162256,"threshold_uncertainty_score":0.040382802},"labels":[],"label_agreement":null},{"id":"W3106195149","doi":"10.1093/jamia/ocaa225","title":"Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Sunnybrook Health Science Centre; Trillium Health Centre; Institute for Clinical Evaluative Sciences; Health Sciences Centre; Mount Sinai Hospital; University of Toronto; University Health Network; Queen's University; St. Michael's Hospital","funders":"University of Toronto","keywords":"Data quality; Data extraction; Computer science; Gold standard (test); Quality (philosophy); Quality management; Data collection; Data mining; Database; Medicine; MEDLINE; Statistics; Operations management; Mathematics","score_opus":0.540661436719735,"score_gpt":0.5948575964308802,"score_spread":0.05419615971114522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106195149","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":[0.91232526,0.0030218281,0.039610535,0.013177321,0.00020797687,0.0014382643,0.020641692,0.0013866792,0.008190529],"genre_scores_gemma":[0.8826638,0.00087790616,0.08732111,0.0016336272,0.0001142742,0.00030891513,0.02614539,0.00022706593,0.00070788694],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9433017,0.026323274,0.007050904,0.004033037,0.017990233,0.0013009481],"domain_scores_gemma":[0.7605157,0.1266459,0.02672993,0.026010426,0.056194652,0.003903469],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.084415674,0.0006869121,0.0007062837,0.0035700542,0.00129702,0.004396054,0.0027437783,0.0007317749,0.00071570225],"category_scores_gemma":[0.22933103,0.0005387535,0.001086156,0.009297737,0.0020524666,0.0021747798,0.0037919166,0.0011815205,0.00018313839],"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":[0.00070479687,0.0001607664,0.86873674,0.0009049939,0.00066920783,0.00016145244,0.004858674,0.012527316,0.0012219644,0.003288073,0.017159829,0.089606136],"study_design_scores_gemma":[0.0003277104,0.00068263005,0.8505705,0.0010703654,0.00052494474,0.000631149,0.005413148,0.07881287,0.007263667,0.004621894,0.049835164,0.00024594655],"about_ca_topic_score_codex":0.2271769,"about_ca_topic_score_gemma":0.22144514,"teacher_disagreement_score":0.9155843,"about_ca_system_score_codex":0.015948962,"about_ca_system_score_gemma":0.020542175,"threshold_uncertainty_score":0.45170915},"labels":[],"label_agreement":null},{"id":"W3122082322","doi":"10.1093/jamia/ocaa232","title":"Optimizing a literature surveillance strategy to retrieve sound overall prognosis and risk assessment model papers","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Computer science; Guideline; Information retrieval; Sensitivity (control systems); Data mining; Machine learning; Medicine; Pathology","score_opus":0.2355297951930919,"score_gpt":0.4462240254982211,"score_spread":0.2106942303051292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122082322","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":[0.14511758,0.18703853,0.351858,0.027255818,0.002821163,0.16558184,0.082975514,0.009305689,0.0280459],"genre_scores_gemma":[0.228914,0.023441236,0.62620294,0.007206269,0.0007359428,0.089001976,0.021702953,0.0008406802,0.0019539134],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.84144825,0.06873413,0.0646205,0.00888498,0.014787238,0.0015248387],"domain_scores_gemma":[0.5926946,0.30586445,0.033715043,0.021163326,0.043781836,0.0027807592],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14450867,0.002968326,0.0065005664,0.06252301,0.0017017258,0.009451228,0.004118309,0.003300839,0.0093232915],"category_scores_gemma":[0.48033732,0.0019710653,0.008982648,0.030990949,0.0013447439,0.009113129,0.0070966873,0.0016777456,0.0032941427],"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":[0.0037059626,0.00041712046,0.039301157,0.26363266,0.011995426,0.0011097866,0.0048379092,0.005557618,0.007201389,0.008525978,0.03825327,0.6154617],"study_design_scores_gemma":[0.013295614,0.0047495854,0.0996806,0.3632652,0.101053536,0.004077236,0.00911636,0.051346462,0.015904082,0.067132786,0.2687216,0.0016568814],"about_ca_topic_score_codex":0.006416555,"about_ca_topic_score_gemma":0.014758688,"teacher_disagreement_score":0.85549134,"about_ca_system_score_codex":0.0062192613,"about_ca_system_score_gemma":0.025593594,"threshold_uncertainty_score":0.7642441},"labels":[],"label_agreement":null},{"id":"W3132436725","doi":"10.1093/jamia/ocaa294","title":"Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions","year":2020,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Institute of Health Services and Policy Research; St. Michael's Hospital; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Documentation; Health care; Clinical decision support system; Context (archaeology); Decision aids; Medicine; Quality (philosophy); Decision support system; Action (physics); Knowledge management; Computer science; Artificial intelligence; Alternative medicine","score_opus":0.07949624665286226,"score_gpt":0.4388486375070747,"score_spread":0.35935239085421244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132436725","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":[0.010964602,0.00034924177,0.9605828,0.0046300148,0.00018480608,0.0028810967,0.00031228064,0.011496175,0.008599016],"genre_scores_gemma":[0.114447996,0.0004530653,0.8759434,0.0017286725,0.00019277856,0.0026956042,0.0006121759,0.00072058215,0.0032056323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95649934,0.025401812,0.005443937,0.0067161946,0.0049477643,0.0009910648],"domain_scores_gemma":[0.884468,0.0583421,0.008857373,0.035744987,0.009872035,0.0027155478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.044137716,0.0018905533,0.0011816798,0.0024814515,0.0016499794,0.0074763233,0.005450094,0.0042882585,0.008675314],"category_scores_gemma":[0.11810775,0.00139543,0.0014734939,0.001691498,0.0048681083,0.01235381,0.007973611,0.004002914,0.006612426],"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":[0.0012345193,0.0013541657,0.0129407365,0.0013054563,0.00033397312,0.00088184676,0.005737238,0.042372353,0.011069627,0.119543366,0.0233186,0.7799081],"study_design_scores_gemma":[0.0011962231,0.001955554,0.003732665,0.001905421,0.00041433354,0.0016428418,0.0016782423,0.2781762,0.03753972,0.46545526,0.205592,0.0007115341],"about_ca_topic_score_codex":0.0019002,"about_ca_topic_score_gemma":0.0012779271,"teacher_disagreement_score":0.044137716,"about_ca_system_score_codex":0.0021452112,"about_ca_system_score_gemma":0.009357967,"threshold_uncertainty_score":0.23342532},"labels":[],"label_agreement":null},{"id":"W3135436544","doi":"10.1093/jamia/ocab001","title":"Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agency for Healthcare Research and Quality","keywords":"Computer science; Psychological intervention; Thematic analysis; Data collection; Process (computing); Machine learning; Cloud computing; Artificial intelligence; Intervention (counseling); Reinforcement learning; Data science; Algorithm; Qualitative research; Medicine; Nursing","score_opus":0.15030950070397475,"score_gpt":0.500054018383595,"score_spread":0.3497445176796202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135436544","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":[0.0007922521,0.00153471,0.9888982,0.0022203773,0.000098235956,0.0010305655,0.000107556596,0.00077697594,0.0045410553],"genre_scores_gemma":[0.011726985,0.0012529104,0.98196113,0.00036531905,0.00006353266,0.0027271947,0.00015225742,0.00019666592,0.0015539983],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98747534,0.0076135965,0.0011792866,0.00071079767,0.0027932632,0.00022764958],"domain_scores_gemma":[0.97792804,0.014287978,0.0009870968,0.0017400221,0.0047104913,0.00034645366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013943229,0.0025011578,0.0010338805,0.0021733823,0.00079409446,0.0034151298,0.0036992761,0.0032551885,0.0071657086],"category_scores_gemma":[0.05163524,0.0013027367,0.0016463923,0.0015377721,0.0018535664,0.0031636804,0.002254447,0.0043468126,0.0037908254],"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":[0.0002241148,0.000539982,0.0023094579,0.0035357727,0.0002467081,0.00041416113,0.0012527007,0.2339329,0.0047555435,0.19563626,0.03240118,0.52475125],"study_design_scores_gemma":[0.00035537852,0.00047500964,0.0007118851,0.0028114284,0.00014619617,0.00025904144,0.00051096384,0.6085223,0.0054301843,0.23612577,0.14452441,0.00012742485],"about_ca_topic_score_codex":0.0019381562,"about_ca_topic_score_gemma":0.0026677689,"teacher_disagreement_score":0.013943229,"about_ca_system_score_codex":0.0015821159,"about_ca_system_score_gemma":0.0037809089,"threshold_uncertainty_score":0.07373971},"labels":[],"label_agreement":null},{"id":"W3135875685","doi":"10.1093/jamia/ocab021","title":"<i>Social informatics</i> is a poor choice of term: A response to Pantell et al","year":2021,"lang":"en","type":"letter","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The Wilson Centre; Public Health Ontario; University of Toronto; University Health Network","funders":"","keywords":"Tel aviv; Library science; Health informatics; Informatics; Public health; Sociology; Gerontology; Political science; Medicine; Nursing; Computer science; Law","score_opus":0.037396611000725576,"score_gpt":0.43480655579855654,"score_spread":0.397409944797831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135875685","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026668038,0.00020057979,0.000022296592,0.995226,0.0035436396,0.000004552929,0.000012349581,0.000006207385,0.000717702],"genre_scores_gemma":[0.005336402,0.00092319323,0.00016652475,0.98008084,0.010506596,0.000038728187,0.00002119183,0.000020229041,0.0029062976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99192774,0.0029190301,0.0012208244,0.00077860145,0.0021814907,0.00097233435],"domain_scores_gemma":[0.9425732,0.032604884,0.003619596,0.0013013115,0.011934106,0.007966974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011367748,0.0006244587,0.001068467,0.0013141042,0.008549607,0.0046836203,0.0019472657,0.03566782,0.007707673],"category_scores_gemma":[0.08539724,0.00072481786,0.0010034107,0.0018895998,0.0061549987,0.007834043,0.0027160433,0.03970541,0.0037486479],"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":[0.000021238098,0.0000235808,0.00081422145,0.00004256031,0.000004991762,0.0009904242,0.0007746057,0.000018608995,0.00004827832,0.0015168793,0.990387,0.005357584],"study_design_scores_gemma":[0.000108460066,0.00012182683,0.003486638,0.0016460345,0.000031410622,0.008805208,0.011159662,0.0005312608,0.00021379073,0.012032164,0.96170163,0.00016192575],"about_ca_topic_score_codex":0.017742157,"about_ca_topic_score_gemma":0.02283022,"teacher_disagreement_score":0.03566782,"about_ca_system_score_codex":0.008242714,"about_ca_system_score_gemma":0.0120667545,"threshold_uncertainty_score":0.060119152},"labels":[],"label_agreement":null},{"id":"W3157526925","doi":"10.1093/jamia/ocab071","title":"Pharmacists’ perceptions of a machine learning model for the identification of atypical medication orders","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Recall; Confusion; Pharmacist; Medicine; Machine learning; Artificial intelligence; Identification (biology); Computer science; Family medicine; Pharmacy; Psychology","score_opus":0.01665857859228839,"score_gpt":0.3344122756747825,"score_spread":0.3177536970824941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157526925","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":[0.99616647,0.0000910792,0.002160222,0.00046578055,0.000008217855,0.00004792854,0.00009852387,0.000034079087,0.0009277134],"genre_scores_gemma":[0.9979018,0.0000510587,0.0017370865,0.00006989021,0.000007684034,0.000015752114,0.000086167194,0.0000034949271,0.00012688838],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99610597,0.0020035626,0.00027642728,0.00040727694,0.0010676632,0.000139075],"domain_scores_gemma":[0.9620663,0.029077632,0.004292159,0.0009863316,0.002728556,0.0008490928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009238798,0.00037923525,0.0003400944,0.0005465137,0.0003948695,0.0014739935,0.00041989202,0.00081591937,0.0012459322],"category_scores_gemma":[0.059847016,0.00019529575,0.00043554627,0.00025556647,0.00039082437,0.0010365765,0.00053980603,0.00074689504,0.00032076734],"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":[0.0017425584,0.0010737613,0.91006064,0.00017502038,0.00020761104,0.00025968387,0.001789673,0.026046468,0.0024338148,0.00024408799,0.0012547324,0.05471183],"study_design_scores_gemma":[0.00030952756,0.0067860447,0.58474183,0.00023480339,0.00021897524,0.0011503788,0.0028260602,0.39550427,0.0043448475,0.0011848527,0.0025513067,0.00014717405],"about_ca_topic_score_codex":0.0038241239,"about_ca_topic_score_gemma":0.0026808835,"teacher_disagreement_score":0.009238798,"about_ca_system_score_codex":0.00083847786,"about_ca_system_score_gemma":0.000987076,"threshold_uncertainty_score":0.048860013},"labels":[],"label_agreement":null},{"id":"W3190523902","doi":"10.1093/jamia/ocab124","title":"Application of natural language processing techniques to identify off-label drug usage from various online health communities","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Pharmaceutical studies and practices","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Bristol-Myers Squibb Canada","keywords":"Drug; Spelling; Recall; Computer science; Multi-label classification; Off-label use; Parsing; Variety (cybernetics); Medicine; Natural language processing; Artificial intelligence; Psychology; Pharmacology","score_opus":0.026678273833087764,"score_gpt":0.42452031761323494,"score_spread":0.3978420437801472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190523902","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":[0.5723936,0.0009480693,0.38063806,0.0021259726,0.00025314878,0.0032290432,0.025933575,0.007918966,0.00655953],"genre_scores_gemma":[0.5290544,0.00026417116,0.44991744,0.00036879157,0.0001267963,0.0014298798,0.017270565,0.00014648002,0.001421499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677473,0.0013069153,0.0004430529,0.0006601192,0.0006679298,0.00014722875],"domain_scores_gemma":[0.9655181,0.02611717,0.0037635278,0.0010867128,0.0031403333,0.00037411213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003973038,0.0009890482,0.00044339788,0.0066509345,0.0007301408,0.0013407608,0.0006745045,0.00081911834,0.0017045446],"category_scores_gemma":[0.014675338,0.0002917158,0.00072436966,0.002527339,0.0005241962,0.0019395448,0.0012186279,0.0010996672,0.0008972893],"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":[0.0019026008,0.0018124462,0.095641114,0.0037751952,0.00030908952,0.006008938,0.01053531,0.014306209,0.106352106,0.005329563,0.017940668,0.7360867],"study_design_scores_gemma":[0.00034936777,0.0014713992,0.17739768,0.0008084511,0.00049302034,0.005273478,0.010125284,0.6073098,0.10873731,0.02742827,0.060139854,0.0004661035],"about_ca_topic_score_codex":0.0034564573,"about_ca_topic_score_gemma":0.0051654717,"teacher_disagreement_score":0.0066509345,"about_ca_system_score_codex":0.0010992326,"about_ca_system_score_gemma":0.0016842803,"threshold_uncertainty_score":0.02101165},"labels":[],"label_agreement":null},{"id":"W3191709143","doi":"10.1093/jamia/ocab128","title":"Improving domain adaptation in de-identification of electronic health records through self-training","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada); University of Toronto","funders":"","keywords":"Computer science; Identification (biology); Software deployment; Domain (mathematical analysis); Domain adaptation; Artificial intelligence; Task (project management); Machine learning; Adaptation (eye); Test data; Data mining; Classifier (UML)","score_opus":0.011711423788104566,"score_gpt":0.3038563359614966,"score_spread":0.292144912173392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191709143","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":[0.19356652,0.0038398567,0.78341675,0.0015737939,0.0005915961,0.0003904883,0.0011878792,0.010049382,0.005383738],"genre_scores_gemma":[0.7406869,0.0011382232,0.24449275,0.001357479,0.00028111716,0.00025769923,0.0062684785,0.00023951197,0.005277823],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975696,0.0010114497,0.00015953464,0.0007803667,0.0003164717,0.00016245204],"domain_scores_gemma":[0.9949892,0.0025393013,0.00043909054,0.0011188899,0.0007775154,0.0001359501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039873268,0.0010685928,0.0010123648,0.0011900323,0.0005335639,0.000899347,0.0012355102,0.0015336558,0.0009847906],"category_scores_gemma":[0.009657885,0.0003047144,0.0009662621,0.00091219775,0.00059847,0.002157612,0.0015540614,0.002088989,0.0013004215],"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":[0.0006716429,0.0015506215,0.022590641,0.00044049727,0.00024994585,0.000466103,0.0004294638,0.18653376,0.023678916,0.003004008,0.025009137,0.7353752],"study_design_scores_gemma":[0.000048123493,0.00016313071,0.004645483,0.00007393908,0.000053499392,0.00039727148,0.00016317118,0.96627444,0.017927697,0.0035742624,0.00664198,0.00003703456],"about_ca_topic_score_codex":0.0029976263,"about_ca_topic_score_gemma":0.0031906674,"teacher_disagreement_score":0.0039873268,"about_ca_system_score_codex":0.00066996925,"about_ca_system_score_gemma":0.0011189374,"threshold_uncertainty_score":0.02108723},"labels":[],"label_agreement":null},{"id":"W3192442226","doi":"10.1093/jamia/ocab135","title":"Differential privacy in health research: A scoping review","year":2021,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Differential privacy; Internet privacy; Computer science; Data science; Computer security; Data mining","score_opus":0.17405305240096253,"score_gpt":0.4687935635856185,"score_spread":0.294740511184656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192442226","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":[0.00016522412,0.9959545,0.00065327866,0.0018467658,0.00031635942,0.00012407886,0.000069409434,0.000007845952,0.0008625026],"genre_scores_gemma":[0.0034338378,0.99333453,0.0014050513,0.001037729,0.00022442869,0.0003511193,0.000092933056,0.000006759196,0.00011356346],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9637698,0.016105048,0.0108296005,0.0019804295,0.0065697185,0.0007453918],"domain_scores_gemma":[0.72501135,0.24283847,0.011917792,0.0034069617,0.015937824,0.0008875567],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.040624157,0.0018286714,0.004513429,0.026194453,0.0021689816,0.007904314,0.0029711837,0.006257054,0.005106033],"category_scores_gemma":[0.17517856,0.0016330354,0.00609348,0.026036214,0.0036870542,0.007982359,0.005097117,0.0044719665,0.00088283385],"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":[0.00011489549,0.000046843747,0.0006830005,0.6151134,0.0012151102,0.0002779591,0.0019479933,0.0006302449,0.00021812422,0.012911293,0.012539787,0.3543014],"study_design_scores_gemma":[0.000019597686,0.000045354074,0.0005890542,0.9225395,0.001732524,0.00028816477,0.0006735781,0.00013016851,0.00010963786,0.0042192102,0.06962603,0.000027153972],"about_ca_topic_score_codex":0.007912811,"about_ca_topic_score_gemma":0.010588453,"teacher_disagreement_score":0.95937586,"about_ca_system_score_codex":0.00853582,"about_ca_system_score_gemma":0.030216305,"threshold_uncertainty_score":0.21484363},"labels":[],"label_agreement":null},{"id":"W3195986168","doi":"10.1093/jamia/ocab118","title":"Developing machine learning models to personalize care levels among emergency room patients for hospital admission","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada; Gordon and Betty Moore Foundation; U.S. National Library of Medicine; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Medical emergency; Medicine; Emergency department; Emergency medicine; Computer