{"id":"W4233775599","doi":"10.22541/au.163310766.62405219/v1","title":"Machine Learning as a New Frontier in Mitral Valve Surgical Strategy","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network; Vector Institute; University of Toronto","funders":"","keywords":"Mitral regurgitation; Machine learning; Artificial intelligence; Medicine; Mitral valve repair; Internal medicine; Cardiology; Mitral valve; Clinical Practice; Revascularization; Computer science; Physical therapy; Myocardial infarction","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":[0.009482013,0.0006831394,0.001333222,0.001770744,0.0004973903,0.004502578,0.00124837,0.002880436,0.002722087],"category_scores_gemma":[0.01962865,0.0003738799,0.000617642,0.001245874,0.004264985,0.005777871,0.001780446,0.006012834,0.001010712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001707571,"about_ca_system_score_gemma":0.001564413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199453,"about_ca_topic_score_gemma":0.0008126476,"domain_scores_codex":[0.9972785,0.001628696,0.0001303935,0.0003106437,0.0005137568,0.0001380733],"domain_scores_gemma":[0.9785333,0.01792937,0.0006075489,0.0009599596,0.001351608,0.0006181601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000185219,0.0002155198,0.005439851,0.0006026619,0.0001859693,0.0001573065,0.0002314179,0.03179854,0.0003901899,0.4632867,0.039761,0.4577458],"study_design_scores_gemma":[0.00005255555,0.0001338507,0.001264115,0.0005221406,0.00002321525,0.0000932031,0.0001353784,0.08214089,0.0002863832,0.8592597,0.05603048,0.00005811203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01741742,0.2445075,0.3088405,0.3912397,0.005653994,0.00007850373,0.0004852016,0.000599042,0.0311782],"genre_scores_gemma":[0.539398,0.1616752,0.2302134,0.02298679,0.0335153,0.0002437816,0.000598504,0.0003148056,0.01105415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009482013,"threshold_uncertainty_score":0.05014628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065738474415939,"score_gpt":0.3498417679629868,"score_spread":0.3291843832188274,"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."}}