{"id":"W3105222409","doi":"10.1161/circresaha.120.317345","title":"Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death","year":2020,"lang":"en","type":"article","venue":"Circulation Research","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Cardiology; Ventricular tachycardia; Ischemic cardiomyopathy; Internal medicine; Medicine; Ventricular fibrillation; Cardiomyopathy; Ejection fraction; Coronary artery disease; Sudden cardiac death; Machine learning; Heart failure; Computer science","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":[0.0015414,0.0005640061,0.0003190347,0.0006069416,0.0001362625,0.000544815,0.0003058054,0.0004412089,0.0008903853],"category_scores_gemma":[0.007954277,0.000121125,0.0002608374,0.0003032673,0.0005123058,0.0003656245,0.000394531,0.0005149313,0.0002707085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002971123,"about_ca_system_score_gemma":0.0003029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007171736,"about_ca_topic_score_gemma":0.0006522338,"domain_scores_codex":[0.9996032,0.0001875087,0.00003347727,0.00008972183,0.00005170573,0.00003442873],"domain_scores_gemma":[0.9968022,0.002083186,0.0005380727,0.0002029245,0.0002222997,0.0001512782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006863222,0.0001977868,0.879678,0.00006154709,0.0001740389,0.000182614,0.00009917847,0.08203023,0.004615998,0.0003699657,0.0005138353,0.03139041],"study_design_scores_gemma":[0.00005139412,0.0006448643,0.5042565,0.00004954591,0.00007913316,0.0004109744,0.00009921887,0.4858625,0.003969663,0.004250244,0.0002982603,0.00002758043],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881897,0.0001273156,0.01037368,0.0002538429,0.00001241966,0.00002965253,0.0004969929,0.00007374972,0.0004427145],"genre_scores_gemma":[0.9982336,0.00001882997,0.001370619,0.00002419294,0.000007353161,0.00001123455,0.0002848061,0.000002344912,0.00004701432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0015414,"threshold_uncertainty_score":0.008151829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07754122470794246,"score_gpt":0.3476845484717739,"score_spread":0.2701433237638315,"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."}}