science; Nursing","score_opus":0.05438370055536089,"score_gpt":0.33478872016488725,"score_spread":0.28040501960952635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195986168","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":[0.8668266,0.0006939014,0.1249981,0.0016148437,0.000100344405,0.00021598993,0.0028492624,0.0011936501,0.0015073129],"genre_scores_gemma":[0.9590217,0.0001413991,0.03756297,0.00017338066,0.00004328969,0.000112782654,0.0022735428,0.00002539153,0.00064545445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992422,0.0003080913,0.00005090679,0.00024169311,0.000081217346,0.00007590966],"domain_scores_gemma":[0.9952608,0.0032672451,0.0005353084,0.00014110339,0.00067359494,0.0001220829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030548295,0.0010095446,0.00059759105,0.0013005765,0.0002378073,0.0009343459,0.0007494061,0.0008673733,0.0010240683],"category_scores_gemma":[0.011111911,0.0003657258,0.0008848828,0.0006556207,0.00015151652,0.0008095013,0.0004754381,0.001204848,0.00044108558],"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":[0.00047228156,0.0010322771,0.42300022,0.00013899585,0.00087614486,0.00016683464,0.00020071505,0.44943514,0.0011083265,0.00048733424,0.0045010378,0.11858071],"study_design_scores_gemma":[0.000022960196,0.00012498829,0.018509746,0.000033865592,0.00007520673,0.000045601093,0.00004956362,0.9795506,0.00048464217,0.00079581427,0.0002937333,0.000013357971],"about_ca_topic_score_codex":0.0124086775,"about_ca_topic_score_gemma":0.010980448,"teacher_disagreement_score":0.0124086775,"about_ca_system_score_codex":0.0009047873,"about_ca_system_score_gemma":0.0011789987,"threshold_uncertainty_score":0.024672866},"labels":[],"label_agreement":null},{"id":"W3197451302","doi":"10.1093/jamia/ocab136","title":"Transgender data collection in the electronic health record: Current concepts and issues","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"LGBTQ Health, Identity, and Policy","field":"Psychology","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Victoria; University of Toronto","funders":"National Institute on Drug Abuse; Agency for Healthcare Research and Quality","keywords":"Transgender; Disclaimer; Terminology; Transgender Person; Health care; Medicine; Leverage (statistics); Data collection; Internet privacy; Psychology; Family medicine; Medical education; Computer science; Sociology; Political science","score_opus":0.046681969176600474,"score_gpt":0.4530278904382472,"score_spread":0.4063459212616467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197451302","genre_codex":"commentary","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":[0.023377413,0.07045032,0.104336835,0.77076715,0.005497366,0.00097421167,0.0010926527,0.0005083648,0.022995612],"genre_scores_gemma":[0.31348002,0.11887094,0.35212255,0.189672,0.0136054745,0.003582688,0.0017096294,0.0009339527,0.0060227388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.64663726,0.26184407,0.03349285,0.013908724,0.040711492,0.003405685],"domain_scores_gemma":[0.31592345,0.51980907,0.03187351,0.049810447,0.07539779,0.0071857567],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.34351835,0.00078820426,0.0015790963,0.009641876,0.008703914,0.025656698,0.009258175,0.005939191,0.0046578636],"category_scores_gemma":[0.42389163,0.0016437138,0.0016798578,0.012234277,0.039197415,0.04658966,0.017078897,0.010045657,0.0025004083],"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":[0.00026318704,0.00026252025,0.039329454,0.0077232094,0.00015858146,0.00041921,0.07637561,0.00041720047,0.0011944118,0.17038614,0.055058405,0.64841217],"study_design_scores_gemma":[0.00007660776,0.00036511844,0.026125662,0.04466599,0.00024668797,0.0031122088,0.17596778,0.002092515,0.0030312452,0.23589844,0.50786066,0.00055706763],"about_ca_topic_score_codex":0.014650611,"about_ca_topic_score_gemma":0.013217802,"teacher_disagreement_score":0.34351835,"about_ca_system_score_codex":0.010919489,"about_ca_system_score_gemma":0.027443938,"threshold_uncertainty_score":0.80955875},"labels":[],"label_agreement":null},{"id":"W3198605523","doi":"10.1093/jamia/ocab140","title":"Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Palliative Care and End-of-Life Issues","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Palliative care; End-of-life care; Medicine; Computer science; Intensive care medicine; Nursing","score_opus":0.08730913357338663,"score_gpt":0.4557360731797833,"score_spread":0.3684269396063967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198605523","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":[0.56502986,0.0015659366,0.4219831,0.0048678145,0.00017514668,0.0008387705,0.000799015,0.001330751,0.0034095761],"genre_scores_gemma":[0.92772067,0.00015976507,0.071067385,0.0003606045,0.00005531294,0.000199543,0.000260421,0.00003265294,0.00014360966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9621942,0.030173976,0.001601747,0.0022971192,0.0032360204,0.00049706147],"domain_scores_gemma":[0.6931716,0.281391,0.009646471,0.0060504857,0.008518515,0.0012219811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.058451846,0.0017757398,0.0014660668,0.0021789083,0.00040243496,0.00333291,0.0018875082,0.0017689251,0.0010863728],"category_scores_gemma":[0.24467549,0.0008154219,0.001064952,0.0011278399,0.0011454953,0.0031123222,0.0014641852,0.0025648812,0.0002517464],"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":[0.002814806,0.0010051122,0.2169221,0.00035213187,0.0013434144,0.00024091941,0.00027010066,0.6655908,0.0010090932,0.0043005995,0.002200133,0.10395084],"study_design_scores_gemma":[0.00009975457,0.0004912628,0.004590371,0.00007685512,0.00009281333,0.000052457344,0.000050951592,0.98853964,0.0005925368,0.005197053,0.00018915816,0.000027258433],"about_ca_topic_score_codex":0.0036309855,"about_ca_topic_score_gemma":0.0024602471,"teacher_disagreement_score":0.058451846,"about_ca_system_score_codex":0.0019762896,"about_ca_system_score_gemma":0.0030291665,"threshold_uncertainty_score":0.30912662},"labels":[],"label_agreement":null},{"id":"W3203510482","doi":"10.1093/jamia/ocab183","title":"Toward an inclusive digital health system for sexual and gender minorities in Canada","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"LGBTQ Health, Identity, and Policy","field":"Psychology","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"Institute of Gender and Health; Canadian Institutes of Health Research","keywords":"Sexual orientation; Sexual minority; Terminology; Health care; Action plan; Health equity; Transgender; Healthcare system; Gender equity; Medicine; Political science; Public relations; Psychology; Business; Nursing; Sociology; Gender studies; Social psychology","score_opus":0.02637326093115859,"score_gpt":0.3497581371984485,"score_spread":0.3233848762672899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203510482","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":[0.63749856,0.002432844,0.017511174,0.1346862,0.000486537,0.004916177,0.0041007074,0.00076498423,0.19760285],"genre_scores_gemma":[0.8973326,0.0020638548,0.058694523,0.012872678,0.00005564055,0.00095372484,0.0011827816,0.00008217307,0.026762083],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9941964,0.0013345438,0.00021906689,0.00036627403,0.0019814689,0.0019022301],"domain_scores_gemma":[0.9872896,0.0007105657,0.0004162586,0.00061455526,0.0043923617,0.006576681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00787367,0.00027981694,0.00025622375,0.00199344,0.024194913,0.0071452814,0.0025767488,0.00097270927,0.004678184],"category_scores_gemma":[0.010550638,0.00033418174,0.00039555613,0.0032005857,0.0041299458,0.002368169,0.00996809,0.0017613759,0.0004636463],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031709965,0.0012161423,0.18869694,0.00072175433,0.00008002784,0.0013225875,0.08836521,0.0026077428,0.0049954313,0.13579458,0.107738264,0.46814427],"study_design_scores_gemma":[0.00016326677,0.00031603247,0.22952496,0.001140676,0.00012369709,0.00036007265,0.18508212,0.0060071656,0.0028999457,0.016691566,0.55745035,0.00024014252],"about_ca_topic_score_codex":0.99381906,"about_ca_topic_score_gemma":0.99679095,"teacher_disagreement_score":0.12334511,"about_ca_system_score_codex":0.12334511,"about_ca_system_score_gemma":0.4880851,"threshold_uncertainty_score":0.8949356},"labels":[],"label_agreement":null},{"id":"W3203841805","doi":"10.1093/jamia/ocab196","title":"Gender harmony: improved standards to support affirmative care of gender-marginalized people through inclusive gender and sex representation","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Health Infoway; University of Victoria","funders":"U.S. National Library of Medicine","keywords":"Harmony (color); Gender diversity; Health care; Interoperability; Gender identity; Computer science; Psychology; Medicine; Political science; Social psychology; Law; Business","score_opus":0.03646101102386387,"score_gpt":0.38646306591065194,"score_spread":0.35000205488678804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203841805","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":[0.021372689,0.002144475,0.7272899,0.114722274,0.0036694726,0.0024407213,0.0009092311,0.002365988,0.1250853],"genre_scores_gemma":[0.25058073,0.0023118446,0.6842817,0.030997535,0.0013271054,0.0039991885,0.0020380237,0.0010089006,0.023454938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.89875704,0.062431384,0.009010604,0.006040647,0.020605756,0.0031546145],"domain_scores_gemma":[0.90657043,0.030862313,0.008046573,0.018985976,0.03005222,0.0054824264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13432507,0.00086488633,0.00072620687,0.004361001,0.0067342324,0.012799794,0.00564626,0.005163911,0.007746611],"category_scores_gemma":[0.1256826,0.00084117544,0.0017935929,0.002154362,0.017475687,0.023205657,0.02222934,0.0066865077,0.0034805636],"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":[0.00009352249,0.00017676002,0.0049449415,0.0005637816,0.00003423752,0.00019630125,0.023709329,0.0011604676,0.0017159925,0.7676719,0.05200276,0.14772998],"study_design_scores_gemma":[0.00008607911,0.0003111861,0.0036556246,0.0025242637,0.0000679212,0.00048629017,0.017707404,0.00401147,0.0049791243,0.3792019,0.5867792,0.00018954469],"about_ca_topic_score_codex":0.009674674,"about_ca_topic_score_gemma":0.0077753,"teacher_disagreement_score":0.13432507,"about_ca_system_score_codex":0.008987263,"about_ca_system_score_gemma":0.051406723,"threshold_uncertainty_score":0.71038735},"labels":[],"label_agreement":null},{"id":"W3205170951","doi":"10.1093/jamia/ocab220","title":"Evaluation of the International Classification of Health Interventions (ICHI) in the coding of common surgical procedures","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Institute for Health Information","funders":"U.S. National Library of Medicine; Intramural Research Program; National Institutes of Health","keywords":"SNOMED CT; Coding (social sciences); Psychological intervention; Concordance; Medicine; Electronic health record; European union; Computer science; Statistics; Internal medicine; Mathematics; Terminology; Health care; Business; Nursing; Political science","score_opus":0.2745497513005913,"score_gpt":0.5238865335857047,"score_spread":0.2493367822851134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205170951","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":[0.7612003,0.008315563,0.12639022,0.008376845,0.0019561257,0.013962613,0.040384874,0.0010623052,0.03835119],"genre_scores_gemma":[0.8417727,0.0012640998,0.1286476,0.00065637537,0.00026041645,0.007869928,0.018354477,0.0002294409,0.00094494346],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.8925686,0.04537936,0.020501781,0.005617726,0.033562,0.002370556],"domain_scores_gemma":[0.74630684,0.12048751,0.04208846,0.015579687,0.07271813,0.0028193982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10570657,0.0011373861,0.0009157281,0.01490625,0.0012017632,0.0038753524,0.002270957,0.0010331878,0.0022443742],"category_scores_gemma":[0.26754248,0.00031594164,0.0016497856,0.014439775,0.0020431273,0.004173925,0.003339193,0.0014217902,0.0006752344],"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":[0.00093813887,0.00027927075,0.8333404,0.0020009265,0.00032730523,0.00012292583,0.0028016479,0.0022459852,0.00081579894,0.004309206,0.011400241,0.14141808],"study_design_scores_gemma":[0.00021894077,0.0009172165,0.9259263,0.0039349967,0.0005736626,0.0006921411,0.00734603,0.029576518,0.0029671742,0.0045956383,0.023088716,0.00016271259],"about_ca_topic_score_codex":0.013003,"about_ca_topic_score_gemma":0.009530324,"teacher_disagreement_score":0.10570657,"about_ca_system_score_codex":0.005960323,"about_ca_system_score_gemma":0.011702564,"threshold_uncertainty_score":0.5590365},"labels":[],"label_agreement":null},{"id":"W4206647565","doi":"10.1093/jamia/ocab281","title":"Knowledge and insights from a maturing international clinical quality registry","year":2021,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Prostate Cancer 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":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Movember Foundation","keywords":"Benchmarking; Quality (philosophy); Health care; Protocol (science); Process (computing); Quality management; Data quality; Computer science; Patient registry; Medicine; Knowledge management; Data science; Business; Operations management; Political science; Alternative medicine; Pediatrics; Pathology; Engineering","score_opus":0.028591021495335513,"score_gpt":0.38485851032027796,"score_spread":0.35626748882494247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206647565","genre_codex":"commentary","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":[0.109482475,0.014623421,0.038421575,0.7501436,0.002851256,0.0006558413,0.0012212541,0.00039834555,0.0822022],"genre_scores_gemma":[0.76069325,0.027612904,0.1091589,0.08787944,0.0032161062,0.0011821457,0.0025983618,0.00040872252,0.0072502275],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9012708,0.07137017,0.009440724,0.003950464,0.009876123,0.0040917005],"domain_scores_gemma":[0.6524722,0.23088585,0.029268555,0.03055727,0.034328196,0.022487866],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19007598,0.00037645228,0.0005803873,0.0040912335,0.0035157357,0.018490689,0.0032500976,0.003213833,0.004662978],"category_scores_gemma":[0.2216543,0.0005965482,0.000784451,0.0057166247,0.006045421,0.018239044,0.011283525,0.008141303,0.0008646766],"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":[0.00021645414,0.000807303,0.08711069,0.003092971,0.00011788829,0.0027286757,0.16343696,0.0015371976,0.0009532379,0.16720815,0.10849998,0.46429053],"study_design_scores_gemma":[0.00009973067,0.0005008741,0.028261814,0.012746525,0.00015007985,0.003659745,0.15926257,0.0023353132,0.001327018,0.06266656,0.7287726,0.00021729132],"about_ca_topic_score_codex":0.0045668636,"about_ca_topic_score_gemma":0.0075326934,"teacher_disagreement_score":0.19007598,"about_ca_system_score_codex":0.007400779,"about_ca_system_score_gemma":0.03664508,"threshold_uncertainty_score":0.9987805},"labels":[],"label_agreement":null},{"id":"W4212821862","doi":"10.1093/jamia/ocac022","title":"Challenges and strategies for promoting health equity in virtual care: findings and policy directions from a scoping review of reviews","year":2022,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Public Health Ontario; Ontario Council of University Libraries; University of Toronto; Women's College Hospital","funders":"Ministry of Health, Ontario","keywords":"Health equity; Health care; CINAHL; Equity (law); Population; Population health; Thematic analysis; Health literacy; Scopus; Digital health; MEDLINE; Social determinants of health; Knowledge management; Business; Public relations; Medicine; Political science; Nursing; Economic growth; Computer science; Qualitative research; Environmental health; Sociology; Psychological intervention; Economics; Social science","score_opus":0.1317169542032928,"score_gpt":0.5019728982535314,"score_spread":0.37025594405023865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212821862","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":[0.00084412267,0.97998536,0.0011647933,0.014877863,0.00069192547,0.00038070985,0.0001658399,0.000016969765,0.0018724012],"genre_scores_gemma":[0.017239569,0.9709909,0.004696387,0.0049817287,0.00034922428,0.0012806478,0.00016728326,0.000014674496,0.0002795969],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9405396,0.032487493,0.013851865,0.002437247,0.008511255,0.0021725243],"domain_scores_gemma":[0.719202,0.23301598,0.01653091,0.0034095396,0.026032511,0.0018090806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08253522,0.0019122342,0.004990446,0.022025175,0.0025821382,0.014300607,0.0030416907,0.0053124647,0.00459249],"category_scores_gemma":[0.20343111,0.0017540992,0.006261363,0.022491423,0.0039610453,0.016615836,0.006989381,0.004362299,0.000669103],"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":[0.00013208363,0.00006113519,0.0023434688,0.6707157,0.0025264777,0.00033267852,0.0071090944,0.00070828677,0.00036802422,0.01900416,0.016257243,0.2804416],"study_design_scores_gemma":[0.000028383742,0.000051592262,0.0013703078,0.9295567,0.0025553678,0.00013960006,0.005786414,0.00016770215,0.00014099234,0.0056099626,0.05455988,0.000033143886],"about_ca_topic_score_codex":0.016631214,"about_ca_topic_score_gemma":0.029504802,"teacher_disagreement_score":0.08253522,"about_ca_system_score_codex":0.013813574,"about_ca_system_score_gemma":0.074063785,"threshold_uncertainty_score":0.43649322},"labels":[],"label_agreement":null},{"id":"W4247722841","doi":"10.1136/jamia.2000.0070021","title":"Virtual Congresses","year":2000,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Conferences and Exhibitions Management","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"The Internet; Computer science; Engineering ethics; World Wide Web; Engineering","score_opus":0.008980016236215001,"score_gpt":0.2908971773830907,"score_spread":0.28191716114687565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247722841","genre_codex":"other","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":[0.026065195,0.0083022155,0.03214932,0.016927166,0.01033395,0.0004870886,0.0010894593,0.0017933178,0.90285224],"genre_scores_gemma":[0.36954686,0.006154123,0.023305837,0.0068838173,0.006823114,0.0009390882,0.002060406,0.000806847,0.5834798],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99478406,0.0026364492,0.00024480143,0.00063069555,0.00096775056,0.00073622994],"domain_scores_gemma":[0.98849154,0.0028035822,0.0011265695,0.0023810377,0.0008671739,0.0043301526],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0042531025,0.00070645945,0.00044737768,0.0017938467,0.005808728,0.0067937374,0.0015587809,0.0018662642,0.11234682],"category_scores_gemma":[0.012302854,0.0003590957,0.0007706665,0.0013690953,0.0023716153,0.008458351,0.010984746,0.0027706614,0.02115314],"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":[0.0002813153,0.0002453058,0.0027227076,0.00038026707,0.00005733621,0.00045113047,0.0057007326,0.0010084775,0.0009120007,0.41712233,0.3899057,0.18121274],"study_design_scores_gemma":[0.00001288884,0.000044082535,0.0006260429,0.00007375033,0.0000055079136,0.0002212165,0.0011530305,0.00017700373,0.00022066233,0.014042508,0.98340684,0.000016471467],"about_ca_topic_score_codex":0.00066139764,"about_ca_topic_score_gemma":0.0012159762,"teacher_disagreement_score":0.99320626,"about_ca_system_score_codex":0.0010407344,"about_ca_system_score_gemma":0.0018294113,"threshold_uncertainty_score":0.37583756},"labels":[],"label_agreement":null},{"id":"W4283519332","doi":"10.1093/jamia/ocac103","title":"Impact of artificial intelligence on pathologists’ decisions: an experiment","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Toronto Metropolitan University; Ted Rogers Centre for Heart Research","funders":"Social Sciences and Humanities Research Council","keywords":"Artificial intelligence; Generalization; Machine learning; Odds; Computer science; Process (computing); Medicine; Mathematics","score_opus":0.1366147232418004,"score_gpt":0.4752907634436966,"score_spread":0.3386760402018962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283519332","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":[0.996543,0.000027542192,0.0007462565,0.00014265589,0.000033688102,0.0012304815,0.00011409979,0.00002346058,0.0011387456],"genre_scores_gemma":[0.98185045,0.00007373016,0.00982312,0.00053074094,0.000095108204,0.0059531163,0.00018443784,0.00001530198,0.0014739437],"study_design_codex":"nonrandomized_trial","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98371744,0.010692534,0.0015127299,0.0015295312,0.001724733,0.0008230243],"domain_scores_gemma":[0.7156689,0.24966156,0.018158376,0.009149337,0.0033712822,0.003990493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018061807,0.00075460767,0.00070408545,0.000445792,0.0008791291,0.0017452484,0.001287479,0.0020341896,0.0081838835],"category_scores_gemma":[0.07497274,0.0009065601,0.00072367553,0.00043027688,0.0013591853,0.0019141209,0.0018090904,0.0018411918,0.0009133923],"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":[0.20872577,0.33486733,0.1913536,0.0032976936,0.0009038497,0.0008126061,0.03265358,0.0107988715,0.046253208,0.002992453,0.004408759,0.16293226],"study_design_scores_gemma":[0.06059305,0.56987864,0.23935694,0.0007219728,0.0010144598,0.00068388093,0.008510217,0.049514715,0.047434736,0.0074870484,0.014188897,0.00061553164],"about_ca_topic_score_codex":0.00045967093,"about_ca_topic_score_gemma":0.00042877538,"teacher_disagreement_score":0.018061807,"about_ca_system_score_codex":0.0007277417,"about_ca_system_score_gemma":0.0017841647,"threshold_uncertainty_score":0.09552109},"labels":[],"label_agreement":null},{"id":"W4289731377","doi":"10.1093/jamia/ocac122","title":"Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"QRS complex; Segmentation; Hypertrophic cardiomyopathy; Artificial intelligence; Medicine; Atrial fibrillation; Cardiology; Repolarization; Internal medicine; Electrocardiography; Pattern recognition (psychology); ST segment; Computer science; Electrophysiology; Myocardial infarction","score_opus":0.022405301797863042,"score_gpt":0.32141831104992846,"score_spread":0.2990130092520654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289731377","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":[0.040920127,0.00077397545,0.9504101,0.00025230006,0.00009945973,0.00014652449,0.0004342793,0.0054084314,0.0015547728],"genre_scores_gemma":[0.19335529,0.0005431168,0.79860395,0.00030652393,0.0001828364,0.00016544489,0.0019332038,0.0006424196,0.0042671342],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993641,0.00008283137,0.00005296154,0.0002083455,0.00023367193,0.000058120462],"domain_scores_gemma":[0.9990101,0.0002611107,0.00013649509,0.00012395185,0.00039422477,0.00007413879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082887453,0.001023609,0.00077016215,0.0023847101,0.0004135664,0.0009690717,0.0008770111,0.0013124101,0.002052241],"category_scores_gemma":[0.002154609,0.00030078265,0.0009521142,0.0011670414,0.00042582673,0.00062019203,0.00094967167,0.0007032134,0.0021036423],"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":[0.00040089374,0.00017844266,0.010561519,0.000267676,0.00017154789,0.00067594944,0.00025497016,0.019377403,0.21363322,0.0016289405,0.009467515,0.74338186],"study_design_scores_gemma":[0.000098662786,0.0002170572,0.021121264,0.00009766494,0.00015856711,0.0021074894,0.00014884585,0.86877024,0.088820435,0.0035201919,0.014865101,0.00007453511],"about_ca_topic_score_codex":0.0025830127,"about_ca_topic_score_gemma":0.004006648,"teacher_disagreement_score":0.0025830127,"about_ca_system_score_codex":0.00028974618,"about_ca_system_score_gemma":0.0008061773,"threshold_uncertainty_score":0.006865442},"labels":[],"label_agreement":null},{"id":"W4289888731","doi":"10.1093/jamia/ocac129","title":"Patient judgments about hypertension control: the role of patient numeracy and graph literacy","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber River Regional Hospital; University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Agency for Healthcare Research and Quality","keywords":"Numeracy; Literacy; Graph; Control (management); Health literacy; Computer science; Medicine; Psychology; Artificial intelligence; Theoretical computer science; Political science; Health care","score_opus":0.008520207020051958,"score_gpt":0.3336925696954413,"score_spread":0.32517236267538935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289888731","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":[0.99515533,0.00025365592,0.0008289255,0.00072661164,0.000016311704,0.000028455659,0.00007313654,0.00002371884,0.0028938982],"genre_scores_gemma":[0.99919873,0.00006081201,0.0005200599,0.000079621655,0.000007087713,0.000009569603,0.000026544183,0.0000036646713,0.000093894574],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950695,0.0032593368,0.00032294221,0.00027773753,0.0008707523,0.00019969267],"domain_scores_gemma":[0.94871527,0.03854384,0.008345605,0.0011203896,0.0016631924,0.0016117208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055548884,0.00025019082,0.00026601614,0.0006839732,0.0003767219,0.0019217181,0.00025790732,0.00045790547,0.0047221375],"category_scores_gemma":[0.0699427,0.00018491957,0.00044635707,0.00033062237,0.0008287367,0.0012261559,0.0011393591,0.0007669605,0.0002477461],"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":[0.0017279814,0.00083571835,0.9005932,0.00030848917,0.00032085585,0.0002519982,0.014686462,0.000596896,0.0015698767,0.0004901111,0.001781331,0.07683701],"study_design_scores_gemma":[0.00012007813,0.0014809751,0.976154,0.00026977467,0.00017927456,0.00062026014,0.01160396,0.0038974334,0.0012315693,0.0017267761,0.0026119284,0.00010393799],"about_ca_topic_score_codex":0.0013314157,"about_ca_topic_score_gemma":0.001593557,"teacher_disagreement_score":0.0055548884,"about_ca_system_score_codex":0.0005455914,"about_ca_system_score_gemma":0.00046462007,"threshold_uncertainty_score":0.0293774},"labels":[],"label_agreement":null},{"id":"W4294011699","doi":"10.1093/jamia/ocac160","title":"PAN-cODE: COVID-19 forecasting using conditional latent ODEs","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"National Cancer Institute; Ontario Institute for Cancer Research; Memorial Sloan-Kettering Cancer Center","keywords":"Code (set theory); Pandemic; Computer science; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Machine learning; Econometrics; Deep learning; Economics; Medicine; Disease; Infectious disease (medical specialty)","score_opus":0.26398263093902813,"score_gpt":0.4381007259146337,"score_spread":0.1741180949756056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294011699","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":[0.20416239,0.0007839767,0.76482743,0.0033960796,0.00046829286,0.00011689616,0.0132541815,0.0046885656,0.008302114],"genre_scores_gemma":[0.89738053,0.0003490123,0.0874056,0.00044318603,0.0001325764,0.00011829447,0.00883504,0.00026027352,0.005075538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980754,0.000052744763,0.000014143844,0.00005952362,0.00003212817,0.000033852393],"domain_scores_gemma":[0.9989524,0.00061119284,0.000102244856,0.00008275241,0.00018154531,0.000069725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076912285,0.00058713625,0.00056015846,0.0005544884,0.0002972018,0.00076754065,0.0010312608,0.00095162034,0.0045788907],"category_scores_gemma":[0.004304063,0.00037003824,0.0007704789,0.0005065877,0.00032237687,0.00090952555,0.0009791944,0.0016442656,0.0005845493],"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":[0.000053087017,0.000033344702,0.0071013486,0.000034386117,0.000032147447,0.00005029462,0.000028537339,0.96443605,0.00034030314,0.005093999,0.004882201,0.017914245],"study_design_scores_gemma":[0.0000032168946,0.0000023578393,0.00019253835,0.000002771804,0.0000015120249,0.000002902915,0.0000026293567,0.9979791,0.000059252026,0.0014248234,0.00032660612,0.000002274746],"about_ca_topic_score_codex":0.045891352,"about_ca_topic_score_gemma":0.05166336,"teacher_disagreement_score":0.045891352,"about_ca_system_score_codex":0.00095114723,"about_ca_system_score_gemma":0.0015206228,"threshold_uncertainty_score":0.09124845},"labels":[],"label_agreement":null},{"id":"W4296471666","doi":"10.1093/jamia/ocac143","title":"Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital; Université de Montréal","funders":"National Center for Advancing Translational Sciences; National Eye Institute; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Healthcare delivery; Health care; Clinical decision support system; Decision support system; Computer science; Personalized medicine; Clinical decision making; Health care delivery; Medicine; Artificial intelligence; Intensive care medicine; Bioinformatics","score_opus":0.08028676825393923,"score_gpt":0.47096100211049097,"score_spread":0.39067423385655176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296471666","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":[0.026302347,0.012593515,0.6004817,0.30757174,0.0026228921,0.002389274,0.0019246615,0.017852146,0.028261812],"genre_scores_gemma":[0.1501257,0.0068850075,0.8134819,0.019915419,0.0018500552,0.00088508835,0.0022225361,0.0006928589,0.003941392],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9795435,0.0119413175,0.0024283868,0.0016979278,0.0035104682,0.00087842677],"domain_scores_gemma":[0.9271129,0.040547747,0.0038585034,0.010430262,0.011326553,0.0067240987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029370116,0.0010897346,0.0014001116,0.0035488096,0.0014586027,0.011669377,0.0045939945,0.0037502826,0.009935349],"category_scores_gemma":[0.0632032,0.0009631348,0.0013153364,0.004052053,0.0014848857,0.009668929,0.004235616,0.0051802015,0.006626831],"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":[0.000296533,0.00078371214,0.0077159056,0.0008870727,0.0003093865,0.00025265085,0.00064903003,0.007878217,0.0020959207,0.019050203,0.1013239,0.85875744],"study_design_scores_gemma":[0.0010521956,0.0014747385,0.012050701,0.004478638,0.00046645853,0.0017289568,0.00451,0.13452102,0.0075916033,0.24585164,0.58560634,0.00066784443],"about_ca_topic_score_codex":0.0049437596,"about_ca_topic_score_gemma":0.0070664384,"teacher_disagreement_score":0.029370116,"about_ca_system_score_codex":0.0027384602,"about_ca_system_score_gemma":0.011029247,"threshold_uncertainty_score":0.15532589},"labels":[],"label_agreement":null},{"id":"W4307337446","doi":"10.1093/jamia/ocac204","title":"An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Hotchkiss Brain Institute, University of Calgary","keywords":"Sample (material); Sample size determination; Computer science; Data collection; Machine learning; Artificial intelligence; Training set; Mean absolute error; Large sample; Data mining; Statistics; Mean squared error; Mathematics","score_opus":0.027239313865338063,"score_gpt":0.2953892222012933,"score_spread":0.26814990833595526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307337446","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":[0.7728258,0.0015358914,0.22053596,0.001398924,0.000101442914,0.00019594778,0.00033913742,0.0009344482,0.0021324474],"genre_scores_gemma":[0.9809862,0.00012935883,0.017989969,0.00011818919,0.000025346859,0.00007459077,0.0002650944,0.000027731798,0.00038350586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972063,0.0015209834,0.0001326168,0.0005980113,0.00031423298,0.00022782733],"domain_scores_gemma":[0.9690076,0.024195239,0.0013116153,0.002486712,0.0023303712,0.00066835724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010352351,0.0010616862,0.00087812444,0.0005383306,0.0005034968,0.0008196575,0.0014447132,0.0013310614,0.0010070418],"category_scores_gemma":[0.03300174,0.00041597034,0.00062670466,0.00040008075,0.0009875007,0.002423489,0.0017234752,0.0014406919,0.00019969427],"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":[0.0008241112,0.00025203914,0.018059375,0.00013704284,0.00013984228,0.00014835536,0.00009630863,0.92728186,0.0016152504,0.0011445471,0.0009890485,0.049312323],"study_design_scores_gemma":[0.00004485736,0.0004286426,0.0035298846,0.000021122918,0.00003325704,0.000068709654,0.00007524318,0.9920061,0.0017776962,0.0017385217,0.00026562597,0.000010278537],"about_ca_topic_score_codex":0.0070182825,"about_ca_topic_score_gemma":0.0043313415,"teacher_disagreement_score":0.010352351,"about_ca_system_score_codex":0.001099586,"about_ca_system_score_gemma":0.0013027157,"threshold_uncertainty_score":0.05474913},"labels":[],"label_agreement":null},{"id":"W4307775477","doi":"10.1093/jamia/ocac196","title":"Assessing the carbon footprint of digital health interventions: a scoping review","year":2022,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Regina","funders":"","keywords":"Carbon footprint; Psychological intervention; CINAHL; PsycINFO; Computer science; Checklist; Health care; Scopus; Digital health; MEDLINE; Telemedicine; Medicine; Nursing; Psychology","score_opus":0.1540488315603577,"score_gpt":0.47958011892243646,"score_spread":0.32553128736207876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307775477","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":[0.00079499994,0.9924777,0.0013513194,0.0014489684,0.0004432439,0.0012213668,0.0005359159,0.00002199191,0.001704472],"genre_scores_gemma":[0.009422586,0.9833672,0.0032731623,0.00080379227,0.00020904021,0.002374302,0.0003606262,0.000019120956,0.00017015007],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9449584,0.025694745,0.0140439775,0.0030151655,0.0113952635,0.000892429],"domain_scores_gemma":[0.7965343,0.16842552,0.01693969,0.0031319766,0.014336604,0.00063194556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05770254,0.0029838218,0.008523117,0.026227301,0.0019050392,0.009529391,0.0032237987,0.0045225886,0.0069132866],"category_scores_gemma":[0.18698843,0.0019217957,0.011224479,0.023165293,0.003062492,0.008108179,0.005123953,0.0036903273,0.00077560457],"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":[0.00017288087,0.000058097015,0.000900593,0.7786195,0.0052775447,0.00007067215,0.00046469926,0.00079880934,0.00020657691,0.0037514302,0.003949091,0.20573013],"study_design_scores_gemma":[0.000043253167,0.00009984922,0.000997717,0.96439815,0.0088179605,0.00007392842,0.00029580935,0.00016936722,0.00020980265,0.0014304986,0.023435827,0.000027936705],"about_ca_topic_score_codex":0.009630045,"about_ca_topic_score_gemma":0.016828164,"teacher_disagreement_score":0.05770254,"about_ca_system_score_codex":0.010289797,"about_ca_system_score_gemma":0.03406559,"threshold_uncertainty_score":0.30516386},"labels":[],"label_agreement":null},{"id":"W4309650827","doi":"10.1093/jamia/ocac216","title":"Machine learning approaches for electronic health records phenotyping: a methodical review","year":2022,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Artificial intelligence; Computer science; Scalability; Data science; Deep learning; Data mining; Database","score_opus":0.08341922040659462,"score_gpt":0.3967048151865077,"score_spread":0.3132855947799131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309650827","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":[0.00010996021,0.98936135,0.005601733,0.0033280482,0.0006909245,0.00009729214,0.00013286673,0.00003109762,0.00064667925],"genre_scores_gemma":[0.003322511,0.98119915,0.012124629,0.0016009922,0.0009691002,0.0004059536,0.0001704604,0.000022599774,0.00018451881],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9826826,0.008603506,0.0035327293,0.0013076679,0.0036263345,0.00024710948],"domain_scores_gemma":[0.844797,0.13586672,0.005643738,0.0028283633,0.010364778,0.0004994172],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03358793,0.0016735364,0.003214835,0.019716768,0.0009097349,0.0042113685,0.0034940904,0.002711654,0.0037983654],"category_scores_gemma":[0.09231218,0.0010740289,0.0034801634,0.0152462,0.0023441624,0.0065103984,0.0027013149,0.0040738545,0.0012705935],"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":[0.00007442815,0.00007422231,0.0013615083,0.14162718,0.0016207292,0.00013644193,0.00034478714,0.0013018225,0.00021157383,0.019833852,0.035363365,0.79805005],"study_design_scores_gemma":[0.000082218634,0.00018863808,0.004921043,0.3699305,0.004014039,0.0010898772,0.000474085,0.0035003845,0.00076873554,0.035471912,0.5793186,0.00024000889],"about_ca_topic_score_codex":0.0034181657,"about_ca_topic_score_gemma":0.0037114788,"teacher_disagreement_score":0.96641207,"about_ca_system_score_codex":0.004456003,"about_ca_system_score_gemma":0.009663013,"threshold_uncertainty_score":0.1776321},"labels":[],"label_agreement":null},{"id":"W4317749998","doi":"10.1093/jamia/ocad002","title":"MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"Hospital for Sick Children","keywords":"Interoperability; Data format; Computer science; Patient data; World Wide Web; Database; Computer hardware","score_opus":0.048243751187236554,"score_gpt":0.3648114203584823,"score_spread":0.31656766917124574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317749998","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":[0.031457547,0.0008322913,0.42401892,0.008304917,0.0012854437,0.0027367265,0.3528714,0.13238817,0.046104573],"genre_scores_gemma":[0.1292602,0.00095255254,0.34704584,0.0026328515,0.00021915857,0.0012886646,0.50122696,0.00828865,0.009085088],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99434185,0.0017196231,0.0009134984,0.0010833547,0.0015976563,0.00034405943],"domain_scores_gemma":[0.98690724,0.003151775,0.0007458604,0.0063272077,0.002362059,0.0005058749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009337399,0.0010396047,0.0005757586,0.0025873468,0.0006852853,0.0042963456,0.0030995305,0.001002224,0.01181346],"category_scores_gemma":[0.032540005,0.00090480054,0.001747522,0.0030302107,0.0006475646,0.004849849,0.004587941,0.0018417202,0.0112799555],"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":[0.0014328852,0.00032307368,0.026956072,0.001464442,0.00037351533,0.0007220965,0.0016276374,0.03849955,0.00810149,0.061798774,0.64218754,0.216513],"study_design_scores_gemma":[0.0003197399,0.00038424117,0.014508152,0.0010353687,0.0001379269,0.00078915566,0.0011764655,0.09521709,0.021371318,0.03945696,0.82533616,0.00026738233],"about_ca_topic_score_codex":0.015183928,"about_ca_topic_score_gemma":0.016159512,"teacher_disagreement_score":0.015183928,"about_ca_system_score_codex":0.0027135734,"about_ca_system_score_gemma":0.0056267367,"threshold_uncertainty_score":0.049381435},"labels":[],"label_agreement":null},{"id":"W4321748673","doi":"10.1093/jamia/ocad022","title":"A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Palliative Care and End-of-Life Issues","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scrutiny; Workflow; Stakeholder; Grounded theory; Qualitative research; Harm; Medicine; Advance care planning; Nursing; Psychology; Management science; Palliative care; Public relations; Computer science; Sociology; Political science; Engineering; Social psychology; Law","score_opus":0.11233380788061192,"score_gpt":0.5147537424773828,"score_spread":0.4024199345967709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321748673","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":[0.15877719,0.0024820352,0.61971736,0.100607306,0.00057792955,0.0051019015,0.00025800604,0.00040047627,0.11207787],"genre_scores_gemma":[0.63528985,0.000986197,0.35192305,0.0038774903,0.00006317631,0.002543672,0.00013664368,0.00013068209,0.0050492533],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.85672295,0.12865557,0.0028559456,0.0028274497,0.004990386,0.0039476976],"domain_scores_gemma":[0.8798176,0.097078905,0.005731607,0.0042946506,0.008226164,0.0048510535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10058085,0.0018468157,0.0006471592,0.006418678,0.022662034,0.018450828,0.006018559,0.009930233,0.004086286],"category_scores_gemma":[0.06961123,0.0013365555,0.0017073326,0.0035817877,0.040732753,0.017792718,0.017673407,0.009297908,0.0008516475],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072525996,0.00041692547,0.00622453,0.0006381737,0.000027040007,0.0048912186,0.70382494,0.002517577,0.0010669322,0.24428134,0.0052159936,0.030822804],"study_design_scores_gemma":[0.00008382349,0.0001986803,0.0016566808,0.0024972134,0.000043605825,0.0026954722,0.7490852,0.009166763,0.0016555138,0.12080513,0.11199658,0.00011532645],"about_ca_topic_score_codex":0.012931899,"about_ca_topic_score_gemma":0.01898756,"teacher_disagreement_score":0.10058085,"about_ca_system_score_codex":0.022847628,"about_ca_system_score_gemma":0.037941966,"threshold_uncertainty_score":0.5319287},"labels":[],"label_agreement":null},{"id":"W4321748937","doi":"10.1093/jamia/ocad009","title":"Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alnylam Pharmaceuticals; AstraZeneca","keywords":"Observational study; Interquartile range; Cohort; Cohort study; Computer science; Inclusion (mineral); Variable (mathematics); Baseline (sea); Medicine; Implementation; Psychology; Statistics; Mathematics; Surgery; Pathology","score_opus":0.7985450138999625,"score_gpt":0.6273149303618455,"score_spread":0.17123008353811697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321748937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6636138,0.0022440162,0.30718082,0.001405128,0.0009970608,0.012718159,0.0020168016,0.0015709399,0.008253207],"genre_scores_gemma":[0.86028016,0.00017363537,0.12990287,0.00060542603,0.00019722375,0.006643464,0.0012061516,0.0004649816,0.0005261581],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.38297865,0.42620528,0.103827074,0.033309724,0.050934497,0.0027447348],"domain_scores_gemma":[0.16026595,0.575684,0.104048714,0.10708114,0.05037116,0.0025490092],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.47286445,0.0011017381,0.0015672954,0.0041662245,0.0020605302,0.004428727,0.0033997886,0.0018899057,0.0016227777],"category_scores_gemma":[0.619253,0.0011589713,0.0027858287,0.0033541531,0.0042622276,0.0023195026,0.008166512,0.0017473558,0.00049548846],"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":[0.0059637823,0.0008649871,0.73489845,0.0041629686,0.0042687375,0.0010214405,0.057613645,0.006788154,0.009930574,0.007454625,0.00745395,0.15957859],"study_design_scores_gemma":[0.0019909842,0.0060375733,0.78369325,0.0054663243,0.004402686,0.0031312392,0.022356942,0.05397513,0.03840275,0.02952416,0.050059438,0.00095958624],"about_ca_topic_score_codex":0.0012373133,"about_ca_topic_score_gemma":0.0013874375,"teacher_disagreement_score":0.52713555,"about_ca_system_score_codex":0.0030779,"about_ca_system_score_gemma":0.005142828,"threshold_uncertainty_score":0.650052},"labels":[],"label_agreement":null},{"id":"W4365458831","doi":"10.1093/jamia/ocad064","title":"Mapping 3 procedure coding systems to the International Classification of Health Interventions (ICHI): coverage and challenges","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Institute for Health Information","funders":"U.S. National Library of Medicine; Patient-Centered Outcomes Research Institute; Intramural Research Program; National Institutes of Health; Kaiser Permanente","keywords":"SNOMED CT; Coding (social sciences); Computer science; Consistency (knowledge bases); Psychological intervention; Diagnosis code; Redundancy (engineering); Data mining; Medicine; Artificial intelligence; Mathematics; Statistics; Terminology","score_opus":0.2557472243060515,"score_gpt":0.45507757185196684,"score_spread":0.19933034754591533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365458831","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":[0.44117114,0.010998874,0.41930854,0.02982374,0.0016634568,0.0037715808,0.03603542,0.0025416142,0.05468562],"genre_scores_gemma":[0.6346338,0.004519939,0.3283271,0.0033634335,0.0002650935,0.003191983,0.023243504,0.0008715684,0.0015834915],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9120583,0.03836334,0.013544839,0.0062670372,0.027679881,0.002086647],"domain_scores_gemma":[0.776067,0.11880728,0.03641479,0.02103642,0.045869898,0.0018045085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.059478864,0.0007734295,0.00088307494,0.0134824505,0.001573208,0.0036928128,0.0030768,0.0011137547,0.0024750354],"category_scores_gemma":[0.21730666,0.0004709402,0.0014211438,0.027182255,0.002287034,0.004001914,0.0045915246,0.0020508596,0.00061711035],"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":[0.00044241853,0.00015326103,0.34079626,0.0057181874,0.00046439058,0.00045206444,0.014845271,0.0056387926,0.003334648,0.03740794,0.028838105,0.5619087],"study_design_scores_gemma":[0.00016892317,0.0006112472,0.58370507,0.017031806,0.0008596862,0.0030105961,0.039209362,0.044841133,0.0118303355,0.07667296,0.2214567,0.00060212746],"about_ca_topic_score_codex":0.062147338,"about_ca_topic_score_gemma":0.05383347,"teacher_disagreement_score":0.062147338,"about_ca_system_score_codex":0.008621097,"about_ca_system_score_gemma":0.022197396,"threshold_uncertainty_score":0.31455803},"labels":[],"label_agreement":null},{"id":"W4381082024","doi":"10.1093/jamia/ocad102","title":"Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Public Health Policies and Education","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; University at Albany; Queen's University Belfast; AIDS Institute, New York State Department of Health; New York State Department of Health","keywords":"Usability; Computer science; Dashboard; Data science; Stakeholder; Data collection; World Wide Web; Human–computer interaction","score_opus":0.33185185588828203,"score_gpt":0.5083714971318191,"score_spread":0.17651964124353703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381082024","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":[0.2223774,0.001327353,0.72820854,0.015914673,0.00089058775,0.0058512916,0.0006146568,0.0053239637,0.019491479],"genre_scores_gemma":[0.30358252,0.0008584125,0.6836419,0.0017025323,0.00013310196,0.0045348955,0.00049810024,0.00059765985,0.004450891],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9611914,0.0303938,0.0016255487,0.0021908418,0.0036872046,0.0009111639],"domain_scores_gemma":[0.92460334,0.052717797,0.0022639283,0.006033645,0.011071034,0.0033101572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04825767,0.001322931,0.0006148571,0.0015201386,0.0031121003,0.0071142344,0.0028760417,0.0016719804,0.003412573],"category_scores_gemma":[0.05558293,0.0008683653,0.0010295474,0.0010961832,0.0047169183,0.004578982,0.004927659,0.002902777,0.00069327897],"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":[0.0010551458,0.0047118496,0.028578522,0.0064696125,0.00040597716,0.0018593814,0.18643957,0.028577972,0.028484222,0.027983887,0.037731647,0.6477022],"study_design_scores_gemma":[0.0015466255,0.012277653,0.045729246,0.0072959075,0.0007701558,0.0019046894,0.10438446,0.08639725,0.042584125,0.06840745,0.62758714,0.0011153651],"about_ca_topic_score_codex":0.004987615,"about_ca_topic_score_gemma":0.0074064136,"teacher_disagreement_score":0.04825767,"about_ca_system_score_codex":0.0033333185,"about_ca_system_score_gemma":0.009636001,"threshold_uncertainty_score":0.25521398},"labels":[],"label_agreement":null},{"id":"W4384663801","doi":"10.1093/jamia/ocad135","title":"Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"DoD Alzheimer's Disease Neuroimaging Initiative; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; BioClinica; Alzheimer's Association; National Institute on Aging; Eli Lilly and Company; Bristol-Myers Squibb; National Institute of General Medical Sciences; Commonwealth Scientific and Industrial Research Organisation; U.S. Department of Defense","keywords":"Memantine; Regimen; Medicine; Depression (economics); Reinforcement learning; Disease; Donepezil; Alzheimer's Disease Neuroimaging Initiative; Concomitant; Population; Artificial intelligence; Dementia; Machine learning; Computer science; Internal medicine","score_opus":0.08607594809556891,"score_gpt":0.38775277139549397,"score_spread":0.30167682329992507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384663801","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":[0.4575455,0.002557245,0.52601236,0.0040237405,0.00021595562,0.00044326135,0.0008364111,0.001983177,0.0063824398],"genre_scores_gemma":[0.9539398,0.0002516829,0.043739732,0.00041235687,0.00004762573,0.00017434364,0.00043389847,0.000033340802,0.0009671949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989549,0.0005338625,0.000059124388,0.00023489915,0.00009917745,0.00011803431],"domain_scores_gemma":[0.99171376,0.007083089,0.00048558018,0.00019475931,0.00033536347,0.00018748728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025985232,0.0011062578,0.0010118681,0.0009058162,0.00036078843,0.00089061784,0.00097074424,0.0011143814,0.0019211958],"category_scores_gemma":[0.01022962,0.00059418735,0.000752873,0.0004969029,0.00079989986,0.001023082,0.0006174016,0.0020316753,0.00025441605],"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":[0.00013095321,0.00019858254,0.0042686467,0.00005902873,0.00006053896,0.000052553587,0.000035715297,0.9681336,0.00020993127,0.0010828625,0.00067160843,0.025095869],"study_design_scores_gemma":[0.000017374627,0.000043007734,0.00020969227,0.000006961575,0.000009645668,0.000005512403,0.0000053878057,0.9979804,0.00011137628,0.0015011326,0.000106135194,0.0000033357499],"about_ca_topic_score_codex":0.011937374,"about_ca_topic_score_gemma":0.009710833,"teacher_disagreement_score":0.011937374,"about_ca_system_score_codex":0.0015703524,"about_ca_system_score_gemma":0.0021751535,"threshold_uncertainty_score":0.023735821},"labels":[],"label_agreement":null},{"id":"W4385690802","doi":"10.1093/jamia/ocad153","title":"Evaluating the representation of disaster hazards in SNOMED CT: gaps and opportunities","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Disaster Response and Management","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"SNOMED CT; Terminology; Hazard; Disaster risk reduction; Computer science; Health care; Data science; Environmental resource management; Environmental planning; Geography; Environmental science; Political science","score_opus":0.14866548045957018,"score_gpt":0.4884991542622431,"score_spread":0.33983367380267293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385690802","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":[0.68501955,0.008448844,0.2621581,0.0090540955,0.00030236927,0.0020333081,0.015451778,0.0019297791,0.015602244],"genre_scores_gemma":[0.7726993,0.0019497437,0.21481298,0.00085628015,0.00006702732,0.000727623,0.008265809,0.00025007626,0.0003711376],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9794231,0.010750924,0.0039089676,0.0014201156,0.0040502353,0.00044653888],"domain_scores_gemma":[0.903562,0.0631351,0.011685886,0.0060538827,0.014619253,0.0009439515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03316918,0.0005633995,0.0005503422,0.0087915575,0.0009105197,0.0034036702,0.0014480341,0.0011148184,0.0014074417],"category_scores_gemma":[0.11362079,0.00029910804,0.0010069995,0.0068719643,0.0012900954,0.006466115,0.0038565851,0.00080158084,0.0003634525],"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":[0.0018496756,0.00046615818,0.39264664,0.011978107,0.0008692946,0.0014416855,0.033431385,0.023957139,0.015466131,0.029176284,0.017953308,0.47076434],"study_design_scores_gemma":[0.0002556939,0.0019171622,0.32967454,0.019978061,0.0024239433,0.0065683387,0.083722435,0.21671104,0.04211029,0.08310304,0.21300226,0.000533161],"about_ca_topic_score_codex":0.007844629,"about_ca_topic_score_gemma":0.0095397625,"teacher_disagreement_score":0.03316918,"about_ca_system_score_codex":0.0020690754,"about_ca_system_score_gemma":0.0051895482,"threshold_uncertainty_score":0.17541748},"labels":[],"label_agreement":null},{"id":"W4386215461","doi":"10.1093/jamia/ocad175","title":"Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Logistic regression; Medicine; Receiver operating characteristic; Retrospective cohort study; Cohort; Electronic health record; Machine learning; Emergency medicine; Internal medicine; Health care; Computer science","score_opus":0.04195921669440161,"score_gpt":0.35838289835082526,"score_spread":0.31642368165642365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386215461","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":[0.90479046,0.0007468885,0.08919351,0.000611774,0.00006783019,0.0002638813,0.0010486931,0.0006739151,0.002603039],"genre_scores_gemma":[0.97377735,0.00016575347,0.023647998,0.0001461485,0.000041580803,0.000080932165,0.001545489,0.000047306636,0.0005474029],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995837,0.002084803,0.000306413,0.0010481183,0.00055937644,0.00016439191],"domain_scores_gemma":[0.9824086,0.010553434,0.0019846698,0.0017910157,0.0027874033,0.00047488522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01110317,0.000711611,0.00089985144,0.0009021642,0.000351889,0.0010096302,0.00069110136,0.0006293203,0.0010853605],"category_scores_gemma":[0.025729127,0.00032407357,0.0008331029,0.00061343296,0.0004350423,0.0012704459,0.000860767,0.0011794099,0.0006755392],"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":[0.0011019845,0.0009970252,0.6722099,0.00024087087,0.0010506079,0.00018600849,0.00026991006,0.1544943,0.003004385,0.0005405323,0.00569881,0.16020565],"study_design_scores_gemma":[0.00008088426,0.0010290864,0.089037105,0.00010799116,0.00015345066,0.00021537994,0.00014401258,0.9039796,0.002852344,0.0011932915,0.0011737302,0.000033067656],"about_ca_topic_score_codex":0.0032321266,"about_ca_topic_score_gemma":0.0051454,"teacher_disagreement_score":0.01110317,"about_ca_system_score_codex":0.0008122761,"about_ca_system_score_gemma":0.0014653981,"threshold_uncertainty_score":0.058719873},"labels":[],"label_agreement":null},{"id":"W4386437708","doi":"10.1093/jamia/ocad171","title":"Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Alberta Children's Hospital; Women and Children’s Health Research Institute; Université de Montréal; University of Alberta; University of Calgary","funders":"Canadian Institutes of Health Research; Canadian Open Neuroscience Platform; Canada Research Chairs; Parkinson Vereniging; Consortium canadien en neurodégénérescence associée au vieillissement; Natural Sciences and Engineering Research Council of Canada; Parkinson Association of Alberta; Hotchkiss Brain Institute, University of Calgary","keywords":"Artificial intelligence; Computer science; Preprocessor; Pattern recognition (psychology); Neuroimaging; Logistic regression; Magnetic resonance imaging; Jacobian matrix and determinant; Medical imaging; Machine learning; Mathematics; Radiology; Medicine","score_opus":0.05609623764239912,"score_gpt":0.327063794625436,"score_spread":0.2709675569830369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386437708","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":[0.40659532,0.0010218191,0.58732784,0.0009028144,0.00009644514,0.00010760299,0.0005808404,0.0015510555,0.0018162424],"genre_scores_gemma":[0.9634372,0.00020008947,0.034022048,0.00016785982,0.00003692462,0.0000836944,0.0007371941,0.000056683035,0.0012582544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994568,0.0001477273,0.000042014075,0.00020890226,0.00008361395,0.000060882212],"domain_scores_gemma":[0.99828416,0.00093100226,0.0002761223,0.00022651118,0.00020306172,0.00007920821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021624907,0.001255213,0.0005862919,0.0006637271,0.00019162195,0.0010904751,0.0011079225,0.00103869,0.0011056615],"category_scores_gemma":[0.006063893,0.0003993492,0.00073587225,0.0005532396,0.0006363116,0.0013799032,0.0010806487,0.0013306342,0.00042163618],"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":[0.0005971664,0.00045848466,0.041951124,0.00021932849,0.00040892928,0.0002456302,0.0002036475,0.5969447,0.017048603,0.0033313856,0.0021205454,0.33647045],"study_design_scores_gemma":[0.0000124879925,0.00010536608,0.0028751746,0.000022380098,0.000030244772,0.000029940054,0.000024354651,0.9910074,0.0030178449,0.0025041956,0.0003604606,0.000010157876],"about_ca_topic_score_codex":0.0035300043,"about_ca_topic_score_gemma":0.004037052,"teacher_disagreement_score":0.0035300043,"about_ca_system_score_codex":0.00071470335,"about_ca_system_score_gemma":0.00064209837,"threshold_uncertainty_score":0.011436462},"labels":[],"label_agreement":null},{"id":"W4387446212","doi":"10.1093/jamia/ocad196","title":"Combining uncertainty-aware predictive modeling and a bedtime<i>Smart Snack</i>intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Diabetes Management and Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Bedtime; Nocturnal; Hypoglycemia; Medicine; Receiver operating characteristic; Intervention (counseling); Machine learning; Diabetes mellitus; Computer science; Internal medicine; Endocrinology; Psychiatry","score_opus":0.011004151972471277,"score_gpt":0.2860122789784805,"score_spread":0.27500812700600924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387446212","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":[0.63445634,0.0020050695,0.3547423,0.0023006296,0.0001837375,0.000118092095,0.0011157164,0.0007935079,0.0042845956],"genre_scores_gemma":[0.9890962,0.00019628453,0.009707384,0.00016527034,0.00003870616,0.000032884967,0.0003369761,0.000011263211,0.00041500805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9997036,0.0001162,0.000019151426,0.000082982246,0.00004224219,0.000035827514],"domain_scores_gemma":[0.9987417,0.0009060931,0.0001494787,0.000040375053,0.00011552229,0.00004695296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012600416,0.00073869666,0.0006256877,0.00036497868,0.0002049719,0.00055627734,0.00078361755,0.00046726764,0.0005868258],"category_scores_gemma":[0.0037134378,0.00029645584,0.00070977386,0.000219486,0.00023002108,0.0005785318,0.0006435191,0.0009459556,0.00008623075],"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":[0.00023680429,0.00011938457,0.011990047,0.000055744567,0.00009387534,0.0000858023,0.000039135117,0.96128774,0.0003570864,0.000534662,0.0004996127,0.0247002],"study_design_scores_gemma":[0.0000073072792,0.000034501463,0.0009863153,0.0000075284743,0.000017244556,0.000006894136,0.0000053580225,0.9981894,0.000096351876,0.0005593511,0.00008576671,0.0000039028914],"about_ca_topic_score_codex":0.019755593,"about_ca_topic_score_gemma":0.017857108,"teacher_disagreement_score":0.019755593,"about_ca_system_score_codex":0.0006138895,"about_ca_system_score_gemma":0.00074414135,"threshold_uncertainty_score":0.03928125},"labels":[],"label_agreement":null},{"id":"W4387932158","doi":"10.1093/jamia/ocad206","title":"Time to treat the climate and nature crisis as one indivisible global health emergency","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Business; Medicine; Computer science","score_opus":0.014353495283432249,"score_gpt":0.3435162921994017,"score_spread":0.32916279691596945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387932158","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031337922,0.01543338,0.0006378755,0.9152086,0.06507678,0.000015757796,0.00005771068,0.00010518765,0.0031512915],"genre_scores_gemma":[0.0062462753,0.013022757,0.001602501,0.90668,0.060547274,0.00007265339,0.00016102863,0.00021761736,0.0114499265],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99168795,0.0028080835,0.000496783,0.00083719037,0.0025533657,0.001616587],"domain_scores_gemma":[0.9603866,0.014978872,0.0015615445,0.0015820502,0.0057252347,0.01576571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014839238,0.0021644877,0.0025328395,0.0019093417,0.011810904,0.02182294,0.0041022524,0.038650136,0.047226727],"category_scores_gemma":[0.032244492,0.0010762376,0.0021041809,0.0014955122,0.013467599,0.035155244,0.014659094,0.06953499,0.017030248],"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":[0.000050843806,0.000061080216,0.00019232587,0.00026316225,0.000028281383,0.00027326652,0.0017103151,0.00004887806,0.00023515137,0.014126675,0.9624071,0.02060296],"study_design_scores_gemma":[0.00003154335,0.00005524406,0.00030479924,0.00070140336,0.000015240543,0.0003350497,0.0046019936,0.000074717354,0.000068525536,0.019875051,0.97388965,0.000046945115],"about_ca_topic_score_codex":0.00465255,"about_ca_topic_score_gemma":0.00797214,"teacher_disagreement_score":0.047226727,"about_ca_system_score_codex":0.005567823,"about_ca_system_score_gemma":0.015842335,"threshold_uncertainty_score":0.15798914},"labels":[],"label_agreement":null},{"id":"W4387963398","doi":"10.1093/jamia/ocad205","title":"Systematic replication of smoking disease associations using survey responses and EHR data in the <i>All of Us</i> Research Program","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Human Genome Research Institute; National Institutes of Health","keywords":"Phenome; Bonferroni correction; Biobank; Replication (statistics); Meta-analysis; Medicine; MEDLINE; Disease; Bioinformatics; Phenotype; Internal