{"id":"W4224089927","doi":"10.1016/j.mayocp.2022.01.016","title":"Prediction of Mortality in Coronary Artery Disease: Role of Machine Learning and Maximal Exercise Capacity","year":2022,"lang":"en","type":"article","venue":"Mayo Clinic Proceedings","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Libin Cardiovascular Institute of Alberta; Total (Canada)","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Medicine; Coronary artery disease; Cardiology; Internal medicine; Disease","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":[0.0006707411,0.0001116607,0.0005766189,0.0001236766,0.0000551273,0.000002530449,0.00005956883,0.00004944673,0.0001585438],"category_scores_gemma":[0.0001080903,0.0001139925,0.0001849535,0.0001899347,0.0001325368,0.00006986946,0.0001683183,0.0003746616,0.000001199926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004075161,"about_ca_system_score_gemma":0.00004529832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001140608,"about_ca_topic_score_gemma":0.00000151846,"domain_scores_codex":[0.9988109,0.00002644997,0.0004304856,0.0002851661,0.0002846094,0.0001623679],"domain_scores_gemma":[0.9995527,0.00002861739,0.0001483706,0.0001061344,0.00006829582,0.00009584513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003122956,0.0003396265,0.980951,0.0004952931,0.0001057797,0.00001444161,0.0007523201,0.00001436785,0.01047267,0.00003275777,0.00006175919,0.003637095],"study_design_scores_gemma":[0.00159008,0.0003956073,0.9926698,0.0001036832,0.0003085983,0.0000595418,0.0008642237,0.002084547,0.0007418684,0.0008088818,0.0002920009,0.00008120055],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971681,0.001677067,0.000002188516,0.00004017744,0.00006686457,0.000333233,0.00007893548,0.00003046514,0.0006029615],"genre_scores_gemma":[0.999209,0.0004637969,0.0000464546,0.00002239745,0.00003821237,0.00006084876,0.00005555985,0.000014854,0.00008887121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01171884,"threshold_uncertainty_score":0.464848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02607466078981498,"score_gpt":0.2616911713752919,"score_spread":0.2356165105854769,"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."}}