{"meta":{"query_hash":"b0a613938d28","filters":{"venue":"Journal of Healthcare Engineering"},"cohort_total":61,"direct_labels_cover":0,"predictions_cover":61,"exported":61,"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/b0a613938d28","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Healthcare+Engineering"},"results":[{"id":"W2008842088","doi":"10.1260/2040-2295.4.1.127","title":"The Effects of Interruptions on Oncologists′ Patient Assessment and Medication Ordering Practices","year":2013,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Patient Safety and Medication Errors","field":"Health Professions","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":"CancerCare Manitoba; University of Toronto; University Health Network","funders":"","keywords":"Medicine; MEDLINE; Medical physics; Intensive care medicine","score_opus":0.04795655640143889,"score_gpt":0.45048013297945244,"score_spread":0.4025235765780135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008842088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966,0.00027482098,0.00027634343,0.0005579462,0.000011450404,0.0000370707,0.000135494,0.00001753044,0.002089393],"genre_scores_gemma":[0.9987135,0.0002206965,0.0005620648,0.00011135142,0.000010089079,0.000027079719,0.00006781693,0.0000031178301,0.00028418045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99303776,0.0030102308,0.00056687085,0.0003889585,0.002269335,0.0007268534],"domain_scores_gemma":[0.92405957,0.039822455,0.023051731,0.0021607275,0.0066652815,0.004240298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004421748,0.00021865008,0.0002591449,0.00083210884,0.0014605131,0.0012203744,0.0006702547,0.00049240503,0.001890809],"category_scores_gemma":[0.058048446,0.00029453816,0.0004329504,0.00066158397,0.00078369334,0.00046313723,0.0010381899,0.0011652658,0.00019246188],"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":[0.0003148542,0.0003514815,0.94903415,0.00010433044,0.00010387028,0.00015713755,0.01584935,0.00034578747,0.0006444547,0.000079956466,0.00064176373,0.03237276],"study_design_scores_gemma":[0.00000908965,0.0002093241,0.9919829,0.00004111785,0.000021588587,0.00005591883,0.0065238974,0.0002401994,0.0001500401,0.00004959987,0.00070226117,0.000014060668],"about_ca_topic_score_codex":0.10632068,"about_ca_topic_score_gemma":0.14269872,"teacher_disagreement_score":0.10632068,"about_ca_system_score_codex":0.003738607,"about_ca_system_score_gemma":0.0060149855,"threshold_uncertainty_score":0.21140367},"labels":[],"label_agreement":null},{"id":"W2015795659","doi":"10.1260/2040-2295.3.3.455","title":"Development of a Laminar Flow Bioreactor by Computational Fluid Dynamics","year":2012,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Fluid dynamics and aerodynamics studies","field":"Engineering","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; University Health Network","funders":"Leibniz-Gemeinschaft; Gottfried Wilhelm Leibniz Universität Hannover; Carnegie Mellon University","keywords":"Laminar flow; Computational fluid dynamics; Bioreactor; Fluid dynamics; Dynamics (music); Flow (mathematics); Mechanics; Computer science; Chemistry; Physics","score_opus":0.007143284849254942,"score_gpt":0.21248961317751663,"score_spread":0.2053463283282617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015795659","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":[0.047972642,0.00031381578,0.9479251,0.00014662661,0.00009190898,0.00015415979,0.0000764491,0.0010508475,0.0022684876],"genre_scores_gemma":[0.18062444,0.00034614943,0.81668025,0.000052429874,0.000014812648,0.00034836459,0.00014746648,0.00010644063,0.0016795626],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975675,0.000042144078,0.00002034044,0.00004023925,0.00011905465,0.000021477721],"domain_scores_gemma":[0.99978083,0.00007258376,0.00002848312,0.000025663148,0.00007118408,0.000021330736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007988395,0.0004995229,0.000403031,0.0003399842,0.00048214258,0.00062252564,0.0008022078,0.0005922105,0.0008656518],"category_scores_gemma":[0.00067714875,0.000378576,0.0005664177,0.00015795323,0.00033317425,0.0004510513,0.00060352415,0.0007093729,0.00049307477],"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":[0.00020660808,0.00024671076,0.0019587497,0.0002821973,0.00003551146,0.0001678754,0.00020037865,0.42068195,0.47607592,0.018750293,0.00092824135,0.080465674],"study_design_scores_gemma":[0.00007073485,0.00017383814,0.00032252292,0.000032736032,0.000019115878,0.00007887439,0.000014931253,0.85714096,0.13101025,0.0014394909,0.009662534,0.000034014876],"about_ca_topic_score_codex":0.002004,"about_ca_topic_score_gemma":0.0013313999,"teacher_disagreement_score":0.002004,"about_ca_system_score_codex":0.0005927498,"about_ca_system_score_gemma":0.0017830178,"threshold_uncertainty_score":0.004300773},"labels":[],"label_agreement":null},{"id":"W2106896520","doi":"10.1260/2040-2295.1.2.169","title":"Low‐Power Implantable Device for Onset Detection and Subsequent Treatment of Epileptic Seizures: A Review","year":2010,"lang":"en","type":"review","venue":"Journal of Healthcare Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Epilepsy; Ictal; Electroencephalography; Medicine; Neuroscience; Stimulation; Psychology","score_opus":0.06863310646638748,"score_gpt":0.35926408415248484,"score_spread":0.29063097768609736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106896520","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":[0.0006176335,0.9950943,0.0012120843,0.00010889488,0.00015095375,0.000019648756,0.000024060755,0.000021628974,0.002750807],"genre_scores_gemma":[0.0027078802,0.9930057,0.001859959,0.00011950321,0.00013072643,0.000029787978,0.00005266831,0.0000058865926,0.002087864],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99985814,0.000018729439,0.00002430329,0.000026624342,0.00006181473,0.000010328681],"domain_scores_gemma":[0.9997644,0.00012834152,0.00003126218,0.000007472575,0.000055025666,0.000013476507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037829188,0.00084102794,0.001159138,0.0017517037,0.0001807392,0.00055437477,0.0009057205,0.0008765958,0.0044090813],"category_scores_gemma":[0.00047522265,0.00025011998,0.00043231176,0.0013822543,0.0002672139,0.00090005895,0.00024460215,0.00059074303,0.0034062136],"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":[0.00005491971,0.0001216254,0.00015778527,0.010797283,0.000051411203,0.00027144054,0.00004468532,0.00046511623,0.00549837,0.0018137002,0.008919315,0.9718043],"study_design_scores_gemma":[0.000043891014,0.00046282986,0.0018607734,0.003986692,0.00019284441,0.0051747113,0.00009437259,0.00055763556,0.006125553,0.0020828675,0.9793743,0.00004353662],"about_ca_topic_score_codex":0.00057280634,"about_ca_topic_score_gemma":0.0007799255,"teacher_disagreement_score":0.0044090813,"about_ca_system_score_codex":0.00025945492,"about_ca_system_score_gemma":0.00052411953,"threshold_uncertainty_score":0.014749825},"labels":[],"label_agreement":null},{"id":"W2114202723","doi":"10.1260/2040-2295.5.3.347","title":"Discrete Event Simulation of Patient Admissions to a Neurovascular Unit","year":2014,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":9,"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; University Health Network","funders":"Krembil Foundation","keywords":"Neurovascular bundle; Medicine; Neurology; Stroke (engine); Neurosurgery; Unit (ring theory); Emergency medicine; Medical emergency; Discrete event simulation; Surgery; Simulation; Computer science; Psychology; Engineering; Psychiatry; Mechanical engineering","score_opus":0.03161063535752166,"score_gpt":0.3901190682712001,"score_spread":0.3585084329136784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114202723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94878995,0.00013653324,0.037394736,0.0009464379,0.000146227,0.00024082193,0.002958184,0.0002745697,0.009112444],"genre_scores_gemma":[0.99089956,0.00006554863,0.0063559082,0.00006573047,0.000013594761,0.0001511101,0.00093460694,0.000010156771,0.0015037672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992499,0.00031912472,0.000041931333,0.00011014707,0.0000999627,0.00017900574],"domain_scores_gemma":[0.99046785,0.0073080584,0.0005774832,0.00024145185,0.00073918654,0.0006659843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014687399,0.0007860958,0.0008334181,0.00066102366,0.00048869906,0.0012153989,0.0013127801,0.0015769296,0.004568132],"category_scores_gemma":[0.006557574,0.0005149269,0.0009587648,0.00076792913,0.0007762116,0.0004708174,0.00087787636,0.0016910263,0.00029156124],"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":[0.00012448824,0.00007025481,0.0026585434,0.000010904845,0.000016854434,0.000037819802,0.000021963928,0.99553114,0.000072878785,0.000837657,0.0001684927,0.00044893517],"study_design_scores_gemma":[0.00004336756,0.00004293362,0.0005188707,0.000002432404,0.00000542177,0.0000047492717,0.000029485283,0.99880815,0.00006483778,0.00036479798,0.000109418936,0.0000055317],"about_ca_topic_score_codex":0.057384454,"about_ca_topic_score_gemma":0.025533421,"teacher_disagreement_score":0.057384454,"about_ca_system_score_codex":0.0018366554,"about_ca_system_score_gemma":0.002287342,"threshold_uncertainty_score":0.11410087},"labels":[],"label_agreement":null},{"id":"W2116094367","doi":"10.1260/2040-2295.3.2.323","title":"Requirements for Interoperability in Healthcare Information Systems","year":2012,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","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":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interoperability; Health care; Information system; Computer science; Healthcare system; Data science; World Wide Web; Knowledge management; Engineering; Political science","score_opus":0.032341849497346184,"score_gpt":0.2719280891384669,"score_spread":0.2395862396411207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116094367","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":[0.059183873,0.002463318,0.76250577,0.04448898,0.000441433,0.0016436583,0.0006263828,0.0007086633,0.12793793],"genre_scores_gemma":[0.6587944,0.0028124417,0.3217383,0.0044258893,0.00063524273,0.0024293608,0.0014085346,0.00029131805,0.007464484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9323869,0.025175253,0.013302954,0.0027292934,0.023141554,0.0032639988],"domain_scores_gemma":[0.86286926,0.0936071,0.007415537,0.013037878,0.02095115,0.0021190403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034509808,0.0007994153,0.0011901826,0.0023638401,0.0028216143,0.009528334,0.002026582,0.006426653,0.0027773278],"category_scores_gemma":[0.11517953,0.0012478752,0.0014370395,0.0024227663,0.0068239775,0.013200355,0.006481965,0.00472882,0.0016629377],"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":[0.00005271793,0.00009955261,0.0016243733,0.00043246287,0.000037842674,0.0008686195,0.0034076814,0.0059680273,0.002844322,0.96476847,0.0024944711,0.017401414],"study_design_scores_gemma":[0.00009958055,0.00017062265,0.0019661747,0.0009898571,0.000070644375,0.0027374027,0.0037656273,0.029256096,0.004659666,0.8525884,0.103583775,0.0001121692],"about_ca_topic_score_codex":0.0030446476,"about_ca_topic_score_gemma":0.0009520507,"teacher_disagreement_score":0.034509808,"about_ca_system_score_codex":0.002682474,"about_ca_system_score_gemma":0.0062222243,"threshold_uncertainty_score":0.18250751},"labels":[],"label_agreement":null},{"id":"W2129830058","doi":"10.1260/2040-2295.4.4.555","title":"Measurement of Lower Limb Joint Kinematics using Inertial Sensors During Stair Ascent and Descent in Healthy Older Adults and Stroke Survivors","year":2013,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Physical medicine and rehabilitation; Kinematics; Descent (aeronautics); Stroke (engine); Medicine; Chronic stroke; Joint (building); Lower limb; Physical therapy; Rehabilitation; Surgery; Engineering","score_opus":0.016361754992721765,"score_gpt":0.25344831301719845,"score_spread":0.23708655802447667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129830058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99937457,0.000046904996,0.0004070638,0.000007450615,0.0000022198979,0.000009542124,0.000044064425,0.0000042698016,0.000103878854],"genre_scores_gemma":[0.99914217,0.00006052086,0.0005768718,0.000015682197,0.000005293824,0.000018254717,0.00009543376,9.022613e-7,0.00008485658],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997764,0.00007731111,0.000029836292,0.00003216481,0.000059967213,0.00002438202],"domain_scores_gemma":[0.9995621,0.00010494317,0.00011106587,0.000025453568,0.00014139975,0.000055064083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032902628,0.00025299905,0.00026335253,0.0004657404,0.00017720852,0.00024119415,0.0001023956,0.00026012445,0.00032094936],"category_scores_gemma":[0.0022727696,0.00012780877,0.00015515789,0.00026580493,0.00013964887,0.00021998379,0.0002368664,0.00012154223,0.00013760672],"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":[0.0004361554,0.00013256953,0.9710362,0.000040339604,0.000059417707,0.000101961086,0.00059895637,0.00019820943,0.0089166565,0.000014226942,0.00008057276,0.018384725],"study_design_scores_gemma":[0.000010755419,0.0007189566,0.9973694,0.000005210558,0.000027738339,0.00016866783,0.00028367108,0.00048147343,0.00081277237,0.000015504282,0.00010144261,0.0000043032323],"about_ca_topic_score_codex":0.0025458713,"about_ca_topic_score_gemma":0.00459777,"teacher_disagreement_score":0.0025458713,"about_ca_system_score_codex":0.00007625918,"about_ca_system_score_gemma":0.0001445333,"threshold_uncertainty_score":0.005062163},"labels":[],"label_agreement":null},{"id":"W2206143544","doi":"10.1260/2040-2295.6.4.635","title":"Healthcare Engineering Defined: A White Paper","year":2015,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":36,"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; Polytechnique Montréal; University Health Network","funders":"","keywords":"Health care; White paper; White (mutation); Engineering; Engineering management; Computer science; Data science; Medicine; Political science","score_opus":0.017160696164258162,"score_gpt":0.23797577114569313,"score_spread":0.22081507498143496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206143544","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":[0.0021030798,0.028956758,0.045332648,0.15772021,0.060617503,0.00033517275,0.0012728616,0.0004910678,0.7031707],"genre_scores_gemma":[0.041584197,0.043353498,0.041897357,0.103203274,0.029302582,0.000668901,0.0027562962,0.0014493114,0.7357846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9889626,0.0023076676,0.0011772141,0.001742773,0.004974155,0.0008356129],"domain_scores_gemma":[0.98745245,0.0032514003,0.00074928615,0.0010466476,0.0059184055,0.0015818662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010818367,0.0009388904,0.00070558774,0.0029085807,0.003920743,0.015584548,0.0017959235,0.0062872614,0.021615757],"category_scores_gemma":[0.013331375,0.0005574926,0.00094586593,0.003359686,0.0061012157,0.009722744,0.0069872793,0.007967239,0.014799716],"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":[0.00001365771,0.00003630487,0.00015423883,0.00020443441,0.000006031264,0.0001542309,0.0005511198,0.00019140712,0.00033464967,0.6068754,0.34348157,0.047996916],"study_design_scores_gemma":[0.0000015132716,0.000005694288,0.00008020877,0.00022604912,0.0000017051434,0.0000618225,0.00010987606,0.00004588058,0.0001344372,0.012859235,0.9864673,0.000006285259],"about_ca_topic_score_codex":0.006083473,"about_ca_topic_score_gemma":0.005251565,"teacher_disagreement_score":0.021615757,"about_ca_system_score_codex":0.0053635733,"about_ca_system_score_gemma":0.01331999,"threshold_uncertainty_score":0.07231188},"labels":[],"label_agreement":null},{"id":"W2209218544","doi":"10.1260/2040-2295.6.4.673","title":"Predicting Neck Fluid Accumulation While Supine","year":2015,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Obstructive Sleep Apnea Research","field":"Medicine","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":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Supine position; Bioelectrical impedance analysis; Medicine; Anthropometry; Body fluid; Demographics; Internal medicine; Body mass index","score_opus":0.07737827933655259,"score_gpt":0.3591947275756332,"score_spread":0.2818164482390806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209218544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967083,0.00008636836,0.0025308214,0.00003053304,0.0000067898413,0.000016992186,0.00030083602,0.000031048665,0.00028831602],"genre_scores_gemma":[0.9986432,0.00005404988,0.0007962993,0.000006991396,0.0000034284824,0.000009783627,0.00036853986,0.0000017369931,0.00011578829],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9999081,0.000024945517,0.000007792947,0.00002408613,0.000018582452,0.000016564829],"domain_scores_gemma":[0.99972516,0.000103883795,0.00007850397,0.000013419448,0.000037348298,0.000041728665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000178922,0.00048614718,0.0002561544,0.0003506881,0.00010478161,0.0005018038,0.00018339403,0.00031484774,0.0005776299],"category_scores_gemma":[0.0013150783,0.0001478551,0.00028541344,0.00015106268,0.00008612351,0.00023371674,0.00024193624,0.00018656754,0.00021382495],"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":[0.00034627173,0.0000793466,0.98121566,0.000024631445,0.0000519133,0.00013244431,0.000067774185,0.0057137045,0.0044174935,0.000026558577,0.00012425864,0.007800073],"study_design_scores_gemma":[0.00001126694,0.00053997134,0.9233753,0.000016997556,0.000051572675,0.00031284182,0.0002678091,0.07337766,0.0016963824,0.00009495587,0.00024039934,0.000014873738],"about_ca_topic_score_codex":0.008390746,"about_ca_topic_score_gemma":0.008002669,"teacher_disagreement_score":0.008390746,"about_ca_system_score_codex":0.00017394057,"about_ca_system_score_gemma":0.0002783646,"threshold_uncertainty_score":0.016683817},"labels":[],"label_agreement":null},{"id":"W2215448934","doi":"10.1260/2040-2295.6.4.691","title":"The