medicine; Biology; Genetics; Statistics","score_opus":0.14019893392524135,"score_gpt":0.44335573665997646,"score_spread":0.3031568027347351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387963398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47881243,0.12914385,0.29984027,0.008524648,0.006368789,0.01835562,0.0410203,0.0020464435,0.015887707],"genre_scores_gemma":[0.91925085,0.004689851,0.052881647,0.0028233065,0.00048379402,0.00943957,0.009380976,0.00039218835,0.0006577785],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.752613,0.17871091,0.031402927,0.022478526,0.012025217,0.0027694264],"domain_scores_gemma":[0.65015155,0.16395812,0.033757467,0.124339536,0.02605884,0.0017344904],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19046944,0.0024899596,0.0046519055,0.00452509,0.0022180714,0.004900369,0.0038578806,0.0027939023,0.0025329804],"category_scores_gemma":[0.37286448,0.0024101634,0.01801067,0.0076522226,0.0030678615,0.0020363154,0.0043918695,0.0022523305,0.00070076925],"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":[0.006283794,0.00042519206,0.5132008,0.023609165,0.38855755,0.00089136744,0.002338794,0.0031173287,0.008040584,0.003836864,0.008260942,0.041437577],"study_design_scores_gemma":[0.006928229,0.005171461,0.50847083,0.010521902,0.41773108,0.0012189556,0.0010705974,0.0041904896,0.007732548,0.009464282,0.027106805,0.0003929418],"about_ca_topic_score_codex":0.008822761,"about_ca_topic_score_gemma":0.012505259,"teacher_disagreement_score":0.80953056,"about_ca_system_score_codex":0.0015974074,"about_ca_system_score_gemma":0.0058842283,"threshold_uncertainty_score":0.9982953},"labels":[],"label_agreement":null},{"id":"W4389219213","doi":"10.1093/jamia/ocad231","title":"Methods for studying medication safety following electronic health record implementation in acute care: a scoping review","year":2023,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Covenant Health; University of Alberta; Queen's University; Alberta Health Services","funders":"Marie Curie; Canadian Nurses Foundation","keywords":"Data extraction; Patient safety; Medicine; Consistency (knowledge bases); Health care; MEDLINE; Computer science","score_opus":0.11027487917369498,"score_gpt":0.6136372877109095,"score_spread":0.5033624085372146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389219213","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":[0.0036955485,0.9329965,0.01903024,0.006662625,0.002175224,0.025650624,0.002907026,0.00013699674,0.006745187],"genre_scores_gemma":[0.020391438,0.8755612,0.05281564,0.0021915722,0.0006111215,0.045063384,0.002228763,0.000078091616,0.0010588021],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.8450648,0.060133398,0.06619688,0.0047784396,0.022412602,0.0014138228],"domain_scores_gemma":[0.63180286,0.27179316,0.041634094,0.009619639,0.04390109,0.0012492003],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.14024986,0.002932423,0.0066422992,0.060742702,0.0038792342,0.011263785,0.0045822887,0.005145752,0.005961733],"category_scores_gemma":[0.33154684,0.0024565742,0.008975452,0.05031361,0.0030584796,0.011890229,0.0059306477,0.0035705378,0.0015041314],"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":[0.00013710026,0.00008974061,0.002574289,0.73535466,0.0030485499,0.0002376766,0.003389089,0.0003883624,0.0005085975,0.0040539107,0.007950336,0.24226776],"study_design_scores_gemma":[0.00005847281,0.00011052403,0.0019531474,0.95670754,0.004712068,0.0001422612,0.0016166415,0.00014710342,0.00039454605,0.0017152614,0.032397658,0.000044796972],"about_ca_topic_score_codex":0.006726512,"about_ca_topic_score_gemma":0.014443052,"teacher_disagreement_score":0.85975015,"about_ca_system_score_codex":0.009969798,"about_ca_system_score_gemma":0.053805653,"threshold_uncertainty_score":0.74172103},"labels":[],"label_agreement":null},{"id":"W4390043054","doi":"10.1093/jamia/ocad235","title":"CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Hôpital du Sacré-Cœur de Montréal; Centre Hospitalier Universitaire de Sherbrooke; McGill University Health Centre; Centre Hospitalier de l’Université de Montréal; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Jewish General Hospital; Université Laval; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Canadian Institutes of Health Research; Réseau en Bio-Imagerie du Quebec; McGill University","keywords":"Computer science; Coda; Documentation; Scalability; Pooling; Data science; License; Database; Artificial intelligence","score_opus":0.06281733640683719,"score_gpt":0.3483821133581296,"score_spread":0.2855647769512924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390043054","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":[0.007276884,0.00046719992,0.66679853,0.0018850714,0.00035751052,0.0013774881,0.017263861,0.30000135,0.004572097],"genre_scores_gemma":[0.16350298,0.00082771713,0.73394656,0.0022347032,0.00022485528,0.0029249843,0.0664057,0.024059748,0.005872725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957925,0.00095124345,0.0005484944,0.0010309311,0.0013342142,0.00034261518],"domain_scores_gemma":[0.9880876,0.004976135,0.00085699407,0.003437411,0.0016012948,0.0010404245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007825313,0.0019346129,0.0011885761,0.0028169493,0.0011076428,0.003951166,0.003855252,0.0013372501,0.010883647],"category_scores_gemma":[0.029635083,0.001033114,0.002555769,0.0021556518,0.0016195872,0.0032382126,0.008200175,0.0027448186,0.0057496903],"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":[0.0035108589,0.0007106852,0.022390371,0.0028371874,0.0017417263,0.0019034382,0.0021509663,0.11632407,0.016983397,0.045693334,0.38813657,0.39761743],"study_design_scores_gemma":[0.001085786,0.00024894747,0.009900342,0.0005371474,0.00016444008,0.0005461187,0.00042761664,0.6586188,0.013316403,0.10283472,0.211933,0.0003865973],"about_ca_topic_score_codex":0.018995253,"about_ca_topic_score_gemma":0.016842261,"teacher_disagreement_score":0.018995253,"about_ca_system_score_codex":0.001831369,"about_ca_system_score_gemma":0.006484929,"threshold_uncertainty_score":0.041384697},"labels":[],"label_agreement":null},{"id":"W4390046685","doi":"10.1093/jamia/ocad226","title":"Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Computer science; Receiver operating characteristic; Variance (accounting); Software; Artificial intelligence; Machine learning; Data mining; Algorithm","score_opus":0.06715583229088423,"score_gpt":0.37725283958016675,"score_spread":0.3100970072892825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390046685","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":[0.033582754,0.0012336553,0.9567319,0.00044940822,0.00009483092,0.00037929544,0.0018007085,0.0046397033,0.0010876629],"genre_scores_gemma":[0.42544314,0.0005742369,0.56538844,0.0004628539,0.00025529257,0.0013216217,0.004840794,0.0011393358,0.00057427795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9601316,0.02775461,0.0027245265,0.0047588465,0.0042416486,0.00038881635],"domain_scores_gemma":[0.81581897,0.13285014,0.019276438,0.013926866,0.016773215,0.0013542641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.041321337,0.0023755175,0.0022725188,0.0060201967,0.00079938705,0.0035097501,0.0021045883,0.0017368566,0.002047627],"category_scores_gemma":[0.1501269,0.00080583483,0.0025616821,0.0025446438,0.001590498,0.0020279884,0.0024203518,0.002863818,0.0015704862],"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":[0.0013590709,0.00069433916,0.095275275,0.002766398,0.003055076,0.00059135363,0.0009791274,0.376243,0.015474364,0.01142453,0.026923666,0.4652138],"study_design_scores_gemma":[0.00007518409,0.00031797556,0.013964917,0.00019660633,0.00016898943,0.0003267359,0.00010628706,0.9578266,0.0074133174,0.013728843,0.0057510403,0.00012359628],"about_ca_topic_score_codex":0.0019962187,"about_ca_topic_score_gemma":0.0020208508,"teacher_disagreement_score":0.041321337,"about_ca_system_score_codex":0.0012258687,"about_ca_system_score_gemma":0.002934112,"threshold_uncertainty_score":0.21853071},"labels":[],"label_agreement":null},{"id":"W4390126703","doi":"10.1093/jamia/ocad234","title":"From illness management to quality of life: rethinking consumer health informatics opportunities for progressive, potentially fatal illnesses","year":2023,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Quality of life (healthcare); Medicine; Taboo; Focus group; Psychological intervention; Psychology; Gerontology; Nursing; Sociology","score_opus":0.054123099323009224,"score_gpt":0.37255430226070857,"score_spread":0.31843120293769933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390126703","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":[0.7430626,0.009728932,0.039600514,0.1199615,0.0012076201,0.000667721,0.0003002888,0.00036527778,0.085105546],"genre_scores_gemma":[0.9707944,0.0032937697,0.018307662,0.0044557606,0.00017016614,0.00029271445,0.000084097475,0.00006963354,0.002531816],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911151,0.007073165,0.00021395528,0.00028719942,0.0009827565,0.00032775156],"domain_scores_gemma":[0.9850374,0.0121204695,0.0006372023,0.0006268103,0.00078648503,0.0007916631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015410155,0.00040601537,0.00033312914,0.0012949266,0.0020448908,0.00686437,0.0011360986,0.0011847431,0.0036313082],"category_scores_gemma":[0.02269781,0.00026586908,0.00067193166,0.0010458272,0.0064723208,0.0077488977,0.0057300483,0.0027117392,0.00028580363],"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":[0.00020131987,0.000288274,0.02477628,0.001675313,0.00006706942,0.00058752176,0.6686137,0.00020861332,0.0012447637,0.04147987,0.007838376,0.25301895],"study_design_scores_gemma":[0.00011030537,0.0007012727,0.046745863,0.0056911353,0.00022742925,0.0011398873,0.56697077,0.0018593896,0.0020141487,0.044809967,0.32960993,0.00011998145],"about_ca_topic_score_codex":0.002235196,"about_ca_topic_score_gemma":0.0054546474,"teacher_disagreement_score":0.015410155,"about_ca_system_score_codex":0.003279119,"about_ca_system_score_gemma":0.003955336,"threshold_uncertainty_score":0.08149767},"labels":[],"label_agreement":null},{"id":"W4391150982","doi":"10.1093/jamia/ocad260","title":"Comparison of phenomic profiles in the <i>All of Us</i> Research Program against the US general population and the UK Biobank","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Institutes of Health; U.S. National Library of Medicine; Institute for Health Metrics and Evaluation","keywords":"Biobank; Cohort; Phenome; Medicine; Population; Cohort study; Public health; Population health; Biorepository; Gerontology; Demography; Family medicine; Environmental health; Bioinformatics; Pathology; Biology","score_opus":0.02835439898497713,"score_gpt":0.3794730432842964,"score_spread":0.35111864429931927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391150982","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":[0.9887975,0.0006952538,0.0008049571,0.00050035835,0.00003321273,0.00011363353,0.0063104094,0.000022338412,0.0027222468],"genre_scores_gemma":[0.99292713,0.00037809167,0.000754714,0.0004514518,0.000030697665,0.0002788203,0.004645312,0.000017737768,0.0005160481],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99598,0.0015253816,0.0005863051,0.0008352664,0.0006318857,0.00044112498],"domain_scores_gemma":[0.9919912,0.0012569065,0.0037861532,0.001260759,0.0011000044,0.0006049638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032635955,0.00024768634,0.00044963634,0.0021153854,0.0008475973,0.001294201,0.0006063472,0.0004835791,0.0031312034],"category_scores_gemma":[0.013124001,0.00021554911,0.0003849548,0.0034550647,0.000540026,0.00066016836,0.0020183078,0.0003661989,0.00042356987],"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":[0.00036342593,0.000038474907,0.9900394,0.00008910799,0.00016503668,0.00010994011,0.00092087995,0.000043788914,0.00097081653,0.00027925725,0.0026625246,0.004317315],"study_design_scores_gemma":[0.000018189021,0.000111297326,0.99533087,0.00006010474,0.000086409134,0.00028160628,0.0006991541,0.00010033351,0.00019943049,0.00009098371,0.003013247,0.000008425855],"about_ca_topic_score_codex":0.018002696,"about_ca_topic_score_gemma":0.01974529,"teacher_disagreement_score":0.018002696,"about_ca_system_score_codex":0.0008309429,"about_ca_system_score_gemma":0.00086408487,"threshold_uncertainty_score":0.035795808},"labels":[],"label_agreement":null},{"id":"W4391225179","doi":"10.1093/jamia/ocad252","title":"Harnessing the potential of large language models in medical education: promise and pitfalls","year":2024,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"National Key Research and Development Program of China; National Institutes of Health; National Natural Science Foundation of China","keywords":"Narrative; Misconduct; Process (computing); Engineering ethics; Psychology; Medicine; Medical education; Political science; Computer science; Engineering","score_opus":0.05065315228572722,"score_gpt":0.4462854854152415,"score_spread":0.3956323331295143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391225179","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":[0.0007082891,0.98454833,0.003489879,0.006619507,0.0002822221,0.000037989037,0.000020716165,0.000044727076,0.004248333],"genre_scores_gemma":[0.013978996,0.9735651,0.008343503,0.00283935,0.00049114926,0.00012740378,0.00003354925,0.000026484118,0.0005944683],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945851,0.0032703932,0.0004396797,0.00027344603,0.0012633614,0.0001680961],"domain_scores_gemma":[0.9608026,0.035093475,0.0011465529,0.0008734455,0.0017183513,0.0003655456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013860897,0.00065294467,0.0013640898,0.0026753512,0.0004823821,0.004972745,0.0015670321,0.0022637425,0.0032619922],"category_scores_gemma":[0.023026265,0.0005565624,0.0013303086,0.0019899728,0.0023917286,0.008070602,0.0032702023,0.0040885764,0.0012845118],"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":[0.000052116466,0.00008600522,0.0004098605,0.017132644,0.00016760787,0.00010987977,0.00057079847,0.00064602226,0.00044555028,0.018194646,0.0054628346,0.9567221],"study_design_scores_gemma":[0.00007664071,0.0004430524,0.0016594307,0.06358122,0.00051753136,0.0018530932,0.0013683686,0.0011906825,0.0017076867,0.03586619,0.8916225,0.00011370152],"about_ca_topic_score_codex":0.0026763284,"about_ca_topic_score_gemma":0.0047368477,"teacher_disagreement_score":0.013860897,"about_ca_system_score_codex":0.001233351,"about_ca_system_score_gemma":0.0062292214,"threshold_uncertainty_score":0.073304296},"labels":[],"label_agreement":null},{"id":"W4392131273","doi":"10.1093/jamia/ocae034","title":"Concerted adoption as an emerging strategy for digital transformation of healthcare—lessons from Australia, Canada, and England","year":2024,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scope (computer science); Health care; Public relations; Scale (ratio); Business; Digital transformation; Knowledge management; Political science; Medicine; Geography; Computer science","score_opus":0.11871158292847324,"score_gpt":0.5000714550894892,"score_spread":0.381359872161016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392131273","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":[0.0014252145,0.99210066,0.00014134828,0.003346462,0.00015485288,0.000036279922,0.000019794701,0.0000053145536,0.002770025],"genre_scores_gemma":[0.02148685,0.9758198,0.0007066202,0.0013504546,0.00006535007,0.00004502419,0.00002696935,0.0000042830507,0.0004946494],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962329,0.0012741822,0.0004410054,0.0002622099,0.0014041866,0.0003855394],"domain_scores_gemma":[0.9835496,0.0084328335,0.0011148747,0.00033133922,0.005669963,0.0009012539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011052187,0.0005036631,0.0011089457,0.0040619136,0.0014598345,0.0045334646,0.0010968768,0.001418364,0.0017192005],"category_scores_gemma":[0.016195957,0.0002854091,0.00069829955,0.0074911597,0.0025351124,0.0030442856,0.0019706965,0.0022810097,0.00018017551],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080405254,0.000067431756,0.0018705311,0.053201616,0.0002361282,0.000303251,0.0072209304,0.00040336425,0.00027453827,0.026384808,0.015880967,0.8940759],"study_design_scores_gemma":[0.000040304672,0.00016892418,0.016331142,0.133549,0.000750463,0.00060342415,0.008653789,0.00029256454,0.00047123796,0.004207651,0.8348513,0.00008009985],"about_ca_topic_score_codex":0.44082627,"about_ca_topic_score_gemma":0.60577214,"teacher_disagreement_score":0.5591737,"about_ca_system_score_codex":0.01935751,"about_ca_system_score_gemma":0.07740017,"threshold_uncertainty_score":0.87652075},"labels":[],"label_agreement":null},{"id":"W4392736665","doi":"10.1093/jamia/ocae312","title":"A dataset and benchmark for hospital course summarization with adapted large language models","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Stanford Cardiovascular Institute, School of Medicine, Stanford University; National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Stanford Institute for Human-Centered Artificial Intelligence, Stanford University; National Institute of Nursing Research; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Computer science; Context (archaeology); Automatic summarization; Benchmark (surveying); Artificial intelligence; Benchmarking; Health care; Machine learning; Natural language processing","score_opus":0.005396857601458518,"score_gpt":0.2874210560819696,"score_spread":0.2820241984805111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392736665","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07599941,0.004920384,0.033142865,0.0030161694,0.0012669523,0.0020924387,0.8296386,0.040472914,0.009450236],"genre_scores_gemma":[0.034429986,0.0004046074,0.03589396,0.0005071861,0.00012655709,0.0009023261,0.925126,0.00042539596,0.0021838904],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970482,0.0008382557,0.0005506561,0.0008004621,0.0005865288,0.00017585026],"domain_scores_gemma":[0.99585885,0.0016391554,0.00030337827,0.0008498657,0.0010995228,0.00024915434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024518708,0.0026303076,0.00074425415,0.0029594572,0.0008820754,0.0012243662,0.0029815515,0.002506033,0.006094604],"category_scores_gemma":[0.0117577575,0.00034482474,0.0017582874,0.0024821786,0.0005321296,0.0014767274,0.0015910327,0.0018174007,0.006953512],"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":[0.0016148427,0.0012328629,0.010269221,0.0038180351,0.0004695191,0.0010201482,0.00039688355,0.03021274,0.013314947,0.002258958,0.7378396,0.19755234],"study_design_scores_gemma":[0.0025847212,0.0020368125,0.03862703,0.00088771497,0.00049560174,0.0021978784,0.0014510709,0.23442556,0.03958642,0.009579435,0.6676481,0.000479656],"about_ca_topic_score_codex":0.017364392,"about_ca_topic_score_gemma":0.030341426,"teacher_disagreement_score":0.017364392,"about_ca_system_score_codex":0.0022328002,"about_ca_system_score_gemma":0.002295269,"threshold_uncertainty_score":0.034526646},"labels":[],"label_agreement":null},{"id":"W4392857085","doi":"10.1093/jamia/ocae046","title":"Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"University of Oxford","keywords":"Comorbidity; Comparability; Medicine; ICD-10; Population; Usability; Computer science; Internal medicine; Environmental health; Psychiatry","score_opus":0.1355587884015753,"score_gpt":0.5073630482366541,"score_spread":0.3718042598350788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392857085","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":[0.9754691,0.00067755656,0.017659739,0.0016821272,0.00023378423,0.00039104396,0.0019077245,0.00007460857,0.0019042855],"genre_scores_gemma":[0.9800903,0.00023221056,0.016837921,0.00038298924,0.000071908995,0.00031769398,0.0018890236,0.000030511348,0.00014748592],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.916284,0.058638938,0.008233482,0.0053822035,0.009956142,0.0015050934],"domain_scores_gemma":[0.74093765,0.18288863,0.040653206,0.016125789,0.016840445,0.0025542728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07438381,0.0006653573,0.00062792434,0.001934272,0.00092164165,0.0031808019,0.0014666931,0.0008367116,0.0014428048],"category_scores_gemma":[0.23809287,0.00040590702,0.0016179302,0.0026537762,0.0011944888,0.002184412,0.0034421615,0.0017967683,0.00018638262],"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":[0.00031228014,0.00009947794,0.97167623,0.00014745176,0.00047115312,0.00007424699,0.0008201907,0.0021656766,0.00023470257,0.00059544115,0.0007277153,0.022675438],"study_design_scores_gemma":[0.000032732947,0.0007449018,0.98145264,0.0003368644,0.00042153313,0.00023931693,0.0013767431,0.01078033,0.0009002798,0.0012053964,0.002457091,0.000052299216],"about_ca_topic_score_codex":0.007424018,"about_ca_topic_score_gemma":0.009259265,"teacher_disagreement_score":0.07438381,"about_ca_system_score_codex":0.0018821697,"about_ca_system_score_gemma":0.0032031105,"threshold_uncertainty_score":0.39338386},"labels":[],"label_agreement":null},{"id":"W4399617010","doi":"10.1093/jamia/ocae143","title":"Promoting