{"id":"W4406366944","doi":"10.1148/radiol.233030","title":"A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease","year":2025,"lang":"en","type":"article","venue":"Radiology","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Coronary artery disease; Cardiology; Internal medicine; Disease; Radiology","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":[0.00533175,0.0008645493,0.0007477906,0.001486441,0.0003125684,0.0014594,0.0008294248,0.000751355,0.004756153],"category_scores_gemma":[0.01996296,0.0002446015,0.0008967043,0.0006860854,0.0003843921,0.00106521,0.0007877042,0.0013978,0.001506255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006015452,"about_ca_system_score_gemma":0.00085781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00295384,"about_ca_topic_score_gemma":0.00243329,"domain_scores_codex":[0.9984957,0.0006720381,0.0001348205,0.0003765998,0.0002207086,0.0001001668],"domain_scores_gemma":[0.9942088,0.004298942,0.0004249984,0.0003382447,0.0005751128,0.0001539245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002454365,0.001185333,0.5602179,0.0002270304,0.001490001,0.0006187769,0.0002083891,0.2088522,0.002731953,0.001630461,0.01232773,0.2080559],"study_design_scores_gemma":[0.0001202429,0.0004604041,0.0535928,0.0001037306,0.0002199359,0.0005072691,0.00007845518,0.9391487,0.0008271773,0.003307173,0.001596288,0.0000377291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8780762,0.002092168,0.1040521,0.00424494,0.0005902941,0.0002011299,0.003459756,0.001402409,0.005881032],"genre_scores_gemma":[0.9882348,0.0001846549,0.00864868,0.0002168003,0.0001804343,0.00007274863,0.0009444304,0.00002586771,0.001491524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00533175,"threshold_uncertainty_score":0.02819735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100117846225245,"score_gpt":0.2756784396312842,"score_spread":0.2546772611690318,"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."}}