{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003593121,0.0001597805,0.000592023,0.0001704039,0.00006524054,0.00000518906,0.0000945604,0.0000470836,0.000001804357],"category_scores_gemma":[0.0006427734,0.0001583667,0.0001374287,0.000138099,0.0001167248,0.0000819732,0.0002481265,0.0003252036,0.000001573352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001063082,"about_ca_system_score_gemma":0.0002307987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001219205,"about_ca_topic_score_gemma":0.000002105156,"domain_scores_codex":[0.9986881,0.0002254137,0.0002000472,0.0004910505,0.0001258077,0.0002695505],"domain_scores_gemma":[0.9989272,0.000219774,0.00003184936,0.0006532883,0.00003252899,0.0001353627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001719846,0.00003376461,0.9625084,0.00008849674,0.000647313,0.000580887,0.0000689895,0.03181509,0.00004372015,0.00005112768,0.0002056241,0.003784596],"study_design_scores_gemma":[0.001683601,0.00002451407,0.717814,0.0001545088,0.0009252733,0.000423758,0.00004837647,0.2773874,0.000003868211,0.0004803362,0.0009300816,0.0001242864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527901,0.04015397,0.00540626,0.0002232625,0.0004483532,0.000389863,0.0001880651,0.00005360307,0.0003464691],"genre_scores_gemma":[0.9970421,0.001166726,0.0009783616,0.0001797307,0.0001064021,0.00001297118,0.0004088492,0.00001822902,0.00008667244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2455723,"threshold_uncertainty_score":0.6458007,"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."}}