interoperability between SNOMED CT and ICD-11: lessons learned from the pilot project mapping between SNOMED CT and the ICD-11 Foundation","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Health Information","funders":"Institut canadien d'information sur la santé; U.S. National Library of Medicine; National Institutes of Health; Bundesinstitut für Arzneimittel und Medizinprodukte; World Health Organization","keywords":"SNOMED CT; Interoperability; Systematized Nomenclature of Medicine; Foundation (evidence); Medicine; Computer science; Medical physics; World Wide Web; Terminology; Linguistics; Geography","score_opus":0.04950987139431113,"score_gpt":0.33273088483822744,"score_spread":0.2832210134439163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399617010","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":[0.33281863,0.003597655,0.49293244,0.12792741,0.0017561602,0.009105148,0.002047224,0.0026577169,0.027157566],"genre_scores_gemma":[0.22201794,0.0015029409,0.76415235,0.005024267,0.00024632108,0.0021817815,0.0023059857,0.00058354647,0.0019848398],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9034141,0.074051015,0.0044975784,0.00442997,0.010816492,0.0027908925],"domain_scores_gemma":[0.7495518,0.14464357,0.0069565377,0.029165443,0.060465682,0.009216971],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1936233,0.0013775234,0.00090566574,0.0045276796,0.0036833424,0.0077638086,0.005974958,0.0034164006,0.0032826024],"category_scores_gemma":[0.17148612,0.00094309525,0.0014316215,0.004089464,0.005097743,0.016786158,0.015778836,0.0067516244,0.0008805123],"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":[0.00068926794,0.0034929002,0.03544912,0.0028253207,0.00020001936,0.0020765793,0.08376482,0.014614602,0.010855167,0.033590678,0.043119773,0.7693218],"study_design_scores_gemma":[0.0010325772,0.0055551543,0.050329998,0.013678009,0.00040727464,0.0044882405,0.20710582,0.06932542,0.031100359,0.16068967,0.45532787,0.0009596661],"about_ca_topic_score_codex":0.017616846,"about_ca_topic_score_gemma":0.02134611,"teacher_disagreement_score":0.1936233,"about_ca_system_score_codex":0.0056744693,"about_ca_system_score_gemma":0.031602513,"threshold_uncertainty_score":0.99440604},"labels":[],"label_agreement":null},{"id":"W4400163580","doi":"10.1093/jamia/ocae165","title":"Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Alberta Innovates; Alberta Children's Hospital Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Children's Hospital Foundation","keywords":"Artificial intelligence; Computer science; Medical imaging; Machine learning; Data science","score_opus":0.12362593132143393,"score_gpt":0.46271244339821427,"score_spread":0.3390865120767803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400163580","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":[0.054116357,0.00750685,0.9271209,0.005044977,0.0003118781,0.0017043898,0.0006154369,0.0004930908,0.0030861453],"genre_scores_gemma":[0.49641377,0.0013516301,0.4971486,0.0014319817,0.0003150495,0.0021780478,0.0005585549,0.00019782681,0.00040455238],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.78215283,0.1807224,0.008584677,0.0058743944,0.021883741,0.00078189647],"domain_scores_gemma":[0.42914903,0.44281185,0.04643534,0.044275034,0.03575618,0.0015725832],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.24203841,0.0019399594,0.0018715282,0.0043471535,0.000981789,0.0054512215,0.002871142,0.0027701147,0.001798697],"category_scores_gemma":[0.4775245,0.00080127915,0.00230729,0.002050407,0.0052455156,0.0051052454,0.0052248896,0.003562526,0.00027727467],"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":[0.002162938,0.0010480684,0.11303097,0.00864626,0.008252341,0.00030625088,0.002476497,0.16660842,0.0139648905,0.1733484,0.0083913915,0.50176364],"study_design_scores_gemma":[0.00064078596,0.0038122903,0.03538132,0.006147894,0.0023238447,0.0003644016,0.0013556037,0.5073705,0.038613945,0.38239008,0.021197248,0.00040219657],"about_ca_topic_score_codex":0.00153375,"about_ca_topic_score_gemma":0.0016158728,"teacher_disagreement_score":0.24203841,"about_ca_system_score_codex":0.003908102,"about_ca_system_score_gemma":0.007985067,"threshold_uncertainty_score":0.93470156},"labels":[],"label_agreement":null},{"id":"W4402164554","doi":"10.1093/jamia/ocae220","title":"Foundation model-driven distributed learning for enhanced retinal age prediction","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Economic shortage; Fundus (uterus); Machine learning; Biobank; Deep learning; Linear regression; Retinal; Bioinformatics; Medicine; Ophthalmology","score_opus":0.00990487732864112,"score_gpt":0.30403277748509905,"score_spread":0.29412790015645796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402164554","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":[0.064408235,0.0002913578,0.93220603,0.00028582138,0.00005386392,0.000034850633,0.00008706649,0.0015938599,0.0010389432],"genre_scores_gemma":[0.9320105,0.00008550649,0.06589563,0.00014479281,0.000031919524,0.000062530205,0.00017783121,0.000048662554,0.0015425886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997061,0.000072428986,0.0000143770985,0.00010037344,0.000055852437,0.00005076347],"domain_scores_gemma":[0.9989857,0.00049984775,0.00009718634,0.00013013257,0.00022674624,0.00006036567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010970493,0.00050616794,0.00076109625,0.0002800283,0.00028991723,0.00054526905,0.0015648908,0.00065224495,0.001591232],"category_scores_gemma":[0.003068925,0.0002517147,0.00047160237,0.00030116024,0.00047628587,0.00093194575,0.0010376316,0.0011906945,0.0003635289],"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":[0.00012590285,0.0000854841,0.0014996803,0.000024567771,0.000025813135,0.000054609107,0.00003853206,0.9342256,0.0016458306,0.0019227908,0.0011022909,0.05924896],"study_design_scores_gemma":[0.0000040032573,0.00000909738,0.000047506797,6.5811327e-7,0.0000016673092,0.000003954928,0.000002280298,0.9991248,0.00020056962,0.00055957766,0.000044886143,9.560271e-7],"about_ca_topic_score_codex":0.008556219,"about_ca_topic_score_gemma":0.007661708,"teacher_disagreement_score":0.008556219,"about_ca_system_score_codex":0.00074755587,"about_ca_system_score_gemma":0.0011537012,"threshold_uncertainty_score":0.017012835},"labels":[],"label_agreement":null},{"id":"W4402858717","doi":"10.1093/jamia/ocae209","title":"Toward a responsible future: recommendations for AI-enabled clinical decision support","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Homewood Research Institute; University of Victoria","funders":"National Institute on Drug Abuse","keywords":"Documentation; Certification; Transparency (behavior); Clinical decision support system; Computer science; Software deployment; Health care; Health informatics; Government (linguistics); Patient safety; Decision support system; Knowledge management; Process management; Engineering; Computer security; Artificial intelligence; Software engineering; Political science","score_opus":0.12205135843605994,"score_gpt":0.4989426505512555,"score_spread":0.37689129211519556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402858717","genre_codex":"commentary","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":[0.00039273506,0.01742192,0.008100961,0.96323216,0.004280092,0.00037980947,0.00012298113,0.00022612275,0.00584325],"genre_scores_gemma":[0.06623965,0.15304425,0.26932186,0.47670856,0.009353464,0.00714835,0.0018637487,0.0004058188,0.015914341],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.924403,0.053665172,0.005614835,0.0029183265,0.009207217,0.004191323],"domain_scores_gemma":[0.7131107,0.15416422,0.009644039,0.0094779525,0.06846731,0.045135718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08220896,0.0019624047,0.0019759894,0.0061499747,0.0066394084,0.022068042,0.008560673,0.024756035,0.02643553],"category_scores_gemma":[0.15999192,0.0012023591,0.0034695198,0.0047557685,0.012622376,0.033169035,0.0128269605,0.028675357,0.010403163],"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":[0.00018351956,0.0006531892,0.0029989649,0.006016916,0.000106514344,0.00067095825,0.005383169,0.0017351944,0.0002615075,0.11474025,0.61378694,0.25346282],"study_design_scores_gemma":[0.00021199284,0.00013117169,0.0018188553,0.03468238,0.00012424345,0.00041489364,0.030694433,0.0024194762,0.00028820054,0.24364725,0.6853647,0.00020234981],"about_ca_topic_score_codex":0.015152238,"about_ca_topic_score_gemma":0.028399242,"teacher_disagreement_score":0.08220896,"about_ca_system_score_codex":0.01671663,"about_ca_system_score_gemma":0.09805691,"threshold_uncertainty_score":0.43476772},"labels":[],"label_agreement":null},{"id":"W4405221973","doi":"10.1093/jamia/ocae276","title":"Returning value to communities from the <i>All of Us</i> Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Institute of Nursing Research","keywords":"Library science; Columbia university; Health informatics; Informatics; Political science; Medicine; Gerontology; Sociology; Public health; Media studies; Computer science; Nursing; Law","score_opus":0.3935896888381223,"score_gpt":0.5782048377048025,"score_spread":0.18461514886668018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405221973","genre_codex":"commentary","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":[0.024216885,0.0075508035,0.26038578,0.54108435,0.008050952,0.01034498,0.0079350285,0.0059412997,0.13448991],"genre_scores_gemma":[0.16068901,0.006976972,0.63300735,0.13835639,0.008157452,0.021453723,0.007286765,0.0034954979,0.020576917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.7981055,0.16232368,0.0061532217,0.009824562,0.018988565,0.0046045217],"domain_scores_gemma":[0.50914985,0.27509493,0.014305817,0.11552678,0.049621377,0.036301285],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.22071074,0.0014079965,0.0020714882,0.010673528,0.009662657,0.029399892,0.006022417,0.005384752,0.017210526],"category_scores_gemma":[0.3369034,0.0020388968,0.002287568,0.010789686,0.01063294,0.026324663,0.050361764,0.0130817145,0.008430295],"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":[0.00036459372,0.00069184334,0.010576182,0.0022321832,0.0005792858,0.0003150506,0.045230363,0.00042693145,0.002830395,0.059204344,0.25086737,0.6266815],"study_design_scores_gemma":[0.00032839586,0.0004287039,0.008807775,0.004018005,0.0002151117,0.00023797213,0.02345711,0.0012433437,0.0016458705,0.19336705,0.7659556,0.00029508496],"about_ca_topic_score_codex":0.0062396796,"about_ca_topic_score_gemma":0.011032524,"teacher_disagreement_score":0.77928925,"about_ca_system_score_codex":0.0057148198,"about_ca_system_score_gemma":0.038811076,"threshold_uncertainty_score":0.9610024},"labels":[],"label_agreement":null},{"id":"W4405395152","doi":"10.1093/jamia/ocae303","title":"Effectiveness of electronic medical record-based strategies for death and hospital admission endpoint capture in pragmatic clinical trials","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's College Hospital; University of Toronto; University Health Network","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; U.S. Department of Veterans Affairs","keywords":"Electronic medical record; Medical record; Clinical trial; Event (particle physics); Electronic data capture; Clinical endpoint; Computer science; Medicine; Medical emergency; Internal medicine","score_opus":0.020012396141644042,"score_gpt":0.40143312695222244,"score_spread":0.3814207308105784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405395152","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":[0.24552281,0.0710998,0.4807153,0.033000324,0.0032870478,0.11916957,0.008775631,0.0027765709,0.035653032],"genre_scores_gemma":[0.6541175,0.0039591375,0.28065732,0.0069863447,0.0008705751,0.05063742,0.0019820463,0.00020852656,0.00058104465],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.1538526,0.792631,0.03119309,0.0070436583,0.014471,0.0008085952],"domain_scores_gemma":[0.16873303,0.73651963,0.055140227,0.026796905,0.010179544,0.0026306782],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.59097654,0.0032018244,0.0068286783,0.0052687037,0.0011302001,0.0075907246,0.0036472618,0.0049662776,0.005180673],"category_scores_gemma":[0.753738,0.0025799188,0.007383729,0.0052058403,0.0024535367,0.008738889,0.0059225094,0.004070737,0.0010252466],"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":[0.11966933,0.003667333,0.06412717,0.026088642,0.05257269,0.0003133414,0.0019342605,0.058010116,0.0009798552,0.01960237,0.010574682,0.6424602],"study_design_scores_gemma":[0.19652724,0.064297736,0.0883086,0.023497323,0.031481065,0.0013568654,0.0010781045,0.46070182,0.0051191356,0.08492887,0.04105223,0.001651087],"about_ca_topic_score_codex":0.0013037657,"about_ca_topic_score_gemma":0.0012346092,"teacher_disagreement_score":0.40902346,"about_ca_system_score_codex":0.004828803,"about_ca_system_score_gemma":0.008181833,"threshold_uncertainty_score":0.5043988},"labels":[],"label_agreement":null},{"id":"W4408406005","doi":"10.1093/jamia/ocaf042","title":"Optimizing the efficiency and effectiveness of data quality assurance in a multicenter clinical dataset","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canada Research Chairs","keywords":"False positive paradox; Data quality; Computer science; Quality assurance; Data collection; Data mining; Chart; Identifier; Medicine; Database; Machine learning; Metric (unit); Statistics; Operations management","score_opus":0.07893946645619035,"score_gpt":0.5384986410175016,"score_spread":0.45955917456131123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408406005","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":[0.6490047,0.001403439,0.32987505,0.0036574828,0.00010359972,0.0016735175,0.0021408228,0.009361112,0.0027803192],"genre_scores_gemma":[0.67764246,0.00013389555,0.31962234,0.00026057722,0.000044557273,0.00035700543,0.0015814757,0.00016019866,0.00019740942],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9415664,0.039341602,0.0046556266,0.007274341,0.006097481,0.0010645417],"domain_scores_gemma":[0.8378254,0.10237212,0.015931347,0.025011519,0.016188672,0.0026709794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08165553,0.001246898,0.0013335485,0.0028291,0.0011584857,0.0036810595,0.002772695,0.0012244864,0.00064205093],"category_scores_gemma":[0.18460327,0.0007524216,0.0013658148,0.00371594,0.0014660243,0.0041285832,0.00395602,0.0014263891,0.00020639393],"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":[0.0036296449,0.0015402101,0.23961857,0.00096549303,0.0013065089,0.00027780462,0.0022828442,0.2814248,0.018346142,0.0066145733,0.008661061,0.43533236],"study_design_scores_gemma":[0.0005599458,0.00082695705,0.042164635,0.00013309105,0.00021040527,0.00011269811,0.0005231848,0.93571275,0.0104729505,0.006610132,0.0026047912,0.000068524954],"about_ca_topic_score_codex":0.012577781,"about_ca_topic_score_gemma":0.010054301,"teacher_disagreement_score":0.08165553,"about_ca_system_score_codex":0.0035651303,"about_ca_system_score_gemma":0.0077390014,"threshold_uncertainty_score":0.4318409},"labels":[],"label_agreement":null},{"id":"W4408591745","doi":"10.1093/jamia/ocaf048","title":"Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Concordia University; University of Calgary; Alberta Health Services","funders":"National Institute on Deafness and Other Communication Disorders; Canadian Institutes of Health Research","keywords":"Medicine; Medical record; Population; Pulmonary embolism; Computer science; Chart; Documentation; Data extraction; Artificial intelligence; Natural language processing; Machine learning; MEDLINE; Internal medicine; Statistics","score_opus":0.01764632910414123,"score_gpt":0.35650855718700425,"score_spread":0.33886222808286304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408591745","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":[0.9940515,0.0003372842,0.004324663,0.00030052496,0.000009934992,0.00012307012,0.000512493,0.000053897496,0.00028648437],"genre_scores_gemma":[0.9907165,0.00017607762,0.0070925625,0.00008711098,0.000014679104,0.0000967975,0.0016443911,0.000018177723,0.000153633],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908827,0.006860662,0.0005608637,0.00087978377,0.00061381503,0.00020224454],"domain_scores_gemma":[0.80690384,0.18055964,0.0045484877,0.0034836703,0.0036534828,0.000850858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023097318,0.00081918977,0.0006014127,0.0015735435,0.0006419861,0.0015216563,0.0011389294,0.00082824327,0.0010115568],"category_scores_gemma":[0.09121164,0.00039697197,0.0015799474,0.001325254,0.0008549091,0.0027927933,0.0012789737,0.001798649,0.0003069607],"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":[0.0014401766,0.0028671557,0.89271754,0.0006038755,0.00093482976,0.00059901946,0.0022753777,0.03331523,0.0010965872,0.00068671344,0.0023824908,0.061080877],"study_design_scores_gemma":[0.00033517616,0.0016918945,0.33717906,0.00031690998,0.0008011555,0.0007505875,0.004109273,0.6481089,0.0019454856,0.002317584,0.0023492607,0.00009466455],"about_ca_topic_score_codex":0.025864983,"about_ca_topic_score_gemma":0.02725195,"teacher_disagreement_score":0.025864983,"about_ca_system_score_codex":0.0021070242,"about_ca_system_score_gemma":0.0018696826,"threshold_uncertainty_score":0.12215179},"labels":[],"label_agreement":null},{"id":"W4408725130","doi":"10.1093/jamia/ocaf037","title":"Robust privacy amidst innovation with large language models through a critical assessment of the risks","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Center for Advancing Translational Sciences; National Human Genome Research Institute; U.S. National Library of Medicine; National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; University of Texas Health Science Center at Houston; National Cancer Institute; National Science Foundation; National Institutes of Health; Cancer Prevention and Research Institute of Texas","keywords":"Health Insurance Portability and Accountability Act; Computer science; Software portability; Cosine similarity; Usability; Health care; Information retrieval; F1 score; Natural language processing; Artificial intelligence; Data extraction; Health records; Data quality; Similarity (geometry); Machine learning; Confidentiality; Data mining; Metric (unit); Computer security; MEDLINE; Pattern recognition (psychology); Human–computer interaction","score_opus":0.03137265139190625,"score_gpt":0.38529761437212934,"score_spread":0.3539249629802231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408725130","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":[0.1324808,0.00086070533,0.8567553,0.0036755647,0.00013188599,0.0003817081,0.00044852245,0.001506392,0.0037589993],"genre_scores_gemma":[0.7660894,0.00035642108,0.23056953,0.00048111592,0.00015312282,0.0002894098,0.00072920066,0.00020494359,0.0011268298],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97902894,0.012596126,0.001080741,0.002688919,0.004142761,0.0004625374],"domain_scores_gemma":[0.8799959,0.09855201,0.0054195295,0.009545183,0.005750567,0.0007368057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026366835,0.0012215909,0.0008428009,0.0016033394,0.00091860746,0.0047005,0.0017657863,0.0014288056,0.0010914999],"category_scores_gemma":[0.10965688,0.0005290726,0.001318231,0.0009106164,0.0025485512,0.0068209725,0.0051219235,0.0034567975,0.00054688106],"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":[0.0015106002,0.00057992,0.047106758,0.0009720663,0.0006509402,0.0011177781,0.004440784,0.41618666,0.02096184,0.0615788,0.0051272605,0.43976662],"study_design_scores_gemma":[0.00003295903,0.00025891102,0.0025426853,0.00011201908,0.000104427,0.00030630812,0.0006100609,0.9408714,0.008435147,0.04359626,0.0030750902,0.000054775694],"about_ca_topic_score_codex":0.002507669,"about_ca_topic_score_gemma":0.0020263263,"teacher_disagreement_score":0.026366835,"about_ca_system_score_codex":0.0019182139,"about_ca_system_score_gemma":0.0030564766,"threshold_uncertainty_score":0.1394428},"labels":[],"label_agreement":null},{"id":"W4408957000","doi":"10.1093/jamia/ocaf051","title":"<u>A</u> I- <u>T</u> echniques <u>L</u> oss-Based <u>A</u> lgorithm for <u>S</u> everity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Queen's University","funders":"Southeastern Ontario Academic Medical Organization; Queen's University","keywords":"Atlas (anatomy); Humanities; Physics; Medicine; Art; Anatomy","score_opus":0.02402559944784682,"score_gpt":0.3281858277039071,"score_spread":0.30416022825606026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408957000","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":[0.14804676,0.00020553765,0.8447172,0.0003124771,0.00009707365,0.00020609921,0.00036000193,0.002200313,0.0038544375],"genre_scores_gemma":[0.6196101,0.00013542201,0.37726954,0.0000870015,0.000087421846,0.00026197496,0.000482889,0.00022123412,0.0018443977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901307,0.00024928575,0.00012580215,0.00018248192,0.00036262057,0.00006669988],"domain_scores_gemma":[0.9972314,0.0011161384,0.00049622403,0.00038320984,0.00065756595,0.00011549565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020697599,0.0005817736,0.00047470746,0.0024244045,0.00048063823,0.001637044,0.0008246464,0.000608462,0.0022097016],"category_scores_gemma":[0.008356332,0.00015909976,0.00060488516,0.0010639622,0.0006601328,0.0011859563,0.001149916,0.00078926893,0.00091532373],"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":[0.0009033587,0.00044446214,0.06045753,0.00021772408,0.0001809458,0.00019004742,0.00050704647,0.043410413,0.06615709,0.010158469,0.005751123,0.8116219],"study_design_scores_gemma":[0.00005158768,0.0004764583,0.040590536,0.000057707177,0.00006120746,0.0006308824,0.00019206856,0.9189995,0.02605976,0.008672496,0.004138606,0.00006924808],"about_ca_topic_score_codex":0.0015517406,"about_ca_topic_score_gemma":0.0013681998,"teacher_disagreement_score":0.0024244045,"about_ca_system_score_codex":0.0005396257,"about_ca_system_score_gemma":0.0006376393,"threshold_uncertainty_score":0.010946035},"labels":[],"label_agreement":null},{"id":"W4410232240","doi":"10.1093/jamia/ocaf081","title":"Improved intrahospital transport time via proximity-based staff assignments","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Workload; Duration (music); Computer science; Patient safety; Confidence interval; Observational study; Intervention (counseling); Health care; Medicine; Nursing","score_opus":0.009049234961022008,"score_gpt":0.3457101923136397,"score_spread":0.3366609573526177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410232240","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":[0.96288025,0.00007644312,0.03414957,0.00031022093,0.00003095744,0.00039512204,0.00011459428,0.00032705453,0.0017158261],"genre_scores_gemma":[0.9869622,0.000040712224,0.012431007,0.000038933882,0.000013303685,0.000169104,0.00006393654,0.0000070644487,0.00027376786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789447,0.0013445211,0.000103808925,0.00024124194,0.00025196082,0.00016397638],"domain_scores_gemma":[0.99685234,0.0013341882,0.000983113,0.00023108908,0.0002497773,0.00034954894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017277588,0.0003591031,0.00025909476,0.00038388636,0.0002517586,0.0005420405,0.0007092252,0.00040263202,0.0024043452],"category_scores_gemma":[0.008751247,0.00017380182,0.0003884923,0.0003105948,0.0002640885,0.00058410596,0.0010930706,0.00040511144,0.00024773594],"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":[0.0061977557,0.014280968,0.23835856,0.0010774177,0.00044097475,0.00026369147,0.0017304049,0.31051782,0.02355792,0.003145468,0.003071859,0.3973572],"study_design_scores_gemma":[0.002513826,0.03175348,0.3192864,0.00024318244,0.0005227519,0.00037167527,0.0015596207,0.60670924,0.021271497,0.0037818186,0.011846317,0.00014021175],"about_ca_topic_score_codex":0.0023478677,"about_ca_topic_score_gemma":0.0021553985,"teacher_disagreement_score":0.0024043452,"about_ca_system_score_codex":0.00085232785,"about_ca_system_score_gemma":0.0016875425,"threshold_uncertainty_score":0.009137392},"labels":[],"label_agreement":null},{"id":"W4410288144","doi":"10.1093/jamia/ocaf069","title":"Dependence of premature ventricular complexes on heart rate—it’s not that simple","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; McGill University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Correlation; Linear correlation; Heart rate; Medicine; Cardiology; Positive correlation; Heart rate variability; Linear relationship; Cardiomyopathy; Piecewise linear function; Internal medicine; Mathematics; Statistics; Heart failure; Blood pressure","score_opus":0.008553248093350684,"score_gpt":0.2872664853841851,"score_spread":0.2787132372908344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410288144","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":[0.90811276,0.005482402,0.07685489,0.0020918802,0.00023270772,0.00008304003,0.0013041843,0.0003739727,0.0054642134],"genre_scores_gemma":[0.9934257,0.0006578907,0.004720369,0.00015088299,0.00014173536,0.000019171719,0.00040995516,0.00004070223,0.00043359233],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998575,0.00037833603,0.00016819987,0.00041057004,0.00038865375,0.000079198035],"domain_scores_gemma":[0.98992085,0.005130944,0.00279627,0.0012096533,0.0007942983,0.00014798074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015139219,0.00039921366,0.0004973135,0.0006255145,0.00025157913,0.001106857,0.0006526271,0.0005054407,0.0014597179],"category_scores_gemma":[0.018896597,0.00030900695,0.000525674,0.00063340337,0.0008505183,0.0011704052,0.0005004935,0.0007402537,0.0007890821],"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":[0.00095369114,0.00013204958,0.7448099,0.0006007546,0.00077901233,0.0012866625,0.0007064702,0.021722088,0.028203689,0.0034079107,0.0035328346,0.19386488],"study_design_scores_gemma":[0.000038942246,0.00040983417,0.8910826,0.00019214056,0.00017870707,0.0051288204,0.0002989859,0.08409948,0.0066103027,0.008241335,0.0036257412,0.00009300442],"about_ca_topic_score_codex":0.0015358368,"about_ca_topic_score_gemma":0.001037098,"teacher_disagreement_score":0.0015358368,"about_ca_system_score_codex":0.00022542194,"about_ca_system_score_gemma":0.00032250537,"threshold_uncertainty_score":0.008006513},"labels":[],"label_agreement":null},{"id":"W4411077497","doi":"10.1093/jamia/ocaf077","title":"Policy context and digital development: a comparative study of trajectories in 4 Canadian academic health centers over 30 years","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Bruyère; University of Ottawa; McGill University Health Centre; University of Alberta; Université de Montréal","funders":"Canadian Institutes of Health Research; Fonds de recherche du Québec","keywords":"Restructuring; Context (archaeology); Maturity (psychological); Government (linguistics); Health information technology; Medical prescription; Public relations; Health informatics; Information system; Business; Process management; Health care; Political science; Medicine; Knowledge management; Nursing; Computer science; Public health","score_opus":0.02867451049686293,"score_gpt":0.43286431629125455,"score_spread":0.4041898057943916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411077497","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":[0.99531144,0.00042640851,0.000076570126,0.0008859213,0.000008165182,0.00007592848,0.0010200188,0.0000062668123,0.0021893936],"genre_scores_gemma":[0.9971533,0.000575314,0.00018948536,0.00021676031,0.0000050605763,0.000059256658,0.00071723067,0.0000059142694,0.0010775022],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99677795,0.00037931014,0.00013316347,0.00026780402,0.0006321591,0.0018096316],"domain_scores_gemma":[0.9917543,0.0010034796,0.0015588374,0.0002073714,0.0028784766,0.002597551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003110738,0.00039433493,0.00057456846,0.00481652,0.0106385695,0.005534724,0.0020099662,0.0010931382,0.0031232801],"category_scores_gemma":[0.008781802,0.0004509623,0.0005105155,0.011929266,0.0023312655,0.0026648524,0.0042652115,0.001631566,0.00034144818],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012211551,0.0003089023,0.8350332,0.0001311073,0.000031390817,0.00049171364,0.13292776,0.00023635195,0.00016377382,0.0015621813,0.0027138265,0.026277637],"study_design_scores_gemma":[0.000006510414,0.00007718522,0.72407657,0.0001527909,0.000015047439,0.00007568662,0.26792505,0.00040433582,0.00009305894,0.0001422669,0.0069887494,0.00004279448],"about_ca_topic_score_codex":0.98676,"about_ca_topic_score_gemma":0.9914232,"teacher_disagreement_score":0.9097755,"about_ca_system_score_codex":0.09022451,"about_ca_system_score_gemma":0.11251863,"threshold_uncertainty_score":0.6546277},"labels":[],"label_agreement":null},{"id":"W4411224502","doi":"10.1093/jamia/ocaf088","title":"Tensions in large-scale electronic health record implementations: insights from a meta-synthesis","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières; HEC Montréal","funders":"","keywords":"Implementation; Scale (ratio); Computer science; Electronic health record; Data science; Software engineering; Health care; Political science; Cartography; Geography","score_opus":0.03479294681354498,"score_gpt":0.42342459269655514,"score_spread":0.38863164588301014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411224502","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":[0.37485105,0.47700956,0.06845067,0.027204117,0.0016383416,0.018013794,0.010125459,0.00022661849,0.022480434],"genre_scores_gemma":[0.8652351,0.077108875,0.03987564,0.002954876,0.000119756056,0.011614292,0.0022534803,0.00011969783,0.0007184356],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.8563291,0.09216359,0.03041519,0.0068843346,0.012107811,0.0020999818],"domain_scores_gemma":[0.5428236,0.41623265,0.01527497,0.0094722705,0.015104806,0.0010917515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.18176197,0.0014687959,0.0036334342,0.038185973,0.0034756318,0.01261887,0.0038928252,0.0024266336,0.0036007636],"category_scores_gemma":[0.32816592,0.001629087,0.005804119,0.030446185,0.0056049605,0.015620948,0.00936433,0.00275639,0.00021209415],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062489754,0.00015296353,0.020985804,0.34395465,0.015138655,0.0011051657,0.4106342,0.0015510477,0.0015929615,0.03393413,0.0034021735,0.16692327],"study_design_scores_gemma":[0.00018634177,0.00039596774,0.014005125,0.49749473,0.017745284,0.0006150059,0.3929673,0.0012543093,0.0019730034,0.026191184,0.046984766,0.000186887],"about_ca_topic_score_codex":0.0064368276,"about_ca_topic_score_gemma":0.010198137,"teacher_disagreement_score":0.18176197,"about_ca_system_score_codex":0.01813734,"about_ca_system_score_gemma":0.020141833,"threshold_uncertainty_score":0.9612606},"labels":[],"label_agreement":null},{"id":"W4411330205","doi":"10.1093/jamia/ocaf096","title":"Developing and sustaining inclusive language in biomedical informatics communications: an AMIA Board of Directors endorsed paper on the Inclusive Language and Context Style Guidelines","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; JMIR Publications","funders":"U.S. National Library of Medicine; Agency for Healthcare Research and Quality","keywords":"Plain language; Context (archaeology); Health informatics; Public relations; Transparency (behavior); Computer science; Medical education; Medicine; Public health; Political science; Nursing; Computer security","score_opus":0.030044058199183883,"score_gpt":0.45794655686407354,"score_spread":0.42790249866488966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411330205","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027124526,0.0062860628,0.0232565,0.907881,0.019583408,0.0013326219,0.0001206943,0.00040746955,0.038419757],"genre_scores_gemma":[0.12975238,0.015144574,0.25660813,0.52463365,0.015029226,0.00930937,0.0008113445,0.0009117929,0.047799535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.74871665,0.14665486,0.025699079,0.008744463,0.0554503,0.014734623],"domain_scores_gemma":[0.61778677,0.1777321,0.018925477,0.018919159,0.117548965,0.04908751],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.25074252,0.0015648141,0.0013917901,0.005070644,0.016265525,0.039716955,0.009258036,0.030159418,0.0062762513],"category_scores_gemma":[0.31169805,0.0019730765,0.0021805065,0.004014354,0.023777809,0.026130818,0.032008756,0.040510908,0.0041173],"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":[0.00004279444,0.00041109332,0.0012162927,0.0011838535,0.000038218423,0.00064744684,0.02962384,0.0005350609,0.00085760717,0.27025276,0.57321143,0.1219796],"study_design_scores_gemma":[0.00005552563,0.000091922826,0.0011657751,0.004062472,0.000031297423,0.00020571375,0.010754639,0.00056817423,0.0006654252,0.04476934,0.9374569,0.00017290436],"about_ca_topic_score_codex":0.02441328,"about_ca_topic_score_gemma":0.025656287,"teacher_disagreement_score":0.74925745,"about_ca_system_score_codex":0.018531624,"about_ca_system_score_gemma":0.1703225,"threshold_uncertainty_score":0.92396784},"labels":[],"label_agreement":null},{"id":"W4411505760","doi":"10.1093/jamia/ocaf087","title":"The administrative burden of medication affordability resources: an environmental scan with implications for health informatics to advance health equity","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Equity (law); Documentation; Informatics; Business; Health informatics; Medicine; Resource (disambiguation); Medical emergency; Finance; Nursing; Computer science; Public health; Political science","score_opus":0.03205839917803716,"score_gpt":0.47696199531240235,"score_spread":0.4449035961343652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411505760","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":[0.75583375,0.013813469,0.028826136,0.13589713,0.00016923147,0.00040162483,0.005477897,0.00017595927,0.059404764],"genre_scores_gemma":[0.97306573,0.004153267,0.017957319,0.0034653363,0.00013341007,0.00018314747,0.000601398,0.000031608834,0.00040874397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98484415,0.009063446,0.0012918415,0.0006724073,0.0033733188,0.0007547911],"domain_scores_gemma":[0.8894082,0.08621415,0.014552645,0.0037406234,0.0052605597,0.00082383543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014491664,0.0003351213,0.0003823557,0.0065269447,0.0018488589,0.005089283,0.00091277284,0.0006836395,0.0033575948],"category_scores_gemma":[0.056726575,0.00034523182,0.00062007795,0.009585218,0.0028230357,0.007341301,0.0045259693,0.0011969649,0.00014694448],"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":[0.0001636606,0.0003211842,0.57892317,0.0023331814,0.00030424574,0.00059029146,0.010986979,0.0026830726,0.0009551083,0.05302212,0.010365152,0.33935177],"study_design_scores_gemma":[0.00006038854,0.0004165808,0.69752157,0.010850487,0.00043606068,0.0011092983,0.06989793,0.011582791,0.0034352057,0.07612036,0.12840924,0.00016005026],"about_ca_topic_score_codex":0.010316973,"about_ca_topic_score_gemma":0.022183722,"teacher_disagreement_score":0.014491664,"about_ca_system_score_codex":0.0033914316,"about_ca_system_score_gemma":0.011574684,"threshold_uncertainty_score":0.07664013},"labels":[],"label_agreement":null},{"id":"W4413625789","doi":"10.1093/jamia/ocaf139","title":"Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review","year":2025,"lang":"en","type":"review","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); AstraZeneca (Canada); St. Joseph’s Healthcare Hamilton; University of Toronto; McMaster University; Population Health Research Institute","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Medicine; Clinical decision support system; Medical prescription; MEDLINE; Randomized controlled trial; Prospective cohort study; Meta-analysis; Adverse effect; Emergency medicine; Cohort study; Intervention (counseling); Cluster randomised controlled trial; Incidence (geometry); Internal medicine; Decision support system; Data mining; Pharmacology; Psychiatry","score_opus":0.018320410425022558,"score_gpt":0.47347608422726284,"score_spread":0.45515567380224026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413625789","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":[0.003294092,0.99553263,0.00013951781,0.00017054363,0.000080693135,0.00024162605,0.0003186442,0.000009919307,0.00021228893],"genre_scores_gemma":[0.07624786,0.92082006,0.000967243,0.00059608783,0.00016420025,0.00069525896,0.0003555676,0.000010197977,0.00014349811],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9905201,0.0037088625,0.0030566514,0.00078294467,0.0017448595,0.00018656251],"domain_scores_gemma":[0.9551421,0.033949785,0.0076344274,0.0004341231,0.002522456,0.00031711336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009079509,0.0013227384,0.01022322,0.005627452,0.000458167,0.001979046,0.0015201769,0.0016569179,0.0038649992],"category_scores_gemma":[0.04358213,0.0009170006,0.011767886,0.006158278,0.00070540217,0.0017721445,0.0010942963,0.0012887059,0.000207493],"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":[0.0016327365,0.00006720545,0.0026875834,0.8930464,0.05941598,0.00006963001,0.00009775547,0.00021144179,0.00012102762,0.00013958772,0.0008995034,0.041611157],"study_design_scores_gemma":[0.002311022,0.000984976,0.014284418,0.49620327,0.47614846,0.00029621326,0.00017461306,0.00039662744,0.00039656428,0.00036607223,0.008372474,0.000065249806],"about_ca_topic_score_codex":0.0050172135,"about_ca_topic_score_gemma":0.011331192,"teacher_disagreement_score":0.01022322,"about_ca_system_score_codex":0.0025674491,"about_ca_system_score_gemma":0.004806166,"threshold_uncertainty_score":0.04801762},"labels":[],"label_agreement":null},{"id":"W4414003247","doi":"10.1093/jamia/ocaf134","title":"Transport-based transfer learning on Electronic Health Records: application to detection of treatment disparities","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Provincial Health Services Authority; Vancouver Coastal Health; University of British Columbia","funders":"","keywords":"Computer science; Transfer of learning; Population; Machine learning; Generalization; Artificial intelligence; Context (archaeology); Binary classification; Data mining; Feature (linguistics); Support vector machine; Mathematics; Medicine; Geography","score_opus":0.005414013964400035,"score_gpt":0.2869131495632091,"score_spread":0.28149913559880907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414003247","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":[0.3255377,0.00029353052,0.6702886,0.0013067443,0.00005846767,0.000177886,0.00027026102,0.00068684225,0.0013799851],"genre_scores_gemma":[0.92053825,0.00009260801,0.077672,0.00017945514,0.000048448983,0.00012315623,0.000379672,0.000046907204,0.00091947685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984426,0.0008854787,0.00007847207,0.00027869493,0.00021375496,0.00010099324],"domain_scores_gemma":[0.9956173,0.002794868,0.00040950644,0.00057111436,0.00045238578,0.00015491834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005227621,0.0006516811,0.0006604984,0.0010199298,0.0005233485,0.0007167933,0.001176143,0.0011886305,0.0012613629],"category_scores_gemma":[0.01846841,0.00020176204,0.00081089785,0.0011005936,0.0010439368,0.0015196421,0.002584962,0.001452564,0.00024021992],"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":[0.0004935838,0.0006034921,0.040960953,0.00012522828,0.00019716342,0.00027368992,0.0005763221,0.66314447,0.0035802966,0.012310074,0.0023590587,0.27537563],"study_design_scores_gemma":[0.00001267721,0.00005920074,0.0019822288,0.000007821368,0.000010858159,0.000023613848,0.00006679302,0.985288,0.0009274324,0.011345927,0.00026765073,0.000007813152],"about_ca_topic_score_codex":0.0069157095,"about_ca_topic_score_gemma":0.0033467172,"teacher_disagreement_score":0.0069157095,"about_ca_system_score_codex":0.0017329693,"about_ca_system_score_gemma":0.0015186013,"threshold_uncertainty_score":0.02764666},"labels":[],"label_agreement":null},{"id":"W4414552918","doi":"10.1093/jamia/ocaf133","title":"Towards responsible artificial intelligence in healthcare—getting real about real-world data and evidence","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Homewood Research Institute; University of Victoria","funders":"","keywords":"Trustworthiness; Foundation (evidence); Health care; Healthcare industry; Health