Performance of a Mobile Phone Respiratory Rate Counter Compared to the WHO <i>ARI Timer</i>","year":2015,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","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":"University of British Columbia","funders":"Thrasher Research Fund","keywords":"Timer; Mobile phone; Phone; Computer science; Over-the-counter; Medicine; Telecommunications; Wireless; Pharmacology","score_opus":0.019668590484487252,"score_gpt":0.2501027057281654,"score_spread":0.23043411524367816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215448934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804227,0.0013803862,0.0132543985,0.00021436013,0.00025598897,0.00024600897,0.00074867427,0.0005070864,0.0029705253],"genre_scores_gemma":[0.9822343,0.0003166774,0.015436318,0.0002396574,0.00012808439,0.00018736642,0.00042751513,0.00009203348,0.0009381195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952064,0.0022137407,0.00046103616,0.00093979726,0.0010505716,0.00012849284],"domain_scores_gemma":[0.9777299,0.014016717,0.0032939443,0.0011968524,0.0032769276,0.000485584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053220536,0.0006626409,0.00079700176,0.000706313,0.00018610276,0.0011959363,0.00058688875,0.00092686375,0.0015651978],"category_scores_gemma":[0.035818722,0.00024863987,0.00042674245,0.00043424804,0.00026238686,0.00073063397,0.00046111285,0.00041252156,0.0006287561],"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":[0.061759606,0.0009913614,0.50990766,0.001880548,0.0014503318,0.00040640798,0.0016875629,0.0042076106,0.09409784,0.00061178027,0.0031864755,0.31981283],"study_design_scores_gemma":[0.0010094576,0.04308796,0.8375609,0.0002476074,0.0017377476,0.0023945447,0.0005352049,0.035212286,0.072339624,0.00024203076,0.0054188133,0.0002138523],"about_ca_topic_score_codex":0.0012672003,"about_ca_topic_score_gemma":0.0009329937,"teacher_disagreement_score":0.0053220536,"about_ca_system_score_codex":0.00036645416,"about_ca_system_score_gemma":0.00046031663,"threshold_uncertainty_score":0.028146029},"labels":[],"label_agreement":null},{"id":"W2560328367","doi":"10.1155/2017/4037190","title":"A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":832,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; Concordia University","funders":"Generalitat de Catalunya; Centres de Recerca de Catalunya; Nvidia","keywords":"Benchmark (surveying); Segmentation; Colonoscopy; Artificial intelligence; Computer science; Image segmentation; Colorectal cancer; Computer vision; Medicine; Cancer; Internal medicine","score_opus":0.02163422462313469,"score_gpt":0.3204258572728158,"score_spread":0.2987916326496811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560328367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7283584,0.01445501,0.11776351,0.0022262312,0.0014052446,0.0021370952,0.0934301,0.023913126,0.016311372],"genre_scores_gemma":[0.5136972,0.0030093186,0.1850908,0.0007096498,0.00031041267,0.0006797633,0.28865525,0.0012771229,0.006570393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860364,0.0002534671,0.00016425825,0.0004543199,0.00034477026,0.00017951409],"domain_scores_gemma":[0.998348,0.00047194582,0.00016336054,0.00032971552,0.0005115123,0.00017539907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012341981,0.0020566003,0.0010437652,0.0033475077,0.0007921612,0.0018218914,0.0020064951,0.0022633602,0.0021534879],"category_scores_gemma":[0.004278869,0.00035609424,0.0015261336,0.0023705945,0.00073191436,0.0009616375,0.0014760224,0.0011511396,0.0017424469],"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":[0.0034577223,0.0027526051,0.02441776,0.0051013594,0.0011728918,0.0016640476,0.0004426661,0.14007592,0.082331546,0.0034730898,0.12151697,0.6135934],"study_design_scores_gemma":[0.00042585423,0.0023404076,0.071451634,0.00056500407,0.00047595036,0.004209541,0.00091899367,0.7072659,0.103848346,0.00592739,0.102351055,0.0002198321],"about_ca_topic_score_codex":0.014955975,"about_ca_topic_score_gemma":0.024802467,"teacher_disagreement_score":0.014955975,"about_ca_system_score_codex":0.001403211,"about_ca_system_score_gemma":0.0013594946,"threshold_uncertainty_score":0.02973783},"labels":[],"label_agreement":null},{"id":"W2626513856","doi":"10.1155/2017/5485080","title":"Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":140,"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 Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institutes of Health; Ministry of Science, ICT and Future Planning; National Research Foundation; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; Institute for Information and Communications Technology Promotion; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Extreme learning machine; Magnetic resonance imaging; Pattern recognition (psychology); Disease; Computer science; Machine learning; Medicine; Pathology; Artificial neural network; Radiology","score_opus":0.024609346213918196,"score_gpt":0.29178573886539355,"score_spread":0.26717639265147536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626513856","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":[0.32527584,0.0005765235,0.6718504,0.00033421698,0.000047685862,0.000068647605,0.00014197348,0.00076419435,0.0009404565],"genre_scores_gemma":[0.89425087,0.00018940569,0.10462735,0.0000740413,0.000050006962,0.000040010225,0.00023967249,0.000016042237,0.0005126674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.00012968415,0.000037288533,0.000116571966,0.00010449563,0.000039654315],"domain_scores_gemma":[0.99956185,0.00018175936,0.00007440093,0.000050223978,0.00010257013,0.000029184153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070736726,0.00042656963,0.00065871316,0.0010556314,0.0001873553,0.0005279837,0.00045695467,0.00059294584,0.00034140455],"category_scores_gemma":[0.0020825635,0.0001453659,0.0007191827,0.00043680798,0.00028350443,0.00048775203,0.00041517842,0.0005472059,0.00017291054],"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":[0.00093947054,0.00056435313,0.025602214,0.00015433221,0.00030536452,0.0008355238,0.00018690017,0.17472298,0.046808943,0.0031524224,0.002996192,0.7437313],"study_design_scores_gemma":[0.00000999192,0.00015751182,0.004942189,0.0000082257075,0.000027493154,0.0002932936,0.000020016254,0.9890456,0.0039115623,0.0013312113,0.00023956402,0.000013337515],"about_ca_topic_score_codex":0.0011890035,"about_ca_topic_score_gemma":0.00092627277,"teacher_disagreement_score":0.0011890035,"about_ca_system_score_codex":0.00023164248,"about_ca_system_score_gemma":0.0002786732,"threshold_uncertainty_score":0.0037409663},"labels":[],"label_agreement":null},{"id":"W2649385687","doi":"10.1155/2017/5703216","title":"Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","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":"Lakehead University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Segmentation; Magnetic resonance imaging; Computer science; Image segmentation; High-intensity focused ultrasound; Ultrasound; Ablation; Artificial intelligence; Radiation treatment planning; Computer vision; Radiology; Medical physics; Medicine; Radiation therapy","score_opus":0.036630761181180686,"score_gpt":0.31726338308792984,"score_spread":0.28063262190674915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2649385687","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":[0.0047125136,0.00023405904,0.99310654,0.000052681946,0.000028240813,0.000054255335,0.00004757865,0.0007100806,0.0010540258],"genre_scores_gemma":[0.14340168,0.0004959127,0.8527555,0.00005936195,0.000047896956,0.00016222255,0.00038157206,0.0003471496,0.002348655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994754,0.0000996042,0.00004359797,0.00012069432,0.00021492052,0.00004577018],"domain_scores_gemma":[0.9995622,0.00012845025,0.0000492253,0.000055465116,0.00017966345,0.000024954921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004271454,0.0005504838,0.0004921807,0.0011446691,0.0005907462,0.0009386118,0.00090522825,0.00084781897,0.0033278752],"category_scores_gemma":[0.0010869504,0.00041678327,0.0007600467,0.0011482878,0.00043286599,0.00069110136,0.00055404066,0.000492802,0.0012665258],"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":[0.00034994088,0.000090660695,0.001361717,0.00041993667,0.000096356984,0.00037663343,0.00038386727,0.2250185,0.16050494,0.015316899,0.005837301,0.5902433],"study_design_scores_gemma":[0.000030034767,0.0000924129,0.0013990976,0.000034696175,0.000050307215,0.00041334692,0.000048973565,0.92578137,0.050231595,0.004862386,0.017007302,0.00004850781],"about_ca_topic_score_codex":0.0050594965,"about_ca_topic_score_gemma":0.004098491,"teacher_disagreement_score":0.0050594965,"about_ca_system_score_codex":0.0006609647,"about_ca_system_score_gemma":0.0017347257,"threshold_uncertainty_score":0.011132836},"labels":[],"label_agreement":null},{"id":"W2735649603","doi":"10.1155/2017/5690519","title":"Effects of Two Fatigue Protocols on Impact Forces and Lower Extremity Kinematics during Drop Landings: Implications for Noncontact Anterior Cruciate Ligament Injury","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":27,"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":"Shanghai University of Sport; Shanghai Municipal Education Commission; National Natural Science Foundation of China; St. Francis Xavier University","keywords":"Kinematics; Anterior cruciate ligament; Physical medicine and rehabilitation; Ankle; Sagittal plane; Medicine; Physical therapy; ACL injury; Ankle dorsiflexion; Biomechanics; Simulation; Surgery; Computer science; Physics; Anatomy","score_opus":0.0208440281147647,"score_gpt":0.3945237646384315,"score_spread":0.37367973652366676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735649603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99950755,0.00009389431,0.0002471503,0.000010105541,0.0000036018398,0.00004263324,0.000015506374,0.0000016992935,0.000077901714],"genre_scores_gemma":[0.99861205,0.00013885113,0.0006842912,0.000038405928,0.0000121429675,0.00019769178,0.000095683325,0.0000024722362,0.00021849286],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99981946,0.00005827991,0.00002629453,0.000025140811,0.000035403307,0.000035437257],"domain_scores_gemma":[0.9995009,0.00019572837,0.00011203851,0.000040433883,0.000059298476,0.000091645634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004679229,0.0003547396,0.00034240016,0.00020834942,0.00019367391,0.00017795134,0.0001737922,0.00033364308,0.0011340582],"category_scores_gemma":[0.0012629643,0.00014605283,0.00018580895,0.00010054751,0.00024383588,0.00020261396,0.00024632254,0.00020980887,0.000087597924],"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":[0.049418554,0.010946626,0.090078436,0.0008327491,0.0003339978,0.00060715165,0.0008060881,0.0011463085,0.7644627,0.00006727662,0.00016764317,0.08113258],"study_design_scores_gemma":[0.00062869495,0.104405575,0.84814286,0.000057695837,0.00020203249,0.0003704309,0.0005748861,0.0015037331,0.043586593,0.00007403913,0.00042956765,0.000023899156],"about_ca_topic_score_codex":0.000793509,"about_ca_topic_score_gemma":0.0019094388,"teacher_disagreement_score":0.0011340582,"about_ca_system_score_codex":0.0001513415,"about_ca_system_score_gemma":0.000156542,"threshold_uncertainty_score":0.0037938356},"labels":[],"label_agreement":null},{"id":"W2739968480","doi":"10.1155/2017/6732459","title":"Iterative Learning Impedance for Lower Limb Rehabilitation Robot","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Iterative Learning Control Systems","field":"Engineering","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":"Toronto Metropolitan University","funders":"Beijing Municipal Science and Technology Commission; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Iterative learning control; Robustness (evolution); Impedance control; Computer science; Trajectory; Convergence (economics); Robot; Gait training; Electrical impedance; Iterative method; Artificial intelligence; Rehabilitation; Control theory (sociology); Control (management); Algorithm; Engineering; Physical therapy; Medicine","score_opus":0.008840095551382637,"score_gpt":0.2716538892446295,"score_spread":0.26281379369324687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739968480","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":[0.017760707,0.00030255792,0.97763777,0.00016587599,0.000038032213,0.00005044844,0.000008573631,0.00050242397,0.003533602],"genre_scores_gemma":[0.9094751,0.00026934542,0.08469494,0.00012525429,0.000036299927,0.00019842964,0.000037765898,0.000033265067,0.005129615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966276,0.00006146619,0.00002754008,0.00007789598,0.00012776372,0.000042641896],"domain_scores_gemma":[0.9996774,0.000112160946,0.00006109925,0.000028115746,0.00010096704,0.00002029045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003939243,0.00052197994,0.0005107562,0.00033196784,0.00044247208,0.0006098941,0.00078507507,0.00073591154,0.0016498051],"category_scores_gemma":[0.0012328951,0.00019226818,0.00032781297,0.00029687586,0.00053786894,0.0005149907,0.0008903743,0.0006280898,0.00038910276],"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":[0.00017315547,0.00013228178,0.0016037603,0.00025375778,0.000054997246,0.00021727948,0.00039468848,0.6772324,0.027675353,0.009580779,0.0015485563,0.28113297],"study_design_scores_gemma":[0.000015216399,0.0001066547,0.00026132484,0.000010060409,0.000008290776,0.00006092141,0.000013840916,0.99415404,0.0025772853,0.0017648396,0.0010175704,0.000009985013],"about_ca_topic_score_codex":0.0032230464,"about_ca_topic_score_gemma":0.0017214818,"teacher_disagreement_score":0.0032230464,"about_ca_system_score_codex":0.00045347176,"about_ca_system_score_gemma":0.0006440207,"threshold_uncertainty_score":0.006408572},"labels":[],"label_agreement":null},{"id":"W2749983284","doi":"10.1155/2017/8750506","title":"Twin SVM-Based Classification of Alzheimer’s Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":76,"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 Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; F. Hoffmann-La Roche; Chosun University; National Research Foundation; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Artificial intelligence; Support vector machine; Neuroimaging; Linear discriminant analysis; Random forest; Pattern recognition (psychology); Magnetic resonance imaging; Multivariate statistics; Computer science; Alzheimer's disease; Psychology; Medicine; Machine learning; Disease; Neuroscience; Pathology; Radiology","score_opus":0.1375711454150016,"score_gpt":0.3471021894924606,"score_spread":0.20953104407745898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749983284","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":[0.4196936,0.002315245,0.5721632,0.00067872996,0.0004461477,0.0001478346,0.00062411156,0.001526804,0.0024043305],"genre_scores_gemma":[0.9090097,0.00050735235,0.08715014,0.00010860055,0.00011776209,0.000093312,0.001212951,0.00004097899,0.0017592477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904126,0.00026388987,0.000120160315,0.00023172377,0.00022908965,0.00011375661],"domain_scores_gemma":[0.9988782,0.0004253894,0.00009520059,0.000108576496,0.0004077554,0.000084932435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022050133,0.0007070997,0.0012669446,0.0017679586,0.0003772592,0.001124795,0.00068581157,0.00077705825,0.0007681235],"category_scores_gemma":[0.003739136,0.0002667056,0.0011008128,0.00090112427,0.0002682129,0.0007646836,0.00081608735,0.000995102,0.00051064865],"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":[0.0011227846,0.0005928673,0.03212992,0.00017002384,0.00040858134,0.00027541854,0.00017988795,0.110742256,0.015484429,0.002441975,0.0078761075,0.8285757],"study_design_scores_gemma":[0.000012017848,0.000057315254,0.0027729569,0.000009893969,0.00003236935,0.00007851074,0.000027986072,0.99475265,0.0011421246,0.00072428887,0.00037868082,0.000011261336],"about_ca_topic_score_codex":0.0034450921,"about_ca_topic_score_gemma":0.002323673,"teacher_disagreement_score":0.0034450921,"about_ca_system_score_codex":0.000431989,"about_ca_system_score_gemma":0.0008386048,"threshold_uncertainty_score":0.011661351},"labels":[],"label_agreement":null},{"id":"W2791097056","doi":"10.1155/2018/5190693","title":"Quantitative Approach Based on Wearable Inertial Sensors to Assess and Identify Motion and Errors in Techniques Used during Training of Transfers of Simulated c-Spine-Injured Patients","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Spinal Cord Injury Research","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":"Université du Québec à Trois-Rivières; Institut interdisciplinaire d'innovation technologique; Concordia University; Université de Sherbrooke","funders":"Canadian Institutes of Health Research","keywords":"Wearable computer; Motion (physics); Computer science; Trunk; Inertial measurement unit; Simulation; Physical medicine and rehabilitation; Training (meteorology); Motion capture; Artificial intelligence; Medicine","score_opus":0.08078814408297327,"score_gpt":0.4012569359016602,"score_spread":0.3204687918186869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791097056","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":[0.27884787,0.0009989684,0.7141468,0.00017067303,0.0002049223,0.00038956903,0.000859977,0.0009588632,0.0034223478],"genre_scores_gemma":[0.89408636,0.00039579562,0.10323977,0.000100547055,0.00007251725,0.0003427886,0.0003785819,0.000030857034,0.0013527732],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993563,0.00018256446,0.000045067973,0.00012073773,0.00026849343,0.000026832076],"domain_scores_gemma":[0.999286,0.0002352582,0.00017375594,0.000063491454,0.00020843199,0.000032951626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051194674,0.00083353405,0.00039840274,0.0014702099,0.00014251907,0.00045846406,0.00043081614,0.00058303727,0.0011698946],"category_scores_gemma":[0.0018781,0.00019445793,0.00031541707,0.00071273366,0.00022659873,0.00044551239,0.0005583186,0.00024503283,0.00030967023],"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":[0.0009443271,0.00082978746,0.052044656,0.001433183,0.00031078552,0.00031559615,0.0006315982,0.034660283,0.32975182,0.0013185696,0.0019354614,0.5758239],"study_design_scores_gemma":[0.00018095333,0.005060062,0.25875807,0.00026781656,0.00033604726,0.0018968257,0.0013536637,0.6071748,0.115459435,0.0030008922,0.0062251966,0.00028623888],"about_ca_topic_score_codex":0.0006528672,"about_ca_topic_score_gemma":0.0013877373,"teacher_disagreement_score":0.0014702099,"about_ca_system_score_codex":0.00018357582,"about_ca_system_score_gemma":0.00020718183,"threshold_uncertainty_score":0.0039137006},"labels":[],"label_agreement":null},{"id":"W2794925563","doi":"10.1155/2018/8039075","title":"A Sorting Statistic with Application in Neurological Magnetic Resonance Imaging of Autism","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; St. Francis Xavier University","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; St. Francis Xavier University; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Autism; Magnetic resonance imaging; Statistic; Sorting; Functional magnetic resonance imaging; Computer science; Medicine; Psychology; Neuroscience; Statistics; Psychiatry; Mathematics; Radiology","score_opus":0.0169255996794884,"score_gpt":0.26606537039605666,"score_spread":0.24913977071656826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794925563","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":[0.04784721,0.00073274574,0.9421928,0.0011614782,0.0003019347,0.0009403421,0.001623815,0.0016905831,0.0035090847],"genre_scores_gemma":[0.3003044,0.00037783946,0.6938778,0.0005767711,0.00022383849,0.0019797457,0.0015219753,0.00030351672,0.0008341136],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97887266,0.011972449,0.0021896625,0.0021232544,0.0045316056,0.0003102939],"domain_scores_gemma":[0.7993026,0.17245828,0.009695655,0.0070282393,0.010346086,0.0011691906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0315744,0.00088287215,0.0014544348,0.0073730247,0.0011864579,0.0018858417,0.0018225028,0.0025289068,0.0038170624],"category_scores_gemma":[0.17274119,0.00027130434,0.0014833764,0.0068479246,0.0028603722,0.0026191543,0.0018592505,0.002049255,0.00069317955],"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":[0.0018035162,0.00053455046,0.06830497,0.0014901651,0.0008161632,0.00045467194,0.0012877133,0.047126137,0.007496781,0.13101351,0.021994887,0.717677],"study_design_scores_gemma":[0.0012342337,0.0050680903,0.05370851,0.000696237,0.0007317281,0.0020216594,0.0016818964,0.5145158,0.018649722,0.35885704,0.04217613,0.0006590161],"about_ca_topic_score_codex":0.0011818227,"about_ca_topic_score_gemma":0.0012881223,"teacher_disagreement_score":0.0315744,"about_ca_system_score_codex":0.0013533346,"about_ca_system_score_gemma":0.0036192753,"threshold_uncertainty_score":0.16698337},"labels":[],"label_agreement":null},{"id":"W2797337496","doi":"10.1155/2018/9204949","title":"A Novel Haptic Interactive Approach to Simulation of Surgery Cutting Based on Mesh and Meshless Models","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","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":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; Nanchang University; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Rendering (computer graphics); Bézier curve; Meshfree methods; Haptic technology; Simulation; Artificial intelligence; Finite element method; Mathematics; Engineering; Geometry; Structural engineering","score_opus":0.036229769230734184,"score_gpt":0.2956616122197239,"score_spread":0.25943184298898975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797337496","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":[0.0071522202,0.00008131771,0.9899042,0.00005837254,0.000045277906,0.000033362536,0.00003517763,0.00047402465,0.0022160432],"genre_scores_gemma":[0.4958145,0.00037736914,0.49572548,0.00013160602,0.000066319975,0.00032922273,0.00014526326,0.00023872263,0.0071715927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979335,0.00004288655,0.000011381483,0.000027435144,0.000110642424,0.000014378078],"domain_scores_gemma":[0.9998572,0.000058605434,0.000015556507,0.00002582615,0.000025916304,0.00001683366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025514455,0.0004502795,0.00043267952,0.00043167284,0.00030332428,0.00071197364,0.0012580497,0.0009855725,0.0033062622],"category_scores_gemma":[0.0006028558,0.0002918719,0.000912172,0.00026268576,0.00043304657,0.00071228243,0.0011920048,0.0005689903,0.0003302519],"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":[0.0001232596,0.00007798821,0.0009132714,0.00021184984,0.00007123426,0.00043094714,0.00031824905,0.8458779,0.04330703,0.0556893,0.0013831316,0.05159589],"study_design_scores_gemma":[0.000011431973,0.00002481026,0.00006565478,0.0000052704436,0.000006503885,0.00006801614,0.000008754438,0.99356693,0.0015657234,0.0023787126,0.002289634,0.000008554481],"about_ca_topic_score_codex":0.0015799889,"about_ca_topic_score_gemma":0.0010660726,"teacher_disagreement_score":0.0033062622,"about_ca_system_score_codex":0.00026792768,"about_ca_system_score_gemma":0.00039171413,"threshold_uncertainty_score":0.0110605955},"labels":[],"label_agreement":null},{"id":"W2810459566","doi":"10.1155/2018/6024635","title":"Electrochemotherapy Effectiveness Loss due to Electric Field Indentation between Needle Electrodes: A Numerical Study","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Microbial Inactivation Methods","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"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 Saskatchewan","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Electrochemotherapy; Indentation; Electrode; Electric field; Materials science; Biomedical engineering; Field (mathematics); Composite material; Acoustics; Medicine; Surgery; Mathematics; Physics","score_opus":0.01135561504237035,"score_gpt":0.33989625023596565,"score_spread":0.3285406351935953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810459566","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9071977,0.0013367626,0.061486628,0.0012263284,0.00012373104,0.00014967746,0.0005124388,0.0002500795,0.027716592],"genre_scores_gemma":[0.9874514,0.00027367377,0.009505662,0.00008123589,0.0000128366155,0.000054695884,0.00009049737,0.00002684031,0.0025031727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999835,0.000038179667,0.000011693483,0.000024310497,0.000051282474,0.00003958587],"domain_scores_gemma":[0.9981517,0.0013316335,0.0001836041,0.00007115896,0.00021008558,0.00005176337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045794432,0.00031561227,0.0004957497,0.0006368673,0.00054368936,0.00081886665,0.0005922246,0.0017565733,0.0026989498],"category_scores_gemma":[0.00289285,0.00022281264,0.0005911044,0.0005161748,0.0007325075,0.00047964114,0.0004885426,0.00050963747,0.00019429984],"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":[0.00016612784,0.00015562556,0.004697561,0.00017134934,0.000029048948,0.0006634198,0.0001523578,0.97454256,0.008684473,0.0037419535,0.00071631186,0.006279257],"study_design_scores_gemma":[0.000017558647,0.00005927366,0.0007555424,0.000015930014,0.000012979671,0.00009852604,0.00006824459,0.9967348,0.0013042657,0.00053121196,0.00039181177,0.000009866562],"about_ca_topic_score_codex":0.008434788,"about_ca_topic_score_gemma":0.0041349162,"teacher_disagreement_score":0.008434788,"about_ca_system_score_codex":0.00085195195,"about_ca_system_score_gemma":0.00046368368,"threshold_uncertainty_score":0.016771436},"labels":[],"label_agreement":null},{"id":"W2811195490","doi":"10.1155/2018/7979528","title":"Nonlocal Coherent Denoising of RF Data for Ultrasound Elastography","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Ultrasound Imaging and Elastography","field":"Medicine","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":"McGill University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University","keywords":"Elastography; Noise reduction; Computer science; Noise (video); Displacement (psychology); Speckle noise; Artificial intelligence; Ultrasound; Ultrasound elastography; Speckle pattern; Estimator; Algorithm; Mathematics; Computer vision; Physics; Acoustics; Statistics; Image (mathematics)","score_opus":0.026216100069927927,"score_gpt":0.3123872418674189,"score_spread":0.286171141797491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811195490","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":[0.013998304,0.00031232988,0.9848202,0.000108881,0.000021522705,0.0000149219695,0.000030135907,0.00016412078,0.0005295618],"genre_scores_gemma":[0.24046966,0.00072110887,0.7564682,0.0001751207,0.000053752432,0.0000949065,0.00028128867,0.00017200662,0.0015640225],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958545,0.00011440143,0.00001834192,0.00006675051,0.00019457909,0.000020482059],"domain_scores_gemma":[0.9989378,0.0006602984,0.00009280024,0.000115399715,0.00016659687,0.000027103164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008932137,0.00067392393,0.0005657616,0.00059411913,0.00019411818,0.00043934048,0.00059186574,0.0009781081,0.0011023565],"category_scores_gemma":[0.0034843264,0.00020977545,0.00044492612,0.0006077254,0.00047433513,0.00072979636,0.0007755093,0.0007508393,0.00039707002],"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":[0.00035724603,0.00019566303,0.001912689,0.0004797108,0.00013884211,0.00027437697,0.00021959159,0.34371766,0.27592257,0.016933464,0.003191546,0.35665667],"study_design_scores_gemma":[0.000012039916,0.00006478052,0.0005998219,0.000016754615,0.00001872211,0.000111133275,0.00002125969,0.9534352,0.040736668,0.0031555744,0.0018100567,0.000018059704],"about_ca_topic_score_codex":0.0008041779,"about_ca_topic_score_gemma":0.0017211132,"teacher_disagreement_score":0.0011023565,"about_ca_system_score_codex":0.00026129294,"about_ca_system_score_gemma":0.00040711957,"threshold_uncertainty_score":0.004723847},"labels":[],"label_agreement":null},{"id":"W2897245349","doi":"10.1155/2018/2572730","title":"3D Printed Anatomy-Specific Fixture for Consistent Glenoid Cavity Position in Shoulder Simulator","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Anatomy and Medical Technology","field":"Engineering","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":"Kingston General Hospital; Queen's University; McGill University","funders":"","keywords":"Fixture; 3d printed; Glenoid cavity; Computer science; Position (finance); Anatomy; Orthodontics; Simulation; Medicine; Engineering drawing; Biomedical engineering; Shoulder joint; Engineering; Mechanical engineering","score_opus":0.011919025417117384,"score_gpt":0.27599294997951307,"score_spread":0.2640739245623957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897245349","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":[0.45935625,0.00112038,0.5280998,0.00033070237,0.0004802543,0.00042373242,0.00038542642,0.0023915358,0.0074119046],"genre_scores_gemma":[0.7520472,0.00041193824,0.24250016,0.00011382319,0.000047223482,0.00023368836,0.00024593147,0.00028577552,0.0041142413],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894255,0.0001182873,0.000118740754,0.00012087272,0.0006458116,0.000053746113],"domain_scores_gemma":[0.9987973,0.0003713424,0.00017093118,0.0004272621,0.00017914988,0.00005387623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013251803,0.000892164,0.0002939179,0.0011774601,0.00023087117,0.0005092121,0.001110815,0.0011729185,0.003975937],"category_scores_gemma":[0.0025893522,0.0005719027,0.00068678055,0.00024544622,0.00066860166,0.00046771174,0.00088368764,0.0004954632,0.0013953409],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030880194,0.00021487015,0.0021682084,0.00042336437,0.00006052385,0.0013552705,0.00053879543,0.020877773,0.8784211,0.0013345657,0.0014293407,0.09286736],"study_design_scores_gemma":[0.00030140264,0.0064314036,0.034612622,0.00035395904,0.00039838228,0.022853978,0.00031142574,0.10065945,0.7846297,0.0018473603,0.047258,0.00034232665],"about_ca_topic_score_codex":0.00039570263,"about_ca_topic_score_gemma":0.00060651085,"teacher_disagreement_score":0.003975937,"about_ca_system_score_codex":0.00024631197,"about_ca_system_score_gemma":0.0005920827,"threshold_uncertainty_score":0.0133007765},"labels":[],"label_agreement":null},{"id":"W2900858611","doi":"10.1155/2018/1797502","title":"Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":64,"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":"Peking University","keywords":"Landmark; Cephalometric analysis; Craniofacial; Computer science; Orthodontics; Decision tree; Cephalometry; Radiography; Artificial intelligence; Dentistry; Medicine","score_opus":0.013556589745715959,"score_gpt":0.30057437759432304,"score_spread":0.2870177878486071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900858611","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":[0.076629445,0.00045581622,0.9171675,0.000102583945,0.000069234324,0.00012638659,0.00054711674,0.0034386117,0.0014633819],"genre_scores_gemma":[0.5202215,0.00037611648,0.47395292,0.00007959019,0.00006731452,0.00015372751,0.0026530277,0.00028843048,0.002207438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987779,0.00019601386,0.00008253794,0.00030626584,0.00052221137,0.00011509727],"domain_scores_gemma":[0.9992447,0.00016128118,0.000087012464,0.000116692005,0.00035992035,0.000030427405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091113936,0.0006790189,0.0009769645,0.0018919664,0.00020455604,0.00062176306,0.0009861591,0.00050712825,0.0016412869],"category_scores_gemma":[0.0022647672,0.00027667338,0.0006809303,0.0011768652,0.00017995025,0.0005173792,0.00067091297,0.00041630832,0.0011525535],"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":[0.0003661063,0.00008945242,0.0050534066,0.00010834185,0.00007458158,0.00022375831,0.00005813978,0.023978814,0.10654858,0.0011440256,0.0049820496,0.8573727],"study_design_scores_gemma":[0.000024311192,0.00009858934,0.008450569,0.000012114172,0.00004407166,0.00039840268,0.00005182442,0.9537064,0.0338254,0.00088853703,0.002471615,0.000028140947],"about_ca_topic_score_codex":0.0017130483,"about_ca_topic_score_gemma":0.0028537118,"teacher_disagreement_score":0.0018919664,"about_ca_system_score_codex":0.00022836032,"about_ca_system_score_gemma":0.00040092476,"threshold_uncertainty_score":0.005490601},"labels":[],"label_agreement":null},{"id":"W2902801798","doi":"10.1155/2018/9235023","title":"Force Analysis and Evaluation of a Pelvic Support Walking Robot with Joint Compliance","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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":"Toronto Metropolitan University","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Kinematics; Brace; Physical medicine and rehabilitation; Simulation; Computer science; Pelvis; Robot; Gait; Engineering; Medicine; Artificial intelligence; Physics; Surgery; Structural engineering","score_opus":0.030263336625876523,"score_gpt":0.2870274556233299,"score_spread":0.25676411899745333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902801798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7805248,0.00017286188,0.21508794,0.0001364338,0.0000492653,0.00023216319,0.0001688535,0.0006944235,0.0029334058],"genre_scores_gemma":[0.96984744,0.00009313624,0.02820029,0.000017802284,0.000006307381,0.000089753885,0.00009070455,0.000014808847,0.0016397168],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997768,0.000032266427,0.000014714478,0.000023802822,0.00012689095,0.000025619102],"domain_scores_gemma":[0.99958664,0.00013551631,0.000058252557,0.00004676798,0.0001448678,0.000027946739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003423202,0.000388922,0.00026985505,0.0006191402,0.00022735725,0.00019802629,0.0003763027,0.00036522234,0.0017570801],"category_scores_gemma":[0.0010381398,0.00015376328,0.00022403816,0.00023115377,0.0002625678,0.00025615757,0.0002449562,0.00013990409,0.00020980506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008861914,0.00032609305,0.0062836814,0.0006961104,0.00004084477,0.00087424216,0.00052892376,0.05619309,0.7777062,0.0017005294,0.0008126063,0.15395153],"study_design_scores_gemma":[0.00012433538,0.0035475818,0.040371135,0.000056342873,0.000064427615,0.0007671292,0.00040880538,0.7499668,0.2003438,0.000559909,0.0037233161,0.00006639205],"about_ca_topic_score_codex":0.0015647552,"about_ca_topic_score_gemma":0.001056701,"teacher_disagreement_score":0.0017570801,"about_ca_system_score_codex":0.0001179922,"about_ca_system_score_gemma":0.00029142396,"threshold_uncertainty_score":0.0058780313},"labels":[],"label_agreement":null},{"id":"W2907548131","doi":"10.1155/2018/4323046","title":"Measuring