{"id":"W4387933992","doi":"10.1016/j.jcjd.2023.10.332","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 Diabetes","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); Acute care; Intensive care medicine; Hospital discharge; Health care; Population; Medical emergency; Emergency medicine; Cardiology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049371,0.0005599759,0.0009630403,0.001190655,0.0004603318,0.001224198,0.001130728,0.0009718816,0.001625953],"category_scores_gemma":[0.01561913,0.0002809793,0.0009777714,0.0007271061,0.00032025,0.0007322259,0.0008349697,0.001815639,0.0003297841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007060317,"about_ca_system_score_gemma":0.001002799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01452601,"about_ca_topic_score_gemma":0.01130939,"domain_scores_codex":[0.9987625,0.0006652465,0.00008366838,0.0002477974,0.00009941609,0.0001413392],"domain_scores_gemma":[0.9938149,0.00440118,0.0005776873,0.000433768,0.0003957915,0.0003766457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001575775,0.0005859818,0.9486695,0.00003384185,0.0006246641,0.0001900842,0.00009405006,0.02258245,0.0003770562,0.000533107,0.001490477,0.02324298],"study_design_scores_gemma":[0.0001309314,0.000441342,0.3159019,0.000032191,0.0001889873,0.0002394536,0.0001872635,0.679958,0.0002656154,0.00222382,0.0003943908,0.00003593975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923021,0.0003208634,0.005506318,0.0005540623,0.00004594319,0.0000345501,0.0008956074,0.00004169505,0.0002989428],"genre_scores_gemma":[0.9960521,0.0001157923,0.002270596,0.00004965872,0.0000432528,0.00003200493,0.001103068,0.000006620266,0.0003268449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01452601,"threshold_uncertainty_score":0.02888292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02292944907560438,"score_gpt":0.2749086696722814,"score_spread":0.251979220596677,"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."}}