{"id":"W4387337803","doi":"10.1016/j.cjca.2023.06.329","title":"MACHINE-LEARNING FOR THE ELECTROCARDIOGRAM-BASED PREDICTION OF SHORT-TERM AND LONG-TERM MORTALITY AT THE TIME OF DISCHARGE IN A POPULATION-LEVEL COHORT OF PATIENTS WITH ACCESS TO UNIVERSAL HEALTHCARE","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Hospital Edmonton","funders":"","keywords":"Medicine; Risk stratification; Cohort; Term (time); Intensive care medicine; Acute care; Health care; Hospital discharge; Medical emergency; Emergency medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004696216,0.00007203763,0.0004289757,0.0002883406,0.00006985944,0.000004154284,0.00009536793,0.00005160352,0.000002179453],"category_scores_gemma":[0.0000998028,0.00004378805,0.0001296171,0.0003223487,0.00007947625,0.00003397238,0.00001172587,0.0001448214,6.313796e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061817,"about_ca_system_score_gemma":0.0002319105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002795943,"about_ca_topic_score_gemma":0.005696842,"domain_scores_codex":[0.9991755,0.000117611,0.0003060439,0.00009299955,0.0001471018,0.0001607171],"domain_scores_gemma":[0.9991701,0.0001234842,0.0001797328,0.0001308025,0.0002572344,0.0001387028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001741002,0.000002462039,0.9930373,0.00005157169,0.0004277427,0.00000641449,0.0000885242,0.005031656,0.00006383604,0.000001806499,0.00003203683,0.001082566],"study_design_scores_gemma":[0.0005649689,0.0004029653,0.9978812,0.0001001437,0.0003743358,0.00000700735,0.00003581609,0.0005265687,0.00004646305,0.000002225848,0.00002670667,0.00003154247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983363,0.0001315495,0.0006060427,0.0004051228,0.00006179784,0.0002818703,0.0001664171,0.000002095569,0.000008786117],"genre_scores_gemma":[0.9997124,0.00003198785,0.000009479837,0.00001520004,0.00008474093,0.000005278563,0.0001166637,0.000008817754,0.00001539751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004843973,"threshold_uncertainty_score":0.4226648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804672340488401,"score_gpt":0.2905445757490695,"score_spread":0.2624978523441855,"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."}}