{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0303529,0.001326289,0.001516157,0.001019252,0.0008791764,0.001401251,0.002653112,0.001976885,0.000821553],"category_scores_gemma":[0.03627478,0.0008999141,0.001870704,0.0004123533,0.000726491,0.001310183,0.002036221,0.003309924,0.0004146324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207978,"about_ca_system_score_gemma":0.002474125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.012023,"about_ca_topic_score_gemma":0.0125191,"domain_scores_codex":[0.9935998,0.004432246,0.0004866103,0.0008681579,0.0003489486,0.0002643019],"domain_scores_gemma":[0.9612471,0.03067666,0.0008757263,0.002663595,0.003920653,0.000616252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002370638,0.002042026,0.1293829,0.0001625974,0.001797149,0.0003047601,0.0006416853,0.7323182,0.003830464,0.002186052,0.003283916,0.1216795],"study_design_scores_gemma":[0.00004743354,0.0001639074,0.003448044,0.00001475749,0.00005827336,0.0000247645,0.00002583675,0.995181,0.0004686242,0.000451764,0.0001053361,0.00001036993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.749561,0.0009029723,0.2460605,0.0005027411,0.000150942,0.0003641688,0.0007275889,0.00101782,0.0007123181],"genre_scores_gemma":[0.9463682,0.0001035956,0.05075972,0.0001423721,0.00002343017,0.0001813899,0.001893168,0.00008492934,0.0004431861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0303529,"threshold_uncertainty_score":0.1605234,"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."}}