professionals; Meaningful use","score_opus":0.14646092572584002,"score_gpt":0.48262665452430453,"score_spread":0.3361657287984645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414552918","genre_codex":"commentary","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":[0.004219196,0.01281406,0.14748932,0.81526285,0.0023674574,0.0009585612,0.00012468362,0.00028095316,0.016482834],"genre_scores_gemma":[0.22677158,0.02757354,0.5582319,0.17437147,0.0035531048,0.003548405,0.000546967,0.00045962224,0.004943368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.42017195,0.4801231,0.0335389,0.014666797,0.044813227,0.00668604],"domain_scores_gemma":[0.2081419,0.6491752,0.022896383,0.05438789,0.048689485,0.016709149],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.52692974,0.00134544,0.0028029294,0.010034263,0.010900955,0.04631426,0.009424352,0.022017037,0.0047033164],"category_scores_gemma":[0.4931992,0.002427661,0.0030018575,0.0060497755,0.06496612,0.04768012,0.044483505,0.037470642,0.0030710264],"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":[0.00013974782,0.00036481748,0.005228354,0.007813278,0.00043500093,0.0008978143,0.11148575,0.0025006877,0.0017679948,0.5164905,0.0937684,0.25910768],"study_design_scores_gemma":[0.00007492747,0.00010020067,0.0014317128,0.016214514,0.00009929488,0.0005228808,0.032658458,0.0020536745,0.0012909302,0.6484638,0.29688713,0.00020246612],"about_ca_topic_score_codex":0.004589546,"about_ca_topic_score_gemma":0.0041289185,"teacher_disagreement_score":0.47307026,"about_ca_system_score_codex":0.016292281,"about_ca_system_score_gemma":0.11497347,"threshold_uncertainty_score":0.58337986},"labels":[],"label_agreement":null},{"id":"W4415063342","doi":"10.1093/jamia/ocaf169","title":"Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Public Health; Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft","keywords":"Synthetic data; Medical research; Data collection; Big data; Data modeling","score_opus":0.06764900475797032,"score_gpt":0.3819244041408982,"score_spread":0.31427539938292787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415063342","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":[0.7614848,0.0014931964,0.22733575,0.0031404418,0.0002780028,0.00078524253,0.0021581405,0.00086116814,0.002463189],"genre_scores_gemma":[0.9163381,0.00029653354,0.07900954,0.0006431574,0.000060972263,0.00029501852,0.0027925072,0.00013151873,0.0004326648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98289895,0.01338901,0.00062735163,0.0015356746,0.0013060948,0.00024299246],"domain_scores_gemma":[0.8412708,0.13367215,0.004032294,0.01719224,0.002911385,0.00092121307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028669637,0.0009111319,0.00072300964,0.0007179716,0.00050173997,0.0025047173,0.0011183156,0.0013460206,0.0011889655],"category_scores_gemma":[0.14269248,0.00050304085,0.0013738412,0.000684337,0.0016725424,0.0025538914,0.0025928335,0.0021981532,0.0003605335],"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":[0.0046160356,0.0010476083,0.14478166,0.0011978094,0.0013215676,0.00063777907,0.0017121735,0.61232495,0.010253465,0.014034882,0.0051326007,0.2029395],"study_design_scores_gemma":[0.00037715663,0.0026147522,0.027821524,0.0005168973,0.00050619495,0.0008666089,0.0008796896,0.9034779,0.018155655,0.037804175,0.00683774,0.0001416797],"about_ca_topic_score_codex":0.0018065417,"about_ca_topic_score_gemma":0.0016786781,"teacher_disagreement_score":0.028669637,"about_ca_system_score_codex":0.0009639381,"about_ca_system_score_gemma":0.0012189809,"threshold_uncertainty_score":0.15162134},"labels":[],"label_agreement":null},{"id":"W4415126726","doi":"10.1093/jamia/ocaf165","title":"FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Universität Duisburg-Essen; Universitätsklinikum Essen","keywords":"Interoperability; Standardization; Bridging (networking); Health care; Resource (disambiguation); Semantic interoperability","score_opus":0.013947233243733873,"score_gpt":0.35549602647411577,"score_spread":0.3415487932303819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415126726","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":[0.03223981,0.0013233027,0.73714495,0.0042140167,0.00036383435,0.0006312531,0.023186594,0.19567232,0.005223903],"genre_scores_gemma":[0.34679797,0.00081711594,0.57647634,0.0034732611,0.00028063406,0.0010468309,0.0608133,0.0062009646,0.0040935962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612254,0.0013739272,0.00034102376,0.0011652677,0.00073873374,0.00025848486],"domain_scores_gemma":[0.99285126,0.0039597275,0.00042650814,0.0015777884,0.00083944754,0.00034528354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073324214,0.0025718259,0.001179543,0.002127941,0.00068647857,0.0027547665,0.0037294289,0.0013801417,0.0052652857],"category_scores_gemma":[0.023990283,0.0010285004,0.0030279486,0.0011037556,0.00072657166,0.004084594,0.005282569,0.0027023403,0.0041554742],"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":[0.002314953,0.0011629562,0.03913712,0.0013331147,0.0013520732,0.0012580847,0.0016088272,0.21977872,0.012993103,0.018470198,0.15286055,0.54773027],"study_design_scores_gemma":[0.00023275802,0.00028376625,0.003350306,0.00017645379,0.0001921493,0.0002983876,0.000204032,0.9248019,0.011444439,0.030614138,0.028208511,0.00019320974],"about_ca_topic_score_codex":0.021909002,"about_ca_topic_score_gemma":0.024188861,"teacher_disagreement_score":0.021909002,"about_ca_system_score_codex":0.0017482984,"about_ca_system_score_gemma":0.004948648,"threshold_uncertainty_score":0.04356295},"labels":[],"label_agreement":null},{"id":"W4415544440","doi":"10.1093/jamia/ocaf184","title":"An exploratory analysis of SNOMED CT national editions","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"National Institutes of Health","keywords":"SNOMED CT; Consistency (knowledge bases); Exploratory analysis; Systematized Nomenclature of Medicine; Extension (predicate logic)","score_opus":0.008706236505678578,"score_gpt":0.30931860472644457,"score_spread":0.300612368220766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415544440","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":[0.8666426,0.0021141414,0.042084444,0.0013890584,0.00013264985,0.00207621,0.052471604,0.0014005441,0.031688713],"genre_scores_gemma":[0.84211135,0.0009467181,0.11046649,0.00044668117,0.00005787398,0.0025294533,0.03951261,0.0008718412,0.0030568962],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9850086,0.0062715947,0.0024373021,0.0015256567,0.004233189,0.00052363414],"domain_scores_gemma":[0.91949356,0.05157042,0.0064212536,0.0058188667,0.015998734,0.00069707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0200039,0.00047489817,0.00048854586,0.01626792,0.0013690209,0.0029065595,0.0009080111,0.00048261645,0.0040831883],"category_scores_gemma":[0.07783797,0.00035399903,0.0009872712,0.018645223,0.0015941915,0.0033135943,0.004057545,0.0007158775,0.0006391868],"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":[0.0022569178,0.00041174793,0.2964953,0.009513615,0.00066360505,0.0060542626,0.17436767,0.0054664416,0.02170954,0.042217646,0.038861,0.40198228],"study_design_scores_gemma":[0.00013503744,0.00058603013,0.43743524,0.005359256,0.0010175076,0.006974818,0.11904961,0.017817296,0.02341118,0.018792639,0.3689059,0.0005154914],"about_ca_topic_score_codex":0.008052312,"about_ca_topic_score_gemma":0.011849446,"teacher_disagreement_score":0.0200039,"about_ca_system_score_codex":0.003174191,"about_ca_system_score_gemma":0.0047339285,"threshold_uncertainty_score":0.105791986},"labels":[],"label_agreement":null},{"id":"W4417088100","doi":"10.1093/jamia/ocaf218","title":"Patient attitudes toward ambient artificial intelligence scribes in clinical care: insights from a cross-sectional study","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Psychological intervention; Patient privacy; Ambient intelligence; MEDLINE; Digital health; mHealth; Patient data","score_opus":0.10086094396102872,"score_gpt":0.46547128028444335,"score_spread":0.3646103363234146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417088100","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":[0.9991092,0.00011993516,0.000054875556,0.00015471125,0.0000026386779,0.000013213134,0.00012262326,0.0000011720524,0.00042163843],"genre_scores_gemma":[0.99944204,0.00014727315,0.00009458762,0.00014799669,0.000004944895,0.000015138177,0.00008980855,9.663255e-7,0.000057229696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843365,0.0007040803,0.00019568708,0.00014769348,0.00033444498,0.00018440728],"domain_scores_gemma":[0.9916551,0.0021594798,0.0038532075,0.0002304728,0.0011637348,0.00093804096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024449853,0.0001041861,0.00022717113,0.00046382117,0.00058349426,0.0009291884,0.00021069938,0.0003981258,0.0014403625],"category_scores_gemma":[0.009158097,0.00021470219,0.0002676984,0.001029494,0.00041531696,0.00064896734,0.00055378163,0.0006927001,0.00018628614],"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":[0.000025239218,0.000063260755,0.9963665,0.000015502244,0.000017868088,0.000031642103,0.0016739542,0.000009890223,0.000041918094,0.000012488077,0.00013065837,0.0016111361],"study_design_scores_gemma":[0.0000043030964,0.00017927578,0.9936865,0.00003001026,0.000015413789,0.00014865065,0.005354333,0.0000995648,0.000030256972,0.000013874717,0.0004316438,0.000006124175],"about_ca_topic_score_codex":0.018282233,"about_ca_topic_score_gemma":0.024324106,"teacher_disagreement_score":0.018282233,"about_ca_system_score_codex":0.00069440075,"about_ca_system_score_gemma":0.0011753573,"threshold_uncertainty_score":0.03635162},"labels":[],"label_agreement":null},{"id":"W7117158505","doi":"10.1093/jamia/ocaf223","title":"AutoReporter: development of an artificial intelligence tool for automated assessment of research reporting guideline adherence","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Princess Margaret Cancer Centre; BC Cancer Agency; McMaster University; University of Toronto","funders":"","keywords":"Guideline; Quality (philosophy); Control (management); MEDLINE; Scalability","score_opus":0.642779943123821,"score_gpt":0.6432471026700052,"score_spread":0.00046715954618414823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117158505","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01339352,0.002090019,0.4119081,0.002868095,0.00058159546,0.0028637794,0.056729153,0.5048717,0.004694122],"genre_scores_gemma":[0.058122557,0.0007621625,0.8485936,0.0020657175,0.00014539954,0.0038039153,0.074984916,0.0082010785,0.0033206306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97794664,0.011545113,0.0037036487,0.0032246038,0.0032726503,0.0003073481],"domain_scores_gemma":[0.9234172,0.05109124,0.0074949875,0.00875274,0.00829955,0.00094420934],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026170863,0.002880474,0.001537625,0.0048858137,0.000709425,0.004329004,0.003967432,0.002052031,0.015168954],"category_scores_gemma":[0.12515776,0.0015787542,0.0027989207,0.0021061762,0.00062845985,0.004028573,0.0046563363,0.0029326696,0.01160289],"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":[0.0017033904,0.00053127494,0.011277895,0.011487145,0.0011438968,0.0005998574,0.0022972086,0.01904839,0.017369287,0.008739847,0.37187946,0.55392236],"study_design_scores_gemma":[0.0018070394,0.0010894487,0.009883233,0.004138631,0.00090774393,0.0010321722,0.0011757531,0.50625724,0.061349276,0.04175878,0.3699294,0.0006713271],"about_ca_topic_score_codex":0.0049548717,"about_ca_topic_score_gemma":0.010983918,"teacher_disagreement_score":0.97382915,"about_ca_system_score_codex":0.0021712696,"about_ca_system_score_gemma":0.007400162,"threshold_uncertainty_score":0.1384064},"labels":[],"label_agreement":null},{"id":"W7118095914","doi":"10.1093/jamia/ocaf209","title":"Interdisciplinary development and application of computational methods in informatics for clinical applications","year":2025,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Informatics; Health informatics; Translational research informatics; Development (topology); Translational bioinformatics; Materials informatics","score_opus":0.04775753505362975,"score_gpt":0.5657008723203824,"score_spread":0.5179433372667526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118095914","genre_codex":"commentary","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":[0.007954845,0.11395291,0.27138555,0.47326115,0.09375077,0.0007560956,0.00019993186,0.0010108437,0.037727904],"genre_scores_gemma":[0.118330255,0.1566078,0.4177188,0.10107341,0.165791,0.0020982614,0.0006273183,0.003937138,0.033816043],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9212969,0.052918717,0.0049548154,0.003644025,0.01521012,0.0019754781],"domain_scores_gemma":[0.77143645,0.18015455,0.004855631,0.012411686,0.020503374,0.010638275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08973573,0.00096138916,0.00090761966,0.0064508184,0.0035737897,0.012484554,0.0038003344,0.006387988,0.010745306],"category_scores_gemma":[0.1314009,0.0010317606,0.0016040595,0.0027581258,0.0072796196,0.011188525,0.017274706,0.013733877,0.0031151741],"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":[0.00006213519,0.0001885372,0.0032137856,0.0033672908,0.000117156946,0.00070824753,0.012539254,0.0013385845,0.0019170616,0.14297059,0.27662113,0.5569562],"study_design_scores_gemma":[0.000009936893,0.00007370973,0.0010383558,0.0029276304,0.000020232268,0.0009964945,0.0033985018,0.0016734106,0.00087091164,0.05757085,0.9313735,0.000046541558],"about_ca_topic_score_codex":0.00060990284,"about_ca_topic_score_gemma":0.0013020718,"teacher_disagreement_score":0.08973573,"about_ca_system_score_codex":0.003057164,"about_ca_system_score_gemma":0.013016813,"threshold_uncertainty_score":0.47457355},"labels":[],"label_agreement":null},{"id":"W77869065","doi":"10.1197/jamia.m2716","title":"Protecting Privacy Using k-Anonymity","year":2008,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":334,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Anonymity; Identification (biology); Computer science; Data anonymization; k-anonymity; Metric (unit); Data mining; Information loss; Baseline (sea); Computer security; Information privacy; Information retrieval; Internet privacy; Artificial intelligence; Engineering","score_opus":0.03263991282377736,"score_gpt":0.29466697341217657,"score_spread":0.2620270605883992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W77869065","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":[0.024938626,0.0012178639,0.95365566,0.004204306,0.00017072399,0.00028392748,0.0006965674,0.00062430487,0.0142079685],"genre_scores_gemma":[0.7600242,0.0019421938,0.231316,0.0013271023,0.00040487765,0.0006790333,0.00085295114,0.00014985676,0.0033038221],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95381564,0.026095804,0.0029335895,0.004487801,0.011092404,0.0015749028],"domain_scores_gemma":[0.91442335,0.046008095,0.005994901,0.027046248,0.005696299,0.00083110743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019881958,0.0012078385,0.0014797964,0.002232249,0.0032834697,0.007598696,0.0032428098,0.0030723729,0.0029244479],"category_scores_gemma":[0.05308314,0.00070723007,0.0018414039,0.0041146004,0.0055373646,0.013358184,0.008941102,0.0038462833,0.0012063454],"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":[0.00072302856,0.0002609549,0.008337665,0.0014260414,0.00057218637,0.0008801611,0.0039025305,0.11550569,0.008755053,0.6064165,0.015707552,0.23751257],"study_design_scores_gemma":[0.00008989263,0.00023281468,0.0013905613,0.00043771643,0.00016276693,0.0013175826,0.00087935553,0.11944483,0.012511502,0.8252444,0.038132135,0.00015647814],"about_ca_topic_score_codex":0.0012122628,"about_ca_topic_score_gemma":0.00060525484,"teacher_disagreement_score":0.019881958,"about_ca_system_score_codex":0.0022225091,"about_ca_system_score_gemma":0.0058289827,"threshold_uncertainty_score":0.10514712},"labels":[],"label_agreement":null},{"id":"W77936192","doi":"10.1197/jamia.m2203","title":"Systematically Assessing the Situational Relevance of Electronic Knowledge Resources: A Mixed Methods Study","year":2007,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; McGill University","keywords":"Relevance (law); Context (archaeology); Thematic analysis; Curiosity; Situational ethics; Information needs; Knowledge management; Psychology; Applied psychology; Situation awareness; Qualitative research; Medicine; Computer science; Social psychology","score_opus":0.05912882270335597,"score_gpt":0.548414584962115,"score_spread":0.4892857622587591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W77936192","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":[0.9904261,0.0019917658,0.0026260451,0.0002129068,0.000017151608,0.00362027,0.00022410325,0.000013520389,0.00086817815],"genre_scores_gemma":[0.973566,0.0014120075,0.014452332,0.0002984807,0.000029370121,0.009747318,0.00019712781,0.000012637571,0.00028472432],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.92119336,0.06003648,0.009042468,0.00263643,0.00559777,0.001493425],"domain_scores_gemma":[0.7798761,0.1731938,0.02997269,0.005269688,0.010419908,0.0012678619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06636031,0.0006076159,0.0012801181,0.007346296,0.0022975665,0.0037270605,0.0014813712,0.001016578,0.0015674804],"category_scores_gemma":[0.13720688,0.0008280787,0.0012128478,0.005476965,0.0022963798,0.0032630572,0.0039741867,0.00069851056,0.00020837504],"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":[0.0015210918,0.0019851865,0.40429506,0.012527705,0.000985315,0.0010092882,0.4187328,0.00025670516,0.001988511,0.00085131376,0.0005990389,0.15524802],"study_design_scores_gemma":[0.00056104764,0.00872632,0.34222332,0.0089960275,0.0012531561,0.0011331291,0.62151116,0.0014824737,0.003646318,0.001387354,0.00890037,0.00017940936],"about_ca_topic_score_codex":0.0024087662,"about_ca_topic_score_gemma":0.0072010113,"teacher_disagreement_score":0.06636031,"about_ca_system_score_codex":0.004330028,"about_ca_system_score_gemma":0.00594969,"threshold_uncertainty_score":0.35095108},"labels":[],"label_agreement":null},{"id":"W90011144","doi":"10.1197/jamia.m1930","title":"The Effects of Creating Psychological Ownership on Physicians' Acceptance of Clinical Information Systems","year":2005,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":223,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Construct (python library); Situated; Nomological network; Point (geometry); Knowledge management; Psychology; Information system; Computer science; Artificial intelligence; Structural equation modeling; Political science","score_opus":0.036356179207366654,"score_gpt":0.46524637689087667,"score_spread":0.42889019768351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W90011144","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":[0.9979038,0.000082412866,0.0001500364,0.0004199941,0.000004194461,0.000014757393,0.0000060181574,0.000004282042,0.001414514],"genre_scores_gemma":[0.9997763,0.000025385778,0.00008850804,0.000029733801,0.00000425209,0.000004855836,0.0000031764273,8.087381e-7,0.000066956585],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927765,0.005210365,0.00024333358,0.00022318176,0.00096747343,0.00057915447],"domain_scores_gemma":[0.9042103,0.068154074,0.016956795,0.0019484226,0.0023243208,0.006406065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004749715,0.00020411865,0.00014071661,0.00053590006,0.0005681674,0.0014434844,0.00034158115,0.0006138383,0.004479989],"category_scores_gemma":[0.0498659,0.00020041435,0.00047837984,0.00026623788,0.0017542654,0.00082423864,0.0017179381,0.0010633538,0.0001385534],"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":[0.001123552,0.0028959464,0.92357,0.00013791959,0.00021862303,0.00027538117,0.008408087,0.00030054423,0.0024658504,0.00091304374,0.00028490848,0.05940605],"study_design_scores_gemma":[0.000050549486,0.0010878548,0.99304754,0.000044291446,0.000060564522,0.00019310963,0.003685959,0.0005774312,0.00053028506,0.0002345913,0.0004759364,0.00001189255],"about_ca_topic_score_codex":0.0012598982,"about_ca_topic_score_gemma":0.0013449525,"teacher_disagreement_score":0.004749715,"about_ca_system_score_codex":0.00064380065,"about_ca_system_score_gemma":0.0012614302,"threshold_uncertainty_score":0.025119185},"labels":[],"label_agreement":null}]}