{"id":"W4408963342","doi":"10.1016/s0735-1097(25)02418-0","title":"DEVELOPMENT AND NESTED CROSS-VALIDATION OF EXPLAINABLE MACHINE-LEARNING MODELS FOR IDENTIFYING OBSTRUCTIVE CORONARY ARTERY DISEASE IN PATIENTS PRESENTING WITH ANGINA","year":2025,"lang":"en","type":"article","venue":"Journal of the American College of Cardiology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Coronary artery disease; Cardiology; Angina; Internal medicine; Disease; Myocardial infarction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005468379,0.00009904234,0.0004393382,0.0002468896,0.0001237872,0.00001415243,0.0003811363,0.00002305229,1.381888e-7],"category_scores_gemma":[0.000273977,0.00007377651,0.0000752378,0.0004286972,0.0001462227,0.0002751508,0.0002241068,0.0002013274,1.70124e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009021343,"about_ca_system_score_gemma":0.0002870371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001655996,"about_ca_topic_score_gemma":0.000002322729,"domain_scores_codex":[0.9985889,0.0003685023,0.0004961577,0.0001633791,0.0002081135,0.0001749377],"domain_scores_gemma":[0.9980903,0.0003096868,0.0009676715,0.0001796829,0.0004091933,0.00004341902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003558834,0.00001290418,0.8613606,0.000114502,0.00007313504,0.00001548986,0.0003422931,0.1361809,0.00002541902,0.0005613013,0.000005284096,0.0009523248],"study_design_scores_gemma":[0.0008485298,0.0001903842,0.9580449,0.0001318069,0.00002314458,0.00003423631,0.0002494504,0.03922391,0.0001915888,0.0009286814,0.00006245079,0.00007085866],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942817,0.0001029979,0.05645502,0.0001743241,0.0001421072,0.0002637644,0.000007142127,0.000004865422,0.00003278141],"genre_scores_gemma":[0.9787917,0.000006292417,0.02111654,0.00002210521,0.00001419713,0.000009819286,0.000001316754,0.000006333775,0.00003173455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09695695,"threshold_uncertainty_score":0.3008519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02059460347108633,"score_gpt":0.2890618828646918,"score_spread":0.2684672793936054,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}