and Training Speech-Language Pathologists’ Orofacial Cueing: A Pilot Demonstration","year":2018,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Action Observation and Synchronization","field":"Psychology","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":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Speech therapy; Psychology; Computer science; Medicine; Audiology","score_opus":0.10247456793863521,"score_gpt":0.3322703820620811,"score_spread":0.22979581412344588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907548131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98637784,0.00014521576,0.011012157,0.00023882373,0.00003442906,0.00059209584,0.00012079371,0.00020540356,0.0012732536],"genre_scores_gemma":[0.9092443,0.00048533428,0.08273591,0.00020790526,0.00004625992,0.0010359705,0.0002446251,0.00006992571,0.005929712],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99924314,0.00023025443,0.000056988818,0.00017058397,0.00017978896,0.00011940671],"domain_scores_gemma":[0.997682,0.001157908,0.00014165237,0.00021668816,0.00032115146,0.00048065293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165004,0.0008279845,0.00039179574,0.00037936177,0.0003926724,0.00039137766,0.00087015313,0.00090509205,0.0035404693],"category_scores_gemma":[0.0029691714,0.00028727253,0.0007299133,0.000113578586,0.0006199447,0.00052441814,0.001195302,0.0010909681,0.00081497873],"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":[0.005739432,0.070039056,0.025318649,0.0015152179,0.00023859434,0.0057968087,0.0127024,0.006019292,0.49698395,0.00075348065,0.0039899745,0.3709032],"study_design_scores_gemma":[0.0027408875,0.37916833,0.21813104,0.00051620236,0.00043728802,0.009928207,0.010302646,0.04103227,0.29696152,0.0018882089,0.03857797,0.0003153773],"about_ca_topic_score_codex":0.0012614441,"about_ca_topic_score_gemma":0.0028061809,"teacher_disagreement_score":0.0035404693,"about_ca_system_score_codex":0.00030698968,"about_ca_system_score_gemma":0.0008802037,"threshold_uncertainty_score":0.011843979},"labels":[],"label_agreement":null},{"id":"W2911963582","doi":"10.1155/2019/9507193","title":"Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening","year":2019,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":15,"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 Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Elan; Northern California Institute for Research and Education; Johns Hopkins University; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Alzheimer's Drug Discovery Foundation; National Institute of Neurological Disorders and Stroke; IXICO; Takeda Pharmaceutical Company; AbbVie; Alzheimer's Association; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Dementia; Segmentation; Neuroimaging; Receiver operating characteristic; Magnetic resonance imaging; Cognitive decline; Cognition; Artificial intelligence; Computer science; Machine learning; Medicine; Radiology; Disease; Pathology; Psychiatry","score_opus":0.013001005793144687,"score_gpt":0.2983763816213805,"score_spread":0.28537537582823586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911963582","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":[0.16230375,0.0023855919,0.70998865,0.0020573912,0.000623275,0.0010674185,0.03028137,0.08621815,0.0050744466],"genre_scores_gemma":[0.68916816,0.0013130755,0.27538115,0.0009395626,0.0003555928,0.00070095644,0.027970036,0.0010280946,0.003143387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951994,0.000044154556,0.0000469158,0.00021686303,0.000106706415,0.00006532145],"domain_scores_gemma":[0.9993205,0.0001418013,0.00011804801,0.0001095324,0.00019774413,0.00011235939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073831424,0.0013507149,0.0014444545,0.0017680494,0.000566378,0.0010778122,0.0016306357,0.0009579759,0.002663911],"category_scores_gemma":[0.002219132,0.0004091655,0.0011170763,0.0012932855,0.0002397056,0.0007884999,0.0013815857,0.00076100597,0.0020914038],"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":[0.0041550747,0.000765997,0.07753175,0.0006460672,0.00065476727,0.0029859375,0.0006284716,0.19604623,0.029601501,0.004303212,0.13875055,0.5439304],"study_design_scores_gemma":[0.00006527576,0.00007433755,0.0074233375,0.00003783994,0.00006441371,0.00024441263,0.000057916008,0.9801429,0.004944787,0.0026403628,0.0042630206,0.000041343126],"about_ca_topic_score_codex":0.021066632,"about_ca_topic_score_gemma":0.024149079,"teacher_disagreement_score":0.021066632,"about_ca_system_score_codex":0.0012803772,"about_ca_system_score_gemma":0.0012056885,"threshold_uncertainty_score":0.04188806},"labels":[],"label_agreement":null},{"id":"W2926858281","doi":"10.1155/2019/2658675","title":"Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar","year":2019,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","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":"Carleton University; University of Ottawa","funders":"Agencia de Innovación y Desarrollo de Andalucía; Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Breathing; Algorithm; Confidence interval; Computer science; Statistics; Standard deviation; SIGNAL (programming language); Artificial intelligence; Mathematics; Telecommunications; Medicine","score_opus":0.017809815587155744,"score_gpt":0.23794347872917007,"score_spread":0.22013366314201432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926858281","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":[0.041544348,0.000389854,0.95703083,0.000042833028,0.000035615067,0.000017547185,0.000040415085,0.00037549462,0.0005230188],"genre_scores_gemma":[0.78338516,0.0005551968,0.2149145,0.000063080406,0.000079410594,0.00007642169,0.00020694965,0.00006724832,0.0006520969],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989421,0.00023365013,0.00006544701,0.00022661831,0.00047715154,0.000054992834],"domain_scores_gemma":[0.9965849,0.0019882987,0.00050843926,0.0003421959,0.000511197,0.00006491026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012933088,0.0005857523,0.00066950294,0.0007089762,0.00016826869,0.0005536115,0.0006620743,0.0007232157,0.00054031896],"category_scores_gemma":[0.008026609,0.0002005866,0.0004524353,0.0005119018,0.00030369608,0.00085935905,0.00071484613,0.00063270645,0.00026397168],"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":[0.0011639534,0.00025765147,0.016334329,0.0005999976,0.00030297445,0.00051219005,0.00028925357,0.21006373,0.11889275,0.007337215,0.0013758701,0.6428701],"study_design_scores_gemma":[0.00002416743,0.00022289371,0.0051453,0.000032117834,0.000047579164,0.0005041031,0.000026228417,0.96314347,0.028654307,0.0013537682,0.0007999559,0.0000460851],"about_ca_topic_score_codex":0.0005317151,"about_ca_topic_score_gemma":0.0003659462,"teacher_disagreement_score":0.0012933088,"about_ca_system_score_codex":0.00015697353,"about_ca_system_score_gemma":0.00029075734,"threshold_uncertainty_score":0.0068396926},"labels":[],"label_agreement":null},{"id":"W2928074773","doi":"10.1155/2019/9156921","title":"Bioprinting of Vascularized Tissue Scaffolds: Influence of Biopolymer, Cells, Growth Factors, and Gene Delivery","year":2019,"lang":"en","type":"review","venue":"Journal of Healthcare Engineering","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":56,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regeneration (biology); Biofabrication; Scaffold; Process (computing); Tissue engineering; Regenerative medicine; Biomedical engineering; Computer science; Biology; Stem cell; Medicine; Cell biology","score_opus":0.027720636943850952,"score_gpt":0.29951253269177364,"score_spread":0.2717918957479227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928074773","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":[0.0010492792,0.996979,0.0005828813,0.0000564517,0.00009672362,0.0000074540662,0.000004961829,0.0000069350776,0.001216405],"genre_scores_gemma":[0.0056176283,0.9923362,0.0007735809,0.00007428441,0.00008280341,0.00001301746,0.000011349245,0.0000031403088,0.0010879167],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976784,0.000033345124,0.000024615118,0.00004899168,0.00009639267,0.000028683295],"domain_scores_gemma":[0.99981135,0.00010420472,0.00003419439,0.000005960954,0.00003411995,0.000010194698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058641145,0.0007682622,0.0010388211,0.0019530831,0.00020667515,0.0007152369,0.0005376058,0.00096504256,0.0009546226],"category_scores_gemma":[0.0003487274,0.00040542614,0.0005113637,0.0012991336,0.00042055818,0.00080496294,0.00037518152,0.0008717746,0.0006377511],"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":[0.000085552645,0.0001508779,0.00027725933,0.029888034,0.00010572919,0.00068347214,0.00019696286,0.0010858942,0.08937279,0.007129436,0.00481966,0.86620426],"study_design_scores_gemma":[0.000028924762,0.0006090853,0.0029005823,0.003996533,0.00024136182,0.005772178,0.00018352842,0.0011830877,0.10483953,0.0022455342,0.8779043,0.00009537696],"about_ca_topic_score_codex":0.0005790096,"about_ca_topic_score_gemma":0.00094816624,"teacher_disagreement_score":0.0019530831,"about_ca_system_score_codex":0.00048373351,"about_ca_system_score_gemma":0.0004139026,"threshold_uncertainty_score":0.00350976},"labels":[],"label_agreement":null},{"id":"W2947045108","doi":"10.1155/2019/8973515","title":"Planning Capacity for Mental Health and Addiction Services in the Emergency Department: A Discrete-Event Simulation Approach","year":2019,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Emergency and Acute Care Studies","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":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Emergency department; Mental health; Medicine; Recreation; Cannabis; Legalization; Harm reduction; Medical emergency; Environmental health; Public health; Psychiatry; Nursing","score_opus":0.022215642512561216,"score_gpt":0.3216564113881076,"score_spread":0.2994407688755464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947045108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81114924,0.00051195873,0.15389691,0.0031576059,0.00021014018,0.00075606833,0.0025309592,0.0004220273,0.02736516],"genre_scores_gemma":[0.9730617,0.00018770905,0.023024768,0.00012455098,0.000030115192,0.00030497403,0.00058886904,0.000023737712,0.0026536155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990804,0.00047742017,0.000034952147,0.00010461906,0.000082364284,0.00022026864],"domain_scores_gemma":[0.99199545,0.0061916127,0.00046368502,0.00010130821,0.00061849825,0.00062949455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018958965,0.0011475262,0.0013077304,0.001188306,0.0010733313,0.0021985976,0.0019539646,0.002349442,0.005011449],"category_scores_gemma":[0.005995525,0.0009868221,0.0014928038,0.0010459557,0.00095391704,0.00091249944,0.0013889518,0.0022060417,0.00020323737],"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":[0.000051907267,0.000042329088,0.0012346236,0.000013308177,0.000020600493,0.00004184356,0.000021897178,0.99676657,0.00004339513,0.0011501956,0.00012508083,0.00048827354],"study_design_scores_gemma":[0.000020543239,0.000020032325,0.00018156657,0.0000038728253,0.00000888585,0.0000031896814,0.000053231168,0.99917895,0.00002564437,0.00038784527,0.00011087294,0.000005410284],"about_ca_topic_score_codex":0.14911436,"about_ca_topic_score_gemma":0.1024926,"teacher_disagreement_score":0.14911436,"about_ca_system_score_codex":0.004586743,"about_ca_system_score_gemma":0.006431713,"threshold_uncertainty_score":0.29649287},"labels":[],"label_agreement":null},{"id":"W3004423570","doi":"10.1155/2020/1506250","title":"Tip-Over Stability Analysis of a Pelvic Support Walking Robot","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Robotic Locomotion and Control","field":"Engineering","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":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Zero moment point; Robot; Moment (physics); Computer science; Statics; Stability (learning theory); Simulation; Gait; Process (computing); Rehabilitation; Physical medicine and rehabilitation; Control theory (sociology); Artificial intelligence; Physical therapy; Humanoid robot; Medicine; Control (management); Physics","score_opus":0.016906322650984556,"score_gpt":0.23361327540341056,"score_spread":0.21670695275242602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004423570","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":[0.38793087,0.00041831014,0.59803796,0.00018260175,0.000028697536,0.0000688776,0.0000794674,0.00043227396,0.01282088],"genre_scores_gemma":[0.99201995,0.000102446,0.0063565476,0.000012476419,0.000003979038,0.000025100277,0.000033152468,0.000016979518,0.0014293016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998776,0.000021261592,0.000005231518,0.000023654977,0.0000529007,0.000019290452],"domain_scores_gemma":[0.99976903,0.00007762052,0.000046541834,0.000012360405,0.00008332154,0.0000111232675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020778917,0.0003577602,0.000301555,0.0006462104,0.00033830456,0.000421465,0.00025722315,0.00036397087,0.0016625143],"category_scores_gemma":[0.000599599,0.00014504622,0.0003442329,0.00020459681,0.00035901167,0.00024146381,0.00030199203,0.00020627314,0.0002185886],"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":[0.00026844355,0.000041084746,0.005069671,0.00017817218,0.000065807944,0.00074159086,0.00023305012,0.86730915,0.070985794,0.006543568,0.0005878888,0.04797585],"study_design_scores_gemma":[0.0000035652786,0.000064751264,0.0013335999,0.0000056803665,0.00000803441,0.00005525512,0.00003435436,0.99501365,0.0026213913,0.0005931722,0.0002598398,0.0000066987654],"about_ca_topic_score_codex":0.0029313203,"about_ca_topic_score_gemma":0.0008836881,"teacher_disagreement_score":0.0029313203,"about_ca_system_score_codex":0.00022447715,"about_ca_system_score_gemma":0.00033974284,"threshold_uncertainty_score":0.0058285},"labels":[],"label_agreement":null},{"id":"W3028134170","doi":"10.1155/2020/9152369","title":"Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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 Saskatchewan","funders":"Pusan National University","keywords":"Finger tapping; Ventrolateral prefrontal cortex; Brain–computer interface; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Functional near-infrared spectroscopy; Computer science; Filter (signal processing); Prefrontal cortex; Psychology; Electroencephalography; Computer vision; Cognition; Neuroscience; Audiology; Medicine","score_opus":0.0491741728576381,"score_gpt":0.3137532600739448,"score_spread":0.2645790872163067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028134170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9702683,0.0007281287,0.027940378,0.00003925999,0.000029603474,0.00008350597,0.00011604961,0.000044191824,0.0007505159],"genre_scores_gemma":[0.9915257,0.00021117854,0.0077078566,0.00003112259,0.000022780196,0.000035435754,0.00011621086,0.000016150543,0.00033356628],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996563,0.000091324044,0.0000229954,0.00008555911,0.00008521272,0.00005858109],"domain_scores_gemma":[0.999086,0.0005725649,0.00005973215,0.00007115075,0.00017793772,0.000032591102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008627027,0.0005298455,0.00037202876,0.00033833855,0.00017720634,0.0003416484,0.00022680536,0.00041220788,0.0004871025],"category_scores_gemma":[0.0042365775,0.00012937927,0.0004067702,0.00021406302,0.00030796055,0.0004271464,0.00020124867,0.0002534643,0.00013157193],"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":[0.006118712,0.0011711314,0.030538308,0.0006891617,0.00039480155,0.0009964793,0.0010739262,0.024342949,0.6808922,0.00075978925,0.0009121484,0.25211033],"study_design_scores_gemma":[0.00024546706,0.003962754,0.40464953,0.00007620171,0.00067302503,0.002558782,0.0008365096,0.37109783,0.21160449,0.002023197,0.0021310751,0.00014120272],"about_ca_topic_score_codex":0.0039662523,"about_ca_topic_score_gemma":0.002883837,"teacher_disagreement_score":0.0039662523,"about_ca_system_score_codex":0.00020594987,"about_ca_system_score_gemma":0.00029005186,"threshold_uncertainty_score":0.007886291},"labels":[],"label_agreement":null},{"id":"W3082458899","doi":"10.1155/2020/3743171","title":"Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":53,"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 Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Eisai; Meso Scale Diagnostics; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation; Northern California Institute for Research and Education; Ministry of Science and ICT, South Korea; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; Korea Brain Research Institute; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Dementia; Neuroimaging; Artificial intelligence; Magnetic resonance imaging; Segmentation; Computer science; Modality (human–computer interaction); Dimensionality reduction; Alzheimer's disease; Pattern recognition (psychology); Medicine; Psychology; Disease; Neuroscience; Pathology; Radiology","score_opus":0.0457663384992802,"score_gpt":0.3400040976523897,"score_spread":0.2942377591531095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082458899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9501255,0.00091879687,0.0045761224,0.00029824532,0.00015993987,0.00047377546,0.0407107,0.0011082796,0.0016286313],"genre_scores_gemma":[0.817364,0.0006889878,0.022793882,0.00018882622,0.00013135737,0.0005253679,0.15651742,0.00006787729,0.0017223966],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937767,0.000082521605,0.00011445491,0.00020419834,0.00012900753,0.000092185925],"domain_scores_gemma":[0.9992606,0.00012683585,0.00009100174,0.0001656915,0.00024512896,0.00011067283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009277995,0.0013301908,0.0008626156,0.0036302512,0.00045892404,0.00077107677,0.00091183285,0.0010835836,0.0011545048],"category_scores_gemma":[0.0018657794,0.00019653332,0.0010470703,0.0014455835,0.00033077967,0.00049081526,0.0009032863,0.00049250806,0.00067265664],"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":[0.008897787,0.004117135,0.43548223,0.0017909415,0.0018534767,0.0038271882,0.0007686061,0.019822678,0.052623745,0.0007958525,0.0609837,0.4090367],"study_design_scores_gemma":[0.00062357506,0.0013552846,0.8212144,0.0002417922,0.0008659225,0.005014654,0.0014493053,0.1161602,0.033933576,0.0013976594,0.017539866,0.00020378389],"about_ca_topic_score_codex":0.011764342,"about_ca_topic_score_gemma":0.015835868,"teacher_disagreement_score":0.011764342,"about_ca_system_score_codex":0.00057169155,"about_ca_system_score_gemma":0.0006052514,"threshold_uncertainty_score":0.023391724},"labels":[],"label_agreement":null},{"id":"W3096847612","doi":"10.1155/2020/8857346","title":"Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care","year":2020,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Artificial intelligence; Pandemic; Edge computing; Health care; Machine learning; Cloud computing; Population; Enhanced Data Rates for GSM Evolution; Medicine; Environmental health","score_opus":0.37414065811356717,"score_gpt":0.41818875641549724,"score_spread":0.04404809830193007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096847612","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":[0.2842428,0.001510366,0.6956197,0.0041259835,0.00028566172,0.00018387288,0.0008119013,0.0008826386,0.0123371035],"genre_scores_gemma":[0.97809404,0.00055822637,0.017463343,0.00018192413,0.00007477716,0.00008154405,0.0002570699,0.00001630601,0.0032727565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995559,0.00013559994,0.000030267693,0.00011572888,0.000072561,0.00008998863],"domain_scores_gemma":[0.999071,0.00058966345,0.00013821102,0.00002784126,0.00012934918,0.000044008426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009830638,0.0006053783,0.0009124246,0.00051331753,0.00033379137,0.0010873341,0.0009562484,0.0009858077,0.0013897755],"category_scores_gemma":[0.0020626052,0.00039259996,0.00089033705,0.00039867344,0.00041190445,0.000986448,0.0007181796,0.0011212683,0.00021713745],"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":[0.000044678094,0.00004819258,0.004379248,0.0000372763,0.000033566062,0.00009982925,0.000031872703,0.9812835,0.00046498448,0.0036427348,0.0005422511,0.009391808],"study_design_scores_gemma":[0.0000025991596,0.000019597506,0.0003824688,0.000003717841,0.000006518198,0.000012486441,0.0000075890043,0.9982551,0.000104302,0.0010727958,0.00012799975,0.000004826064],"about_ca_topic_score_codex":0.014517094,"about_ca_topic_score_gemma":0.0076298197,"teacher_disagreement_score":0.014517094,"about_ca_system_score_codex":0.0010495626,"about_ca_system_score_gemma":0.0012264118,"threshold_uncertainty_score":0.028865218},"labels":[],"label_agreement":null},{"id":"W3161130502","doi":"10.1155/2021/6632394","title":"Galvanic Vestibular Stimulation Improves Subnetwork Interactions in Parkinson’s Disease","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Vestibular and auditory disorders","field":"Neuroscience","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":"University of British Columbia; Canadian Sport Centre Pacific","funders":"University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Galvanic vestibular stimulation; Parkinson's disease; Stimulus (psychology); Psychology; Vestibular system; Neuroscience; Audiology; Stimulation; Disease; Subnetwork; Medicine; Internal medicine; Cognitive psychology; Computer science","score_opus":0.020763455159112574,"score_gpt":0.28382057023657553,"score_spread":0.26305711507746293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161130502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99894315,0.00013936302,0.0006732478,0.0000138902005,0.000001973528,0.0000063768266,0.0000409158,0.000017291448,0.0001637903],"genre_scores_gemma":[0.99944526,0.00006209324,0.0003604617,0.000008221826,0.0000021372387,0.000004381767,0.00005650215,0.0000021226297,0.000058750586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999567,0.000011734312,0.0000042174684,0.00001076763,0.00000783754,0.000008674107],"domain_scores_gemma":[0.99991536,0.000031862524,0.000022643071,0.0000058310043,0.000008743437,0.000015546691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015788621,0.00029583523,0.00022533404,0.0002161311,0.00010842133,0.00014361137,0.000066043394,0.0001254797,0.0008156718],"category_scores_gemma":[0.0004714352,0.00008361675,0.00018492626,0.00009889797,0.0001706201,0.00013603673,0.00022885816,0.00011290294,0.000039655453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0072817374,0.00050291466,0.1439262,0.00033022926,0.00043090602,0.00046303216,0.0003199468,0.006182453,0.75565416,0.00018565515,0.00040166767,0.084321134],"study_design_scores_gemma":[0.00011041763,0.002509956,0.95820606,0.0000141999535,0.00017064459,0.00044697846,0.00010424225,0.007471242,0.030076396,0.00050827436,0.00036908584,0.000012473659],"about_ca_topic_score_codex":0.00077376567,"about_ca_topic_score_gemma":0.0017379533,"teacher_disagreement_score":0.0008156718,"about_ca_system_score_codex":0.0001484742,"about_ca_system_score_gemma":0.000107710184,"threshold_uncertainty_score":0.002728641},"labels":[],"label_agreement":null},{"id":"W3164721408","doi":"10.1155/2021/9500304","title":"Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":60,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Taif University","keywords":"Robustness (evolution); Fuse (electrical); Computer science; Convolutional neural network; Artificial intelligence; Object detection; Computational intelligence; Artificial neural network; Algorithm; Machine learning; Backpropagation; Face detection; Field (mathematics); Pattern recognition (psychology); Facial recognition system","score_opus":0.022601397701450764,"score_gpt":0.29858915478299086,"score_spread":0.2759877570815401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164721408","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":[0.004974446,0.000704574,0.9916502,0.0003288825,0.000075007156,0.000046257133,0.000042006926,0.00042344953,0.0017551203],"genre_scores_gemma":[0.2476648,0.0013422443,0.74284554,0.0003597193,0.00014670752,0.00024104025,0.00040837566,0.0000937611,0.0068977573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995147,0.00008588182,0.000039705574,0.00013895967,0.00015133078,0.00006951467],"domain_scores_gemma":[0.9994942,0.0001873233,0.000055305685,0.000064248416,0.00017704978,0.000021868827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080746895,0.00086015655,0.00090899447,0.0009002266,0.00052045076,0.0010104183,0.0011227572,0.0015832876,0.004250472],"category_scores_gemma":[0.0026241615,0.00036414803,0.00079491903,0.0010422305,0.0004252665,0.0015420186,0.00131528,0.00114726,0.0012038338],"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":[0.00015705817,0.00016747096,0.0015250653,0.00021959294,0.00008107818,0.00013772397,0.00011210877,0.3797851,0.0044383793,0.021217544,0.009327882,0.58283097],"study_design_scores_gemma":[0.000013932098,0.00003471099,0.0002615296,0.000013659917,0.0000115005005,0.00008995449,0.000039887917,0.98300606,0.0014105155,0.01213878,0.0029719316,0.0000076035417],"about_ca_topic_score_codex":0.004065239,"about_ca_topic_score_gemma":0.003114767,"teacher_disagreement_score":0.004250472,"about_ca_system_score_codex":0.00073160167,"about_ca_system_score_gemma":0.0014859212,"threshold_uncertainty_score":0.014219224},"labels":[],"label_agreement":null},{"id":"W3188021701","doi":"10.1155/2021/2302379","title":"Cognitive Dysfunction of Pregnant Women with Gestational Diabetes Mellitus in Perinatal Period","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":11,"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":"Harbin Medical University","keywords":"Gestational diabetes; Perinatal period; Medicine; Gestational period; Obstetrics; Pregnancy; Cognition; Diabetes mellitus; Postpartum period; Pediatrics; Gestation; Psychiatry; Endocrinology","score_opus":0.010987315917202483,"score_gpt":0.26640072906814344,"score_spread":0.25541341315094096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188021701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976374,0.0015698711,0.000045783658,0.00006880746,0.0000096278345,0.000006839043,0.00010962638,0.000002279693,0.0005498081],"genre_scores_gemma":[0.9984962,0.0011553521,0.000088317945,0.00004725809,0.000008465954,0.000007485692,0.00008148694,6.9194203e-7,0.0001147027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998511,0.00003715196,0.00001740751,0.00003063504,0.000023433928,0.000040193878],"domain_scores_gemma":[0.9997484,0.00003917625,0.00013183895,0.0000128619395,0.000022795364,0.000044918103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023107488,0.00029165327,0.00026308076,0.00052576524,0.00032614122,0.00031572965,0.00015613042,0.00029514136,0.00053207105],"category_scores_gemma":[0.0013837611,0.00016821672,0.0002273761,0.00045678267,0.00018640644,0.00023147643,0.0002452897,0.0003393244,0.000086049215],"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":[0.00019223065,0.000041187046,0.9929939,0.000027150587,0.000035687444,0.0012325438,0.00019334667,0.000016684899,0.0003712918,0.000023763874,0.00008140451,0.0047908006],"study_design_scores_gemma":[0.00000447203,0.00014623904,0.9957812,0.000028355895,0.00004289107,0.0032167034,0.00035355255,0.00005368189,0.0001037352,0.000050315615,0.00021522945,0.0000036502915],"about_ca_topic_score_codex":0.0058049867,"about_ca_topic_score_gemma":0.006349536,"teacher_disagreement_score":0.0058049867,"about_ca_system_score_codex":0.00025893695,"about_ca_system_score_gemma":0.00026242615,"threshold_uncertainty_score":0.01154238},"labels":[],"label_agreement":null},{"id":"W3193560119","doi":"10.1155/2021/9739219","title":"Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis","year":2021,"lang":"en","type":"review","venue":"Journal of Healthcare Engineering","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":48,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University; National Science Foundation","keywords":"Healthcare industry; Field (mathematics); Health care; Blockchain; Data science; Knowledge management; Bibliometrics; Web of science; Visualization; Computer science; Business; Engineering management; MEDLINE; Engineering; World Wide Web; Artificial intelligence; Political science; Economic growth; Economics; Computer security","score_opus":0.020227482202427705,"score_gpt":0.3166005475590922,"score_spread":0.2963730653566645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193560119","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":[0.00095585844,0.9951568,0.0002662405,0.001122045,0.00014650947,0.000026429549,0.00029519643,0.000013940935,0.0020168612],"genre_scores_gemma":[0.003959015,0.99502593,0.0003092606,0.0001872786,0.00010112697,0.000018788533,0.00018127913,0.000003035576,0.00021442027],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809545,0.000471718,0.00047387823,0.000175754,0.00069655856,0.00008661808],"domain_scores_gemma":[0.9849268,0.01009107,0.0017159162,0.00020727498,0.002830136,0.00022887041],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003146148,0.0008038136,0.0017568618,0.033706952,0.000652745,0.002067299,0.00083767803,0.0009308622,0.004967939],"category_scores_gemma":[0.012735699,0.0004758135,0.0013512515,0.045776524,0.0005309479,0.0023052501,0.000882392,0.00068130845,0.00083183695],"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":[0.00008768787,0.000060581904,0.0032558143,0.23829524,0.00090370263,0.0003414302,0.00071824505,0.0006723451,0.00067886413,0.005558055,0.035511404,0.7139166],"study_design_scores_gemma":[0.000025705514,0.00015757939,0.017043531,0.25563592,0.004753995,0.0015308664,0.001251351,0.0005193244,0.00090252835,0.0040818197,0.71401024,0.0000871712],"about_ca_topic_score_codex":0.006629851,"about_ca_topic_score_gemma":0.01386921,"teacher_disagreement_score":0.96629304,"about_ca_system_score_codex":0.0017471611,"about_ca_system_score_gemma":0.0073816464,"threshold_uncertainty_score":0.016638577},"labels":[],"label_agreement":null},{"id":"W3197692955","doi":"10.1155/2021/7358874","title":"Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":25,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Moncton; Université du Québec à Montréal","funders":"Ministry of Education – Kingdom of Saudi Arabi; Taif University","keywords":"Context (archaeology); Interactivity; Computer science; Augmented reality; Asynchronous learning; Blended learning; Distance education; Educational technology; Synchronous learning; Multimedia; Human–computer interaction; Teaching method; Cooperative learning; Psychology; Pedagogy","score_opus":0.015043987123667904,"score_gpt":0.25179792386012917,"score_spread":0.23675393673646128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197692955","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":[0.31941316,0.0014484377,0.5804641,0.0024707464,0.0007504992,0.0008741618,0.00046538934,0.007902795,0.086210676],"genre_scores_gemma":[0.9523946,0.00039248655,0.036489278,0.0006631487,0.00005004666,0.00027370543,0.00018330311,0.000059243324,0.009494023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997583,0.00004618787,0.00002046781,0.000052134266,0.000062640036,0.000060184524],"domain_scores_gemma":[0.9997645,0.00004883268,0.00002340059,0.000025307718,0.000079936755,0.000057975092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027664145,0.000357235,0.00033993818,0.00033281837,0.00047040204,0.0008918368,0.0011590244,0.00097301893,0.0029966973],"category_scores_gemma":[0.0006424289,0.00012393713,0.00033433276,0.00017789436,0.00030217404,0.0010779286,0.0013421273,0.00049413176,0.0012878021],"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":[0.0018288627,0.0021382042,0.016169168,0.0014559525,0.00019986354,0.0051355897,0.0029332396,0.084594995,0.21005844,0.026815854,0.039416187,0.6092537],"study_design_scores_gemma":[0.00030289267,0.0015093405,0.011940083,0.00029247068,0.00028958006,0.002373935,0.0015139717,0.78461105,0.06301308,0.017202038,0.11670912,0.00024248242],"about_ca_topic_score_codex":0.0012894253,"about_ca_topic_score_gemma":0.0014437546,"teacher_disagreement_score":0.0029966973,"about_ca_system_score_codex":0.00031864597,"about_ca_system_score_gemma":0.000508976,"threshold_uncertainty_score":0.010024905},"labels":[],"label_agreement":null},{"id":"W3201221629","doi":"10.1155/2021/9624386","title":"Task-Oriented Intelligent Solution to Measure Parkinson’s Disease Tremor Severity","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":6,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University; Michael J. Fox Foundation for Parkinson's Research","keywords":"Computer science; Physical medicine and rehabilitation; Artificial intelligence; Support vector machine; Resampling; Machine learning; Medicine","score_opus":0.02366483988301614,"score_gpt":0.27977330113387033,"score_spread":0.2561084612508542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201221629","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":[0.28348994,0.0017956847,0.70656455,0.00028536853,0.00016717913,0.00043801955,0.00067296514,0.0025569163,0.004029344],"genre_scores_gemma":[0.84852964,0.00046003898,0.14798047,0.00011014895,0.000057478912,0.00031425757,0.00080708525,0.000048002945,0.001693032],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999154,0.00018021972,0.00009091865,0.00019793717,0.0003234228,0.00005355434],"domain_scores_gemma":[0.99917334,0.00018692593,0.00014222364,0.00007642639,0.00038007414,0.00004095049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008305807,0.0007609033,0.0005730322,0.00081123837,0.00021705651,0.0007062989,0.00043420543,0.00046936,0.00092950714],"category_scores_gemma":[0.0022205485,0.00013120321,0.00039012215,0.00057000946,0.000112895075,0.00048655932,0.00038687763,0.00030262582,0.0005177102],"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":[0.0010664844,0.00053359393,0.026340695,0.000562531,0.00023721824,0.0001751476,0.00025721252,0.032031063,0.09906466,0.0011181799,0.00332501,0.8352883],"study_design_scores_gemma":[0.00011221859,0.0027502317,0.09711365,0.000111920934,0.0003800555,0.00088113855,0.0003412325,0.8057959,0.08070644,0.003433504,0.008242615,0.00013114604],"about_ca_topic_score_codex":0.0013416321,"about_ca_topic_score_gemma":0.0016601075,"teacher_disagreement_score":0.0013416321,"about_ca_system_score_codex":0.0002860986,"about_ca_system_score_gemma":0.0003593918,"threshold_uncertainty_score":0.0043925643},"labels":[],"label_agreement":null},{"id":"W3201715095","doi":"10.1155/2021/3531199","title":"The