{"id":"W4376113111","doi":"10.1001/jamacardio.2023.0968","title":"Automated Assessment of Cardiac Systolic Function From Coronary Angiograms With Video-Based Artificial Intelligence Algorithms","year":2023,"lang":"en","type":"article","venue":"JAMA Cardiology","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute; University of Ottawa","funders":"National Heart, Lung, and Blood Institute","keywords":"Ejection fraction; Medicine; Receiver operating characteristic; Cardiology; Coronary artery disease; Internal medicine; Odds ratio; Algorithm; Heart failure; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001442549,0.000465617,0.0003072693,0.001643627,0.0001191376,0.0006477198,0.000503581,0.0005112783,0.0007703912],"category_scores_gemma":[0.005692548,0.0001591172,0.0002963891,0.0005305113,0.0001800187,0.0004622857,0.0003300552,0.0003684874,0.0003081647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004208871,"about_ca_system_score_gemma":0.0004085032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003947922,"about_ca_topic_score_gemma":0.00861471,"domain_scores_codex":[0.9992231,0.0003404164,0.0000608248,0.0001724477,0.0001638184,0.00003938287],"domain_scores_gemma":[0.998178,0.0007520291,0.0004150702,0.0001312263,0.0004420734,0.00008165994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006462958,0.000719707,0.6809116,0.000140509,0.000311428,0.0001226474,0.0000925387,0.02454868,0.005590699,0.0002408421,0.003016348,0.2836588],"study_design_scores_gemma":[0.0000773365,0.0007867677,0.4353597,0.00008794065,0.0001127553,0.0003154094,0.0001297954,0.5564394,0.004693719,0.0007085658,0.001246365,0.00004218739],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9292988,0.0009329442,0.06427017,0.0003279302,0.00005255206,0.0002475347,0.002491413,0.0003977628,0.001980915],"genre_scores_gemma":[0.9580155,0.000218685,0.03897987,0.00008799773,0.00005543029,0.0001272223,0.002027372,0.00001056624,0.0004773587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003947922,"threshold_uncertainty_score":0.007849872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02525819572581778,"score_gpt":0.291844820311995,"score_spread":0.2665866245861773,"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."}}