{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006183639,0.00009901642,0.0002842871,0.0001909457,0.0001024513,0.0000178501,0.00006249572,0.0001208016,0.0002231285],"category_scores_gemma":[0.0004468324,0.00009369621,0.0001114085,0.0006517154,0.00008158699,0.00006668393,0.00006233153,0.0006716873,0.0002435382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008213601,"about_ca_system_score_gemma":0.0001653996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001157099,"about_ca_topic_score_gemma":0.000004205205,"domain_scores_codex":[0.99827,0.0003323655,0.0001971126,0.0003572728,0.0004711685,0.0003720929],"domain_scores_gemma":[0.9992753,0.0001232999,0.00002321829,0.0002428391,0.0001549243,0.0001804205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002042068,0.0001901318,0.6777179,0.000396728,0.0002411363,0.003230791,0.002101276,0.01840432,0.2748895,0.003588694,0.001816773,0.01538072],"study_design_scores_gemma":[0.003105989,0.0005763513,0.9225258,0.00007307823,0.00005325748,0.00008967723,0.000166828,0.06399819,0.004348276,0.001972246,0.002837295,0.0002530025],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985696,0.00143655,0.0003652369,0.004308338,0.00004615084,0.0005023117,0.000005350093,0.00006617052,0.00757388],"genre_scores_gemma":[0.9987664,0.00008261181,0.00004157584,0.0002787453,0.000519112,0.00002884751,0.00009521378,0.00002189714,0.0001655979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2705412,"threshold_uncertainty_score":0.382082,"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."}}