Influence of Different Dexmedetomidine Doses on Cognitive Function at Early Period of Patients Undergoing Laparoscopic Extensive Total Hysterectomy","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":9,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sufentanil; Medicine; Dexmedetomidine; Propofol; Remifentanil; Hysterectomy; Anesthesia; Group B; Postoperative cognitive dysfunction; Group A; Montreal Cognitive Assessment; Surgery; Cognitive impairment; Internal medicine; Cognition; Sedation","score_opus":0.009317658524198889,"score_gpt":0.2529176690702194,"score_spread":0.2436000105460205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201715095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978855,0.0015292418,0.000037458405,0.000058721198,0.000017191038,0.000007213885,0.000054639622,0.0000016120272,0.0004083479],"genre_scores_gemma":[0.99915504,0.00051262556,0.000054974247,0.00004671058,0.00002223602,0.000008607469,0.00008032937,5.6282704e-7,0.00011891039],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99986327,0.000024978608,0.000018698232,0.000022590473,0.000026655576,0.000043788055],"domain_scores_gemma":[0.9995003,0.00006800766,0.0002232464,0.000017230945,0.000050204366,0.00014100038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017183054,0.00025314215,0.00031179193,0.00023871668,0.00022655747,0.0003018671,0.00011939245,0.00017669408,0.000632402],"category_scores_gemma":[0.0008814013,0.00006885782,0.00044760527,0.00017385832,0.00014675026,0.00019676113,0.00015773423,0.00043433564,0.000077102275],"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":[0.006817806,0.0006619425,0.9536052,0.00020291354,0.00028265512,0.0009616127,0.00027454356,0.00019226166,0.003394494,0.00003215504,0.00036836465,0.03320623],"study_design_scores_gemma":[0.000055422523,0.0022713915,0.99572647,0.000026476726,0.000123905,0.00051967433,0.00017178571,0.00010552521,0.0005471527,0.0000326517,0.00041147752,0.000008085919],"about_ca_topic_score_codex":0.0010474594,"about_ca_topic_score_gemma":0.0021813305,"teacher_disagreement_score":0.0010474594,"about_ca_system_score_codex":0.00027804845,"about_ca_system_score_gemma":0.0002977219,"threshold_uncertainty_score":0.0021156073},"labels":[],"label_agreement":null},{"id":"W3210290409","doi":"10.1155/2021/2621655","title":"A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Vital signs; Machine learning; Decision tree; Support vector machine; Artificial intelligence; Naive Bayes classifier; Medicine; Clinical decision support system; Computer science; Decision support system","score_opus":0.06145172069090836,"score_gpt":0.3635881226520493,"score_spread":0.30213640196114094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210290409","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":[0.32763147,0.0036359522,0.5936908,0.002870508,0.0013703819,0.0011963422,0.009920257,0.050410394,0.009273892],"genre_scores_gemma":[0.85683244,0.0005926634,0.1322468,0.0005570813,0.00019302127,0.00052001607,0.0048426124,0.000070257425,0.004145092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993144,0.00009226505,0.00008651726,0.0002849571,0.00014918193,0.0000726722],"domain_scores_gemma":[0.9989213,0.00048733762,0.00012204351,0.0000739829,0.00032876976,0.00006653779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011590935,0.0010219543,0.0010538442,0.0013453466,0.00056631624,0.0006901941,0.0011324179,0.0012156744,0.0029855003],"category_scores_gemma":[0.0031326744,0.00030282987,0.0006038414,0.0008666769,0.00014955606,0.0009162002,0.00045602437,0.0009596093,0.0016771627],"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":[0.0014251388,0.0022664205,0.0678486,0.0005098437,0.00033360624,0.0013169906,0.00019944565,0.09591893,0.017084027,0.0014192308,0.038357876,0.77331984],"study_design_scores_gemma":[0.000052749063,0.00028519408,0.009600109,0.000040179115,0.000056241493,0.00017709387,0.000028050023,0.9815702,0.0046187015,0.0009813955,0.002553191,0.000036761234],"about_ca_topic_score_codex":0.00949154,"about_ca_topic_score_gemma":0.0076348763,"teacher_disagreement_score":0.00949154,"about_ca_system_score_codex":0.0006906251,"about_ca_system_score_gemma":0.0009238867,"threshold_uncertainty_score":0.01887256},"labels":[],"label_agreement":null},{"id":"W3212082921","doi":"10.1155/2021/9974059","title":"Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":10,"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 Saskatchewan","funders":"Hong Kong Polytechnic University; University Grants Committee","keywords":"Nursing; Workforce; Blockchain; Long-term care; Term (time); Nursing homes; Medicine; Business; Computer science; Computer security","score_opus":0.049242390353874545,"score_gpt":0.4181900347243553,"score_spread":0.3689476443704807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212082921","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":[0.11545724,0.0012168942,0.8642441,0.0013480207,0.00017601244,0.0002936985,0.0002963152,0.0006052027,0.016362483],"genre_scores_gemma":[0.97501695,0.00040318965,0.021927923,0.000079094774,0.000024599232,0.00011681916,0.00011851245,0.0000196399,0.0022932822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994436,0.00018027575,0.000032921766,0.00010322563,0.00011455167,0.00012546904],"domain_scores_gemma":[0.99898845,0.00050000125,0.0001536743,0.000057564583,0.00017876257,0.0001215899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010209652,0.000492954,0.00069066684,0.00040689355,0.0006524505,0.0012750882,0.000866,0.00077535666,0.003436709],"category_scores_gemma":[0.0020157672,0.00030812886,0.00039445583,0.00056079443,0.0005057602,0.001507079,0.0014222013,0.0007347244,0.00038671723],"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":[0.0001323356,0.00007744181,0.0018218728,0.00012439712,0.000025719195,0.00018192502,0.00013230201,0.94566804,0.0022545087,0.00954478,0.0010767384,0.038959913],"study_design_scores_gemma":[0.000017618851,0.000044646637,0.00028899958,0.000017224043,0.000009061445,0.00002533674,0.000046151727,0.9911528,0.00052267645,0.0068711406,0.0009951254,0.000009190432],"about_ca_topic_score_codex":0.006796257,"about_ca_topic_score_gemma":0.008415421,"teacher_disagreement_score":0.006796257,"about_ca_system_score_codex":0.0010448945,"about_ca_system_score_gemma":0.0024364197,"threshold_uncertainty_score":0.013513446},"labels":[],"label_agreement":null},{"id":"W3213349144","doi":"10.1155/2021/5853128","title":"Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Traditional Chinese Medicine Studies","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":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Tongue; Segmentation; Deep learning; Artificial intelligence; The Internet; Image segmentation; Medicine; World Wide Web; Pathology","score_opus":0.010598209167982415,"score_gpt":0.2643532729723852,"score_spread":0.25375506380440277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213349144","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":[0.087387376,0.0012262409,0.9015624,0.00042182574,0.00018847511,0.00011312394,0.00020024476,0.0042775,0.0046228333],"genre_scores_gemma":[0.7759871,0.00055459107,0.21608585,0.00035993633,0.00008341448,0.00011189962,0.0005227769,0.0001293227,0.0061650802],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978346,0.000040104856,0.000016189058,0.00006860036,0.000052069936,0.000039572013],"domain_scores_gemma":[0.9997743,0.00007658745,0.00001987614,0.000026451236,0.00008621287,0.000016625121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073743804,0.0006693237,0.0005132361,0.00082637067,0.00041419588,0.00059556257,0.0008502731,0.001039889,0.0021933122],"category_scores_gemma":[0.00091927906,0.00030050232,0.0005661757,0.0005615874,0.00036828654,0.00086300157,0.00065125857,0.00048802598,0.00042818865],"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":[0.00084242626,0.00020902432,0.0051642284,0.00016170304,0.00016133473,0.000473073,0.00014372676,0.27964258,0.029770005,0.003989425,0.0054004793,0.67404205],"study_design_scores_gemma":[0.0000086851405,0.00006507646,0.00054521277,0.0000074702975,0.00002309667,0.00006336432,0.000014222555,0.9886884,0.008881891,0.000931589,0.0007620176,0.0000088995685],"about_ca_topic_score_codex":0.007540333,"about_ca_topic_score_gemma":0.006372012,"teacher_disagreement_score":0.007540333,"about_ca_system_score_codex":0.00075231976,"about_ca_system_score_gemma":0.000736744,"threshold_uncertainty_score":0.014992893},"labels":[],"label_agreement":null},{"id":"W3216640438","doi":"10.1155/2021/9857089","title":"Guest Editorial: Special Issue on Artificial Intelligence in E-Healthcare and M-Healthcare","year":2021,"lang":"en","type":"editorial","venue":"Journal of Healthcare Engineering","topic":"Technology and Data Analysis","field":"Computer Science","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 Victoria","funders":"","keywords":"Health care; Computer science; Data science; Artificial intelligence; Political science","score_opus":0.011680183409602769,"score_gpt":0.2891179782138005,"score_spread":0.2774377948041977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216640438","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023742528,0.0035801802,0.00008390288,0.023901295,0.971134,0.000015937578,0.000041741427,0.000036387453,0.0011827791],"genre_scores_gemma":[0.00017651662,0.0017664209,0.000052385927,0.009340442,0.9841934,0.000014000263,0.000025147487,0.000026591937,0.004405108],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9917883,0.0015157233,0.0009079629,0.00096683955,0.0042172447,0.0006038801],"domain_scores_gemma":[0.9643731,0.011976296,0.0030373484,0.00087263493,0.013191633,0.0065489984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009082037,0.004595628,0.0058139456,0.0063144304,0.003981606,0.013889049,0.0035826953,0.02107147,0.030653162],"category_scores_gemma":[0.03282436,0.0016140576,0.003444894,0.0022918305,0.0025937855,0.004903814,0.002737201,0.018145766,0.017515292],"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":[0.000034207143,0.000011194211,0.000026446118,0.00016648426,0.000020487902,0.000100337806,0.0000058911373,0.000017590723,0.00004469796,0.00015496682,0.99616635,0.003251331],"study_design_scores_gemma":[0.00011893538,0.00004790804,0.00045439537,0.0006664761,0.000092290495,0.0003950375,0.00004992135,0.00029725186,0.000118381235,0.001358202,0.99637115,0.000030068937],"about_ca_topic_score_codex":0.0012359228,"about_ca_topic_score_gemma":0.004897956,"teacher_disagreement_score":0.030653162,"about_ca_system_score_codex":0.0036863394,"about_ca_system_score_gemma":0.003820589,"threshold_uncertainty_score":0.10254502},"labels":[],"label_agreement":null},{"id":"W35812896","doi":"10.1155/2022/8399822","title":"Shared landscape, divergent visions? transboundary environmental management in the Northern Great Plains","year":2010,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"American Environmental and Regional History","field":"Environmental Science","cited_by":2,"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":"Farm Service Agency; European Commission; Nature Conservancy; Western Association of Fish and Wildlife Agencies; World Wildlife Fund; Parks Canada; University of Saskatchewan; U.S. Fish and Wildlife Service; U.S. Department of Agriculture","keywords":"Vision; Geography; Environmental resource management; Environmental planning; Environmental protection; Environmental science; Sociology","score_opus":0.005475663850695139,"score_gpt":0.1966285700001321,"score_spread":0.19115290614943695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W35812896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916848,0.0005642299,0.0007308107,0.0031272129,0.000017472035,0.000008473107,0.00012460226,0.000016795882,0.0037256756],"genre_scores_gemma":[0.9986877,0.0002270791,0.0005594657,0.000098251156,0.000012305716,0.0000061765063,0.000065826534,0.0000029292175,0.0003403623],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99948704,0.0002571497,0.0000176867,0.000081224,0.00006045025,0.000096554635],"domain_scores_gemma":[0.9997009,0.000058780246,0.00010537145,0.000026268404,0.000052128136,0.000056626504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050841545,0.00014340406,0.00019747231,0.00054639624,0.0008226172,0.0010061149,0.00039913395,0.00033595203,0.0010458421],"category_scores_gemma":[0.0009932787,0.00010331579,0.00018724403,0.0012091174,0.0015018242,0.0008878904,0.0010627102,0.0003390289,0.00008075358],"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":[0.00019250628,0.00015548359,0.87898606,0.0002271389,0.00019718744,0.004448935,0.014209158,0.003768003,0.0027724567,0.006987382,0.0032003506,0.08485526],"study_design_scores_gemma":[0.000010687414,0.00004924173,0.9474059,0.00010435725,0.000040465336,0.0006343428,0.03179202,0.0054708733,0.00022828829,0.00446422,0.009765879,0.00003374132],"about_ca_topic_score_codex":0.0635136,"about_ca_topic_score_gemma":0.12875608,"teacher_disagreement_score":0.0635136,"about_ca_system_score_codex":0.0013533308,"about_ca_system_score_gemma":0.0014842623,"threshold_uncertainty_score":0.12628782},"labels":[],"label_agreement":null},{"id":"W4200055690","doi":"10.1155/2021/4178161","title":"Classification of Public Health Centres in Accra through a Web-Based Portal Integrated with Geographical Information System (GIS)","year":2021,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Data-Driven Disease Surveillance","field":"Medicine","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":"Concordia University","funders":"","keywords":"Public health; Geographic information system; Health care; Information system; Population; Categorization; Business; Health information; Location; Knowledge management; Medicine; World Wide Web; Environmental health; Computer science; Geography; Nursing; Economic growth; Political science; Cartography","score_opus":0.022748581651365233,"score_gpt":0.27334914586657133,"score_spread":0.2506005642152061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200055690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9237903,0.001032044,0.026043575,0.0019574875,0.00006906646,0.0013203582,0.0145667475,0.001196012,0.030024467],"genre_scores_gemma":[0.92443806,0.0007012216,0.063168846,0.00009057945,0.000021558357,0.00048118614,0.0066655227,0.00003064662,0.0044021807],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993755,0.00018010108,0.00009129839,0.00008640035,0.00015637086,0.00011032313],"domain_scores_gemma":[0.9985845,0.00037142407,0.00049339054,0.000111922665,0.0002691864,0.00016955819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008184864,0.0002226504,0.00017196347,0.0056293015,0.0007338599,0.0018575018,0.00045718715,0.00043010997,0.0041990587],"category_scores_gemma":[0.0021365026,0.00018001324,0.000244408,0.008205435,0.00039880397,0.0013742913,0.0010745898,0.00030704384,0.00078428356],"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":[0.00020416478,0.00039959498,0.7146132,0.0007439969,0.000048268917,0.0014516765,0.0130582815,0.0052323085,0.0037867436,0.008665322,0.020186825,0.23160958],"study_design_scores_gemma":[0.0000608097,0.00017038717,0.7604151,0.0005335693,0.000082130486,0.0013159803,0.060992107,0.032536738,0.0040509854,0.0032441164,0.13649967,0.00009837478],"about_ca_topic_score_codex":0.0333781,"about_ca_topic_score_gemma":0.03638779,"teacher_disagreement_score":0.0333781,"about_ca_system_score_codex":0.0010630423,"about_ca_system_score_gemma":0.002567641,"threshold_uncertainty_score":0.066367626},"labels":[],"label_agreement":null},{"id":"W4210397188","doi":"10.1155/2022/1573076","title":"Semisupervised Seizure Prediction in Scalp EEG Using Consistency Regularization","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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 British Columbia","funders":"University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Computer science; Regularization (linguistics); Dropout (neural networks); Artificial intelligence; Machine learning; Consistency (knowledge bases); Deep learning; Electroencephalography; Artificial neural network; Deep neural networks; Psychology","score_opus":0.028266012431487485,"score_gpt":0.2671460695630629,"score_spread":0.23888005713157542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210397188","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":[0.057078347,0.00018243179,0.9409665,0.00030125654,0.000023287483,0.00004373922,0.00013419925,0.0006506502,0.0006197137],"genre_scores_gemma":[0.88523287,0.0002336037,0.110910855,0.0002450033,0.000091983464,0.00014197876,0.0007352368,0.0001364545,0.0022720655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995493,0.00015320434,0.000027750493,0.00013415917,0.00008790087,0.000047627665],"domain_scores_gemma":[0.99834263,0.0009149086,0.0002827764,0.00016062897,0.00022432221,0.000074683456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011150829,0.0007825867,0.0008983052,0.0004262211,0.00029173557,0.00060937693,0.0011763951,0.0008951089,0.0007823417],"category_scores_gemma":[0.003987401,0.00048991153,0.00069435674,0.0003889778,0.00063540076,0.00094332255,0.0010998719,0.0013056913,0.00026694525],"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":[0.0003828652,0.00013697351,0.0055960095,0.00009792174,0.00009790456,0.00026437384,0.00011031035,0.8561153,0.010310738,0.005336423,0.002650369,0.1189008],"study_design_scores_gemma":[0.0000052835003,0.000013141335,0.00020471735,0.0000028030688,0.0000032066437,0.00001826137,0.000002690466,0.9974722,0.00066431327,0.0015292207,0.000080315855,0.0000037273214],"about_ca_topic_score_codex":0.0029176276,"about_ca_topic_score_gemma":0.0037434443,"teacher_disagreement_score":0.0029176276,"about_ca_system_score_codex":0.00037027468,"about_ca_system_score_gemma":0.0010639902,"threshold_uncertainty_score":0.005897224},"labels":[],"label_agreement":null},{"id":"W4210784663","doi":"10.1155/2022/3449433","title":"Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","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":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation; University