{"id":"W4386407034","doi":"10.1007/s43678-023-00572-5","title":"Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review","year":2023,"lang":"en","type":"review","venue":"Canadian Journal of Emergency Medicine","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of Ottawa","funders":"","keywords":"Medicine; Emergency department; Acute coronary syndrome; Chest pain; MEDLINE; CINAHL; Receiver operating characteristic; Machine learning; Emergency medicine; Artificial intelligence; Internal medicine; Myocardial infarction; Psychological intervention; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002755134,0.0005060183,0.006115038,0.0009677065,0.0002309134,0.00000492886,0.0005626577,0.0001980633,0.0005658841],"category_scores_gemma":[0.004096701,0.0002760068,0.001898384,0.001323102,0.0001191604,0.00005425862,0.00002397582,0.0009465407,0.00001023409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003435524,"about_ca_system_score_gemma":0.001757366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003223192,"about_ca_topic_score_gemma":0.0009495083,"domain_scores_codex":[0.9948606,0.000341803,0.003458159,0.0002651555,0.0005928018,0.0004814909],"domain_scores_gemma":[0.9941746,0.0008365485,0.003059595,0.0005600734,0.0006354845,0.0007336781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000006147601,0.00001681491,0.0007388902,0.9732479,0.009337444,0.0009285493,0.0001083495,0.000006142954,4.075281e-7,0.000003149377,0.004884561,0.0107216],"study_design_scores_gemma":[0.0002792205,0.000611577,0.00001446769,0.7895574,0.1114898,0.001611198,0.0001084181,0.0002646998,7.899951e-8,0.000006480754,0.09586184,0.0001948204],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001503186,0.994132,0.0001502551,0.0006961176,0.00314403,0.001753254,0.00007442165,0.00001071176,0.00002419268],"genre_scores_gemma":[0.00008553609,0.9963759,0.00009335852,0.0000542166,0.000794187,0.0001279212,0.00006802911,0.0001214762,0.002279366],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1836906,"threshold_uncertainty_score":0.9999692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2458001122033285,"score_gpt":0.4256058514404084,"score_spread":0.1798057392370799,"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."}}