{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004180118,0.0006661891,0.0009310498,0.0007989472,0.0002171389,0.001450156,0.0006092649,0.0006991546,0.001037119],"category_scores_gemma":[0.01664486,0.0001537665,0.0006397619,0.0004690044,0.0003868467,0.0009984837,0.0005205832,0.001137682,0.0003211756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002676116,"about_ca_system_score_gemma":0.0005032428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002213794,"about_ca_topic_score_gemma":0.002028209,"domain_scores_codex":[0.9988441,0.0007608137,0.00007480582,0.0001329365,0.0001225352,0.00006477091],"domain_scores_gemma":[0.988198,0.009840922,0.000618588,0.0004196167,0.0005542645,0.0003686101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001119829,0.0005616785,0.8892579,0.00007826543,0.0005847591,0.00005762423,0.00004438101,0.0185381,0.000320853,0.000381518,0.001905594,0.08714946],"study_design_scores_gemma":[0.0001011062,0.0007326985,0.662829,0.0001653502,0.000459618,0.0002902029,0.0001492034,0.3248495,0.0007134742,0.008186086,0.001463084,0.00006068045],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642048,0.01048474,0.01537308,0.003996363,0.000263879,0.0000389073,0.001090604,0.0001853505,0.00436226],"genre_scores_gemma":[0.9946752,0.001355844,0.002731293,0.0001285621,0.000254195,0.00001233398,0.0004405588,0.00001293512,0.0003889856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004180118,"threshold_uncertainty_score":0.02210683,"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."}}