of Engineering and Technology, Lahore","keywords":"Dilemma; Personality; Social dilemma; Trait; Computer science; Prisoner's dilemma; Cognitive psychology; Psychology; Artificial intelligence; Social psychology","score_opus":0.02922504755833371,"score_gpt":0.2848748206498123,"score_spread":0.25564977309147857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210784663","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":[0.33268747,0.00069200015,0.65865904,0.0012437076,0.0001242896,0.00007216809,0.00006386444,0.00027285193,0.006184593],"genre_scores_gemma":[0.98321503,0.000117416675,0.0147868125,0.00013626798,0.000012611774,0.000051037612,0.00002778113,0.000012650899,0.0016402988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978966,0.00006796691,0.000011748663,0.000046767127,0.000029053606,0.000054791777],"domain_scores_gemma":[0.99908566,0.0005266666,0.000112227084,0.000033801833,0.00015216722,0.00008942984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009248749,0.00047983736,0.0006948062,0.00023844939,0.00044307063,0.0007953997,0.0010378971,0.0008844967,0.0019464487],"category_scores_gemma":[0.0025267533,0.00029028315,0.00043700205,0.00015579611,0.00079889817,0.00096643524,0.00089014444,0.0009378189,0.00015151304],"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":[0.00008529541,0.000082528095,0.0030712336,0.000050743907,0.000054679927,0.00010213958,0.00014122522,0.96900004,0.0020213572,0.007954703,0.00067982357,0.016756313],"study_design_scores_gemma":[0.000004867749,0.000012541985,0.00010735768,0.0000014455333,0.000003716069,0.000003840709,0.0000055926444,0.99829704,0.0000791279,0.0014425237,0.000040083058,0.0000018461008],"about_ca_topic_score_codex":0.0057425774,"about_ca_topic_score_gemma":0.003358091,"teacher_disagreement_score":0.0057425774,"about_ca_system_score_codex":0.00084628747,"about_ca_system_score_gemma":0.00089267426,"threshold_uncertainty_score":0.011418283},"labels":[],"label_agreement":null},{"id":"W4212777698","doi":"10.1155/2022/7821525","title":"A Cohort Study to Assess Cognitive Impairment and Its Effects on Older Patients in the Orthopedic Rehabilitation","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":0,"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":"Rehabilitation; Orthopedic surgery; Cohort; Medicine; Cohort study; Cognitive impairment; Physical medicine and rehabilitation; Cognition; Physical therapy; Gerontology; Psychiatry; Pathology","score_opus":0.010315015212752993,"score_gpt":0.31064190912829137,"score_spread":0.30032689391553835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212777698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991098,0.000116716656,0.00008291437,0.000020368767,0.000012071409,0.00007068092,0.00034430102,0.0000013912438,0.00024177013],"genre_scores_gemma":[0.99854714,0.00013927324,0.00018370537,0.000066132925,0.000028285334,0.00012874136,0.0005286609,0.0000018964492,0.0003761084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993218,0.00016300344,0.00007961013,0.00015068527,0.00012978588,0.00015520469],"domain_scores_gemma":[0.9991285,0.00008403208,0.00024000756,0.0000980368,0.0001377798,0.00031162033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010516389,0.00046357757,0.0005670527,0.0010369166,0.0011803934,0.0006594377,0.00031694173,0.00040770293,0.002037724],"category_scores_gemma":[0.001599066,0.0003875829,0.0007783073,0.0011319984,0.00022887408,0.00063593115,0.00061952474,0.0007155536,0.00046402944],"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":[0.00028146175,0.00023690473,0.9979235,0.000013644176,0.000059721395,0.00026103697,0.00024331738,0.0000073386545,0.00014503382,0.000014211044,0.00009771305,0.0007162082],"study_design_scores_gemma":[0.000044721863,0.0014925227,0.99643207,0.000016087653,0.00006503357,0.00065092585,0.00074099144,0.0000675756,0.00004170898,0.000018355495,0.00042303873,0.000007007762],"about_ca_topic_score_codex":0.0071671237,"about_ca_topic_score_gemma":0.01094542,"teacher_disagreement_score":0.0071671237,"about_ca_system_score_codex":0.00037673413,"about_ca_system_score_gemma":0.0008212834,"threshold_uncertainty_score":0.014250815},"labels":[],"label_agreement":null},{"id":"W4213446910","doi":"10.1155/2022/7396950","title":"Effects of Systemic Rehabilitation Nursing Combined with WeChat Publicity and Education on the Early Cognitive Function and Living Quality of the Patients with Cerebral Arterial Thrombosis","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":13,"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":"Medicine; Publicity; Nursing; Rehabilitation; Montreal Cognitive Assessment; Activities of daily living; Stroke (engine); Cognition; Physical therapy; Health education; Cognitive impairment; Psychiatry; Public health","score_opus":0.012719692848688971,"score_gpt":0.2431673326001955,"score_spread":0.2304476397515065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213446910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99813,0.0010135334,0.000090115915,0.00011796329,0.000060062495,0.000082084975,0.000018853292,0.0000072640114,0.00048013582],"genre_scores_gemma":[0.99775994,0.0008668336,0.00052436657,0.00012612251,0.00009274599,0.00018095513,0.00006040936,0.0000011729671,0.00038749896],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9996669,0.00013705573,0.000027990702,0.000042386157,0.00004147993,0.00008414498],"domain_scores_gemma":[0.99947757,0.00009022278,0.00010113457,0.000027407234,0.00005645938,0.0002471754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005041646,0.00038606048,0.0006471645,0.00032408035,0.0005032109,0.00026379986,0.00017657316,0.00035307137,0.0017595993],"category_scores_gemma":[0.001468133,0.00009734432,0.0008917867,0.00024653308,0.00024041126,0.00022902647,0.0005629556,0.00049625017,0.00012592619],"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":[0.049484637,0.09679917,0.17771475,0.0029406066,0.0019146006,0.00091193523,0.0013678662,0.0012150465,0.00839152,0.0002011196,0.0023494144,0.6567093],"study_design_scores_gemma":[0.014125867,0.196174,0.7758289,0.0005652807,0.0026015835,0.00057406514,0.0014263382,0.0015587551,0.0031429864,0.00023312104,0.003686779,0.00008233238],"about_ca_topic_score_codex":0.0014857338,"about_ca_topic_score_gemma":0.0029437516,"teacher_disagreement_score":0.0017595993,"about_ca_system_score_codex":0.00028725585,"about_ca_system_score_gemma":0.00064142066,"threshold_uncertainty_score":0.0058864355},"labels":[],"label_agreement":null},{"id":"W4214505042","doi":"10.1155/2022/8472947","title":"Detection of Emotion of Speech for RAVDESS Audio Using Hybrid Convolution Neural Network","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":45,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disgust; Computer science; Speech recognition; Surprise; Spectrogram; Convolutional neural network; Artificial neural network; Hidden Markov model; Mel-frequency cepstrum; Emotion classification; Artificial intelligence; Natural language processing; Feature extraction; Psychology; Communication","score_opus":0.04109324861394537,"score_gpt":0.31547934300735997,"score_spread":0.2743860943934146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214505042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.850933,0.0006165107,0.14268915,0.00018918565,0.0001584004,0.00007678394,0.00063711597,0.0011604032,0.0035393694],"genre_scores_gemma":[0.9611598,0.00021780435,0.034832872,0.000044408593,0.00003517545,0.000037489448,0.0006586845,0.0000204053,0.0029932526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980944,0.000021969096,0.000012303489,0.000058717822,0.000065218795,0.00003245308],"domain_scores_gemma":[0.9998652,0.000039097067,0.0000124357675,0.000009829503,0.000061147395,0.0000122851325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025331645,0.00037826123,0.00031206076,0.00064119115,0.00014162848,0.00028199455,0.00023364232,0.00035575422,0.0011421208],"category_scores_gemma":[0.0004808513,0.00010623976,0.0003881118,0.00021420674,0.00011104998,0.0003076187,0.00028732515,0.00021727597,0.00028179595],"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":[0.0013473448,0.000281096,0.020012992,0.00017599465,0.0001406273,0.00057416083,0.00018438313,0.026092373,0.2932745,0.0005359439,0.0026664152,0.65471417],"study_design_scores_gemma":[0.000019806526,0.00023124188,0.04575029,0.000018084855,0.00005406344,0.00031974807,0.00013731892,0.8939674,0.057931866,0.00029165772,0.0012501891,0.00002838767],"about_ca_topic_score_codex":0.0024709278,"about_ca_topic_score_gemma":0.0035751062,"teacher_disagreement_score":0.0024709278,"about_ca_system_score_codex":0.0002792767,"about_ca_system_score_gemma":0.00015050024,"threshold_uncertainty_score":0.004913032},"labels":[],"label_agreement":null},{"id":"W4220784859","doi":"10.1155/2022/1979892","title":"Effects of Different Nonsteroidal Anti-Inflammatory Drugs Combined with Platelet-Rich Plasma on Inflammatory Factor Levels in Patients with Osteoarthritis","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Periodontal Regeneration and Treatments","field":"Medicine","cited_by":4,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Celecoxib; Diclofenac Sodium; Osteoarthritis; Diclofenac; Medicine; Visual analogue scale; Nonsteroidal; Internal medicine; Tumor necrosis factor alpha; Gastroenterology; Inflammation; Anti-inflammatory; Pharmacology; Anesthesia; Pathology","score_opus":0.005097937747361234,"score_gpt":0.20617826524238342,"score_spread":0.2010803274950222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220784859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9685962,0.030448252,0.0000949849,0.00008592421,0.000044633303,0.000034927332,0.00015418159,0.0000047338926,0.00053613965],"genre_scores_gemma":[0.995804,0.0034975393,0.00019786025,0.000095496565,0.000092134134,0.00001958301,0.00016692837,0.0000010012288,0.000125494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993425,0.00022339453,0.00012554132,0.000086308064,0.00015291781,0.000069195754],"domain_scores_gemma":[0.99913317,0.00019065633,0.0004452407,0.000022601853,0.00008628134,0.0001221636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000582305,0.0003470704,0.000864794,0.00051671464,0.00025198772,0.00029576474,0.00013148463,0.0002936314,0.00078271603],"category_scores_gemma":[0.0012196283,0.00014808367,0.00083082286,0.0005890583,0.00020004589,0.00027531743,0.00018647559,0.00035884045,0.00010059201],"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":[0.014846991,0.0007045101,0.93657,0.0011505097,0.0016060609,0.0011655638,0.0001490474,0.00010311704,0.0055506495,0.000034938097,0.00037007345,0.03774857],"study_design_scores_gemma":[0.0006554326,0.009671542,0.98376197,0.00008673043,0.0012493972,0.0020065354,0.0002289949,0.00018741781,0.00092657405,0.000032449592,0.001175276,0.000017602235],"about_ca_topic_score_codex":0.00041659654,"about_ca_topic_score_gemma":0.0012975254,"teacher_disagreement_score":0.000864794,"about_ca_system_score_codex":0.00016655175,"about_ca_system_score_gemma":0.00018045696,"threshold_uncertainty_score":0.0030795932},"labels":[],"label_agreement":null},{"id":"W4220892977","doi":"10.1155/2022/2545762","title":"Diffusion Tensor Imaging Observation of Frontal Lobe Multidirectional Transcranial Direct Current Stimulation in Stroke Patients with Memory Impairment","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Cardiovascular Disease and Adiposity","field":"Medicine","cited_by":5,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fujian Medical University","keywords":"Diffusion MRI; Transcranial direct-current stimulation; Frontal lobe; Neuroscience; Stroke (engine); Temporal lobe; Physical medicine and rehabilitation; Audiology; Tractography; Medicine; Psychology; Magnetic resonance imaging; Stimulation; Physics; Radiology; Epilepsy","score_opus":0.006837545276592116,"score_gpt":0.22687419016692664,"score_spread":0.22003664489033453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220892977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99938846,0.00011978862,0.00005706052,0.000021987715,0.0000027146295,0.000010781675,0.00003221298,0.0000017690512,0.00036527668],"genre_scores_gemma":[0.99972755,0.0000689699,0.00004227786,0.000013852724,0.0000050099616,0.000004503733,0.00004498417,3.632663e-7,0.00009248286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99990845,0.000015725025,0.000015706082,0.000017888839,0.000015613816,0.00002654592],"domain_scores_gemma":[0.9996611,0.000022271865,0.00016072243,0.000018223433,0.000057099875,0.000080521044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020054796,0.00030438282,0.00028723586,0.0005298109,0.00032745785,0.00024634015,0.00011147628,0.00022487216,0.00065548887],"category_scores_gemma":[0.0009491072,0.00012141366,0.00025265242,0.0002684621,0.00022625849,0.00025589613,0.00021350979,0.00022895179,0.00012742351],"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":[0.0019762383,0.00034763306,0.9697613,0.000073148374,0.00015930577,0.0043132426,0.0010940272,0.00013931704,0.0110146385,0.00004180639,0.00021042084,0.010868938],"study_design_scores_gemma":[0.00003554072,0.0006205631,0.9955721,0.0000078494795,0.000058797927,0.0024014788,0.00027636322,0.00020447125,0.0006080112,0.00003936536,0.0001667708,0.000008624386],"about_ca_topic_score_codex":0.0036820231,"about_ca_topic_score_gemma":0.0043826094,"teacher_disagreement_score":0.0036820231,"about_ca_system_score_codex":0.00022295862,"about_ca_system_score_gemma":0.0002079372,"threshold_uncertainty_score":0.007321179},"labels":[],"label_agreement":null},{"id":"W4220894258","doi":"10.1155/2022/6005446","title":"Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":137,"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":"King Saud University","keywords":"Classifier (UML); Computer science; Emotion recognition; Multilayer perceptron; Artificial intelligence; Speech recognition; Perceptron; Human–computer interaction; Pattern recognition (psychology); Artificial neural network","score_opus":0.11357282162374316,"score_gpt":0.39355888245126214,"score_spread":0.279986060827519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220894258","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":[0.13363865,0.0033997253,0.8330385,0.0008279745,0.00092817313,0.0005504842,0.0023759394,0.015020496,0.010220045],"genre_scores_gemma":[0.7462625,0.00129364,0.23157778,0.00049005693,0.00018870962,0.00088001956,0.0045175385,0.00024036472,0.01454943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994118,0.000110072084,0.000048026894,0.00016482352,0.00015515709,0.00011010568],"domain_scores_gemma":[0.99977833,0.000083926825,0.000013841067,0.000014439588,0.00009468499,0.00001484352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075742824,0.0010554921,0.0006934977,0.0008642302,0.00035864755,0.00063692254,0.0007332538,0.0009584064,0.0066187214],"category_scores_gemma":[0.0014721125,0.00029997333,0.0010350405,0.00043730333,0.0001302049,0.00066978595,0.00070168974,0.00088483223,0.0024014967],"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":[0.00083695684,0.0007037456,0.003973769,0.0003251881,0.00024175632,0.00031364232,0.00014257761,0.049057886,0.03567692,0.00095668103,0.025750725,0.88202006],"study_design_scores_gemma":[0.00003163541,0.00018229608,0.0036068494,0.000026770285,0.00004168927,0.00007750672,0.000043840228,0.9838988,0.008801672,0.0007416929,0.0025217016,0.000025579879],"about_ca_topic_score_codex":0.005759441,"about_ca_topic_score_gemma":0.0054607224,"teacher_disagreement_score":0.0066187214,"about_ca_system_score_codex":0.00044994723,"about_ca_system_score_gemma":0.0004188931,"threshold_uncertainty_score":0.022141814},"labels":[],"label_agreement":null},{"id":"W4220968993","doi":"10.1155/2022/8958099","title":"Quality Care Alleviates Behavioral Cognitive Impairment and Reduces Complications in Elderly Patients with Cardiovascular and Cerebrovascular Diseases","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":2,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Cognition; Intervention (counseling); Montreal Cognitive Assessment; Physical therapy; Complication; Disease; Emergency medicine; Cognitive impairment; Internal medicine; Psychiatry","score_opus":0.024880677409086147,"score_gpt":0.28007862940162964,"score_spread":0.25519795199254347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220968993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875987,0.00907503,0.00048747,0.0007884774,0.00009213537,0.00010554661,0.00011871769,0.000022544382,0.0017113299],"genre_scores_gemma":[0.9954782,0.0029868302,0.0007919144,0.00023413119,0.000060080736,0.000041202646,0.00009998232,0.0000014948242,0.00030621415],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997372,0.00009138919,0.000031653064,0.00002512623,0.00005501142,0.000059561564],"domain_scores_gemma":[0.9996439,0.000032825148,0.00014441648,0.000011385286,0.00008247836,0.00008499041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038217386,0.00018600837,0.00027384487,0.00032652073,0.0003647502,0.00029037753,0.00017095117,0.00023424724,0.0012329932],"category_scores_gemma":[0.0017129342,0.00005794377,0.0004977536,0.00025196729,0.00013537923,0.00017935174,0.00039242543,0.00030194165,0.000068134555],"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":[0.0031683727,0.009370522,0.44783553,0.0030695128,0.0011240009,0.000436215,0.0016314338,0.00061922864,0.006348271,0.00037983898,0.0054771854,0.5205399],"study_design_scores_gemma":[0.0006912249,0.0060901223,0.9831093,0.0006126545,0.0007134759,0.00031249697,0.0010101176,0.0008848126,0.0011806353,0.00039887486,0.0049756523,0.000020689278],"about_ca_topic_score_codex":0.0033862332,"about_ca_topic_score_gemma":0.0077591445,"teacher_disagreement_score":0.0033862332,"about_ca_system_score_codex":0.0004044333,"about_ca_system_score_gemma":0.0006583196,"threshold_uncertainty_score":0.00673306},"labels":[],"label_agreement":null},{"id":"W4221025407","doi":"10.1155/2022/4247023","title":"Short-Axis PET Image Quality Improvement by Attention CycleGAN Using Total-Body PET","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Medical Imaging Techniques and Applications","field":"Medicine","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":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer science; Image quality; Scanner; Computer vision; Field of view; Ground truth; Deep learning; Positron emission tomography; Pattern recognition (psychology); Nuclear medicine; Image (mathematics); Medicine","score_opus":0.02452822582982349,"score_gpt":0.36047331887566486,"score_spread":0.33594509304584136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221025407","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":[0.42226496,0.0014915507,0.5689906,0.00051126967,0.00015323426,0.00015146189,0.00024733195,0.0019200015,0.004269676],"genre_scores_gemma":[0.94969344,0.0003526429,0.0466436,0.00027712487,0.000047984115,0.000052099673,0.00042382616,0.000089732515,0.0024194268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997696,0.00005774039,0.000009287499,0.00007256205,0.000054293105,0.00003642165],"domain_scores_gemma":[0.9996371,0.0001720917,0.0000463365,0.000043606316,0.000081737366,0.000019055129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000525523,0.0006161424,0.00044736062,0.00040596834,0.00012673148,0.00032943962,0.0005721985,0.00050921354,0.00080390973],"category_scores_gemma":[0.0013941971,0.0002265227,0.0004488208,0.00021330081,0.00032471033,0.0005272341,0.0006148457,0.00042570254,0.00014415041],"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":[0.000590785,0.0002055999,0.0065316963,0.00020812705,0.00019855247,0.00038228507,0.000102424136,0.4704594,0.054622117,0.0014971605,0.0039045229,0.4612974],"study_design_scores_gemma":[0.000014199785,0.00013764313,0.0016714181,0.000010223801,0.00004501215,0.00015037369,0.000010273747,0.9855253,0.011168719,0.00068840774,0.00056650833,0.000011998312],"about_ca_topic_score_codex":0.0026203664,"about_ca_topic_score_gemma":0.003309971,"teacher_disagreement_score":0.0026203664,"about_ca_system_score_codex":0.00044959286,"about_ca_system_score_gemma":0.00036169332,"threshold_uncertainty_score":0.005210161},"labels":[],"label_agreement":null},{"id":"W4223505544","doi":"10.1155/2022/9891192","title":"Effect of Early Cognitive Training Combined with Aerobic Exercise on Quality of Life and Cognitive Function Recovery of Patients with Poststroke Cognitive Impairment","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":9,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aerobic exercise; Cognition; Physical therapy; Rehabilitation; Quality of life (healthcare); Montreal Cognitive Assessment; Functional Independence Measure; Physical medicine and rehabilitation; Medicine; Cognitive rehabilitation therapy; Activities of daily living; Stroke (engine); Cognitive training; Intervention (counseling); Psychology; Cognitive impairment; Psychiatry; Nursing","score_opus":0.019326305736783924,"score_gpt":0.2576561339341886,"score_spread":0.2383298281974047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223505544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99833065,0.0011609647,0.0000457055,0.0000591313,0.000030832623,0.000052544037,0.000022129747,0.000003802348,0.00029433818],"genre_scores_gemma":[0.9986972,0.000607146,0.00018366834,0.00008763183,0.00006771824,0.00009058376,0.00006284028,6.03476e-7,0.00020248521],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9998677,0.00003441151,0.000012774597,0.00002105183,0.000016581736,0.000047574777],"domain_scores_gemma":[0.99982965,0.00002476711,0.000030444317,0.0000070211363,0.000014286284,0.000093864925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029388568,0.00040228327,0.00054879225,0.00025623353,0.00027314996,0.00020235563,0.00014983784,0.0002893677,0.0009773872],"category_scores_gemma":[0.00046680335,0.00007574236,0.00060670107,0.00015662293,0.0001467991,0.00016816748,0.00030404693,0.0003492045,0.000083036546],"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":[0.151467,0.12706275,0.2067928,0.0025001671,0.0026311355,0.0010760234,0.00053302443,0.0014979795,0.021520006,0.00012378076,0.0015997789,0.4831956],"study_design_scores_gemma":[0.0119279185,0.19947691,0.7810747,0.00018983705,0.0014588177,0.00040841434,0.00029191817,0.00092834944,0.0028029422,0.00010484297,0.001306815,0.000028579661],"about_ca_topic_score_codex":0.0006314062,"about_ca_topic_score_gemma":0.001671557,"teacher_disagreement_score":0.0009773872,"about_ca_system_score_codex":0.00014925931,"about_ca_system_score_gemma":0.0003037989,"threshold_uncertainty_score":0.003269732},"labels":[],"label_agreement":null},{"id":"W4224265906","doi":"10.1155/2022/6056502","title":"The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Cardiovascular Health and Risk Factors","field":"Medicine","cited_by":1,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Education Department of Jilin Province; People's Government of Jilin Province","keywords":"Stenosis; Medicine; Internal carotid artery; Internal medicine; Cardiology; Montreal Cognitive Assessment; Cognition; Common carotid artery; Radiology; Carotid arteries; Cognitive impairment; Psychiatry; Disease","score_opus":0.01149980321510331,"score_gpt":0.24479974651094555,"score_spread":0.23329994329584225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224265906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982982,0.0006931016,0.00027915125,0.000037717058,0.000009244954,0.0000151749955,0.000055606644,0.0000034194788,0.00060835975],"genre_scores_gemma":[0.9993618,0.00027186872,0.00014091645,0.000009682741,0.000022978324,0.0000064760206,0.000069215304,8.4683853e-7,0.00011612626],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99967134,0.00007186627,0.000051784562,0.00005535048,0.00008715986,0.000062454696],"domain_scores_gemma":[0.9992398,0.00016864846,0.00033235672,0.00002980194,0.00012321031,0.000106273386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038815473,0.00030071952,0.00025431515,0.001687868,0.00034493164,0.0003878553,0.00016835568,0.00020975767,0.00095905364],"category_scores_gemma":[0.001703393,0.00022137987,0.00038437746,0.000780617,0.0003087852,0.00042492087,0.00043187165,0.0002499712,0.00012894378],"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":[0.00010975434,0.000030262427,0.996276,0.000019425184,0.000048727303,0.00029106325,0.000075690165,0.00003738686,0.00025735406,0.000014395868,0.000053468626,0.0027865951],"study_design_scores_gemma":[0.000007433813,0.00021489036,0.99709797,0.000008582263,0.00008092166,0.001431271,0.00024016046,0.0005908733,0.00014285995,0.000041641117,0.00013633196,0.000007162745],"about_ca_topic_score_codex":0.0029559627,"about_ca_topic_score_gemma":0.0036135367,"teacher_disagreement_score":0.0029559627,"about_ca_system_score_codex":0.00024090114,"about_ca_system_score_gemma":0.00031895717,"threshold_uncertainty_score":0.0058775544},"labels":[],"label_agreement":null},{"id":"W4225592385","doi":"10.1155/2022/5340064","title":"Identification Level of Awareness and Knowledge of Emirati Men about HPV","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Cervical Cancer and HPV Research","field":"Medicine","cited_by":4,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hashemite University; University of Jordan; Concordia University","keywords":"Cervical cancer; Vaccination; Human papillomavirus; Family medicine; Medicine; HPV vaccines; HPV infection; Cancer; Gynecology; Immunology; Internal medicine","score_opus":0.10267146308860342,"score_gpt":0.4001523346711701,"score_spread":0.2974808715825667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225592385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99281365,0.00061246305,0.00008139152,0.0004647482,0.000015266216,0.00001728311,0.000119076045,0.0000021370374,0.005873979],"genre_scores_gemma":[0.9981336,0.00055003277,0.000086752756,0.00012800723,0.000009309701,0.000007981948,0.000055231245,7.3265306e-7,0.0010283979],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996055,0.00008093205,0.00003906969,0.000037372618,0.00012450531,0.000112673784],"domain_scores_gemma":[0.999158,0.00026901165,0.00025273353,0.000038401613,0.00018852965,0.00009324639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076081493,0.00020505891,0.0002362385,0.0005938106,0.00062302925,0.0006335685,0.00021547452,0.00052371976,0.005656019],"category_scores_gemma":[0.0031137723,0.00016998907,0.000256727,0.00046116894,0.0003445106,0.0007079988,0.0004337152,0.00041676516,0.0005871777],"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":[0.00006290928,0.00018613345,0.9584365,0.00021339532,0.000034828252,0.0004113626,0.01376823,0.00006211639,0.00072285155,0.00040423006,0.0007649884,0.024932506],"study_design_scores_gemma":[0.0000036590804,0.00025322734,0.97694916,0.00019981808,0.000042830397,0.00077071786,0.016378332,0.00019038303,0.0003272099,0.00015770172,0.0047138436,0.000013074499],"about_ca_topic_score_codex":0.005222132,"about_ca_topic_score_gemma":0.00652601,"teacher_disagreement_score":0.005656019,"about_ca_system_score_codex":0.0003031849,"about_ca_system_score_gemma":0.00061887206,"threshold_uncertainty_score":0.018921316},"labels":[],"label_agreement":null},{"id":"W4311033765","doi":"10.1155/2022/8538700","title":"Altered Structural and Functional Abnormalities of Hippocampus in Classical Trigeminal Neuralgia: A Combination of DTI and fMRI Study","year":2022,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Trigeminal Neuralgia and Treatments","field":"Medicine","cited_by":17,"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":"Natural Science Foundation of Jiangxi Province; National Health Commission of the People's Republic of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Trigeminal neuralgia; Neuroscience; Hippocampus; Medicine; Functional connectivity; Psychology","score_opus":0.019696347624072793,"score_gpt":0.27679119612912284,"score_spread":0.25709484850505004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311033765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99737,0.0012790085,0.0006707995,0.000034627647,0.000008196018,0.000023904138,0.00011964934,0.000008291999,0.00048560824],"genre_scores_gemma":[0.99874794,0.0003451407,0.00065367843,0.0000230922,0.000016061995,0.000010637765,0.000086911175,0.0000016509476,0.00011490704],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998872,0.000015593085,0.000013791553,0.000033984157,0.000028374852,0.000020987933],"domain_scores_gemma":[0.9998522,0.000018194214,0.00006238794,0.000015096716,0.0000214682,0.00003071447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002899247,0.0004155225,0.00024011276,0.0009539974,0.00021587202,0.00027075756,0.00018097599,0.0002871778,0.0008413843],"category_scores_gemma":[0.0005437292,0.00015623958,0.00019980266,0.0003825863,0.0003792144,0.00032812782,0.0002650791,0.0001539383,0.000093358634],"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":[0.0037868929,0.00022800518,0.7081693,0.0005986348,0.00064291974,0.011214528,0.00048257838,0.00061487884,0.2215227,0.00020437688,0.00044618433,0.052089054],"study_design_scores_gemma":[0.000043481494,0.00028581326,0.9836759,0.00002576321,0.00010335155,0.010870846,0.00019544117,0.0006978852,0.0036232197,0.00018442108,0.00027978147,0.000014070864],"about_ca_topic_score_codex":0.0024697245,"about_ca_topic_score_gemma":0.0062799254,"teacher_disagreement_score":0.0024697245,"about_ca_system_score_codex":0.00024818632,"about_ca_system_score_gemma":0.00019428704,"threshold_uncertainty_score":0.0049107075},"labels":[],"label_agreement":null},{"id":"W4318692996","doi":"10.1155/2023/4258362","title":"Development and Evaluation of BLE‐Based Room‐Level Localization to Improve Hand Hygiene Performance Estimation","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":7,"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 Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Health Research Foundation","keywords":"Hygiene; Computer science; Beacon; Transmission (telecommunications); Health care; Binary number; Artificial intelligence; Medicine; Real-time computing; Pathology; Mathematics; Telecommunications","score_opus":0.03385854235621909,"score_gpt":0.27779613226477745,"score_spread":0.24393758990855835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318692996","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":[0.268486,0.0006014621,0.725359,0.00013015793,0.00011359692,0.0001399895,0.00030343604,0.0024484005,0.002417968],"genre_scores_gemma":[0.8219539,0.00029908514,0.17511801,0.000057923568,0.000023802932,0.000111821304,0.00047362258,0.000037037687,0.0019247886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.00013113431,0.000022350787,0.00009201531,0.00013559159,0.00003771852],"domain_scores_gemma":[0.9992036,0.00022561742,0.000056750483,0.00006494678,0.00040696893,0.0000421296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067301444,0.00044060563,0.00047380806,0.00071612763,0.00012702741,0.00032667612,0.00057737227,0.0005946389,0.0009971196],"category_scores_gemma":[0.0016713311,0.00014672494,0.0002866638,0.00037723078,0.00011829626,0.0004892486,0.00026595604,0.00021838154,0.0004885638],"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":[0.0008022013,0.0005702209,0.019995788,0.00040121985,0.00022279397,0.00016060277,0.00011581493,0.12636378,0.163713,0.0013045965,0.002308242,0.68404174],"study_design_scores_gemma":[0.00003723457,0.0008879786,0.016436562,0.000020486312,0.00006792922,0.00016809486,0.000061173894,0.93623257,0.04400338,0.0002096864,0.0018414846,0.00003357351],"about_ca_topic_score_codex":0.0017703967,"about_ca_topic_score_gemma":0.0019249511,"teacher_disagreement_score":0.0017703967,"about_ca_system_score_codex":0.00019036848,"about_ca_system_score_gemma":0.000258253,"threshold_uncertainty_score":0.0035592914},"labels":[],"label_agreement":null},{"id":"W4321016468","doi":"10.1155/2023/4301745","title":"Detection of COVID‐19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"COVID-19 diagnosis using AI","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":false,"ca_institutions":"Lakehead University","funders":"Khulna University; King Saud University","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Computer science; Coronavirus disease 2019 (COVID-19); Pattern recognition (psychology); Image registration; Machine learning; Infectious disease (medical specialty); Pathology; Medicine; Image (mathematics); Disease","score_opus":0.0427888264543686,"score_gpt":0.33066804658198146,"score_spread":0.28787922012761286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321016468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55286473,0.002567416,0.42856008,0.00088204106,0.0003560537,0.00029774394,0.0015178917,0.005263837,0.0076901885],"genre_scores_gemma":[0.89495146,0.0011565747,0.09537423,0.00032461167,0.000066362234,0.00009516237,0.002414163,0.00008741163,0.005530139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974257,0.000024697169,0.000020544265,0.0000733958,0.000086361804,0.000052398314],"domain_scores_gemma":[0.9998216,0.000038699556,0.000024914998,0.000023332754,0.0000686179,0.000022877093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003655379,0.00094199966,0.00043158748,0.001373819,0.00019989107,0.00046593265,0.0007847878,0.00073037733,0.0014492646],"category_scores_gemma":[0.0008513733,0.00032586584,0.0006416732,0.0003649133,0.00022685598,0.0005221556,0.0005141407,0.00043358703,0.00046649223],"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":[0.0007088539,0.0004942872,0.028942581,0.00024450631,0.00026830778,0.0018771434,0.000108731976,0.21260847,0.08860511,0.0010716397,0.008533776,0.65653664],"study_design_scores_gemma":[0.000009209857,0.00008911067,0.0059605455,0.000016697762,0.00004144608,0.00028262057,0.000022014028,0.9778292,0.014457498,0.00036900048,0.00090288033,0.000019614705],"about_ca_topic_score_codex":0.0110303955,"about_ca_topic_score_gemma":0.013935961,"teacher_disagreement_score":0.0110303955,"about_ca_system_score_codex":0.0006209337,"about_ca_system_score_gemma":0.0005899551,"threshold_uncertainty_score":0.021932423},"labels":[],"label_agreement":null}]}