{"id":"W3214762646","doi":"10.1093/ehjdh/ztab101","title":"Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; McMaster University; Population Health Research Institute","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institutes of Health; National Center for Advancing Translational Sciences; Icahn School of Medicine at Mount Sinai","keywords":"Medicine; Pulmonary embolism; Receiver operating characteristic; Artificial intelligence; Retrospective cohort study; Cohort; Internal medicine; Electrocardiography; Cardiology; Machine learning","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":[0.001084858,0.0002734978,0.0008643495,0.0002876309,0.0004540596,0.000185825,0.0001303656,0.00003245948,0.00002387645],"category_scores_gemma":[0.0000918346,0.0002562641,0.0003048922,0.0004214648,0.00002432105,0.0002728847,0.0002772575,0.0005955782,0.00003356706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002013405,"about_ca_system_score_gemma":0.0007528917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001040229,"about_ca_topic_score_gemma":0.000001687589,"domain_scores_codex":[0.9970506,0.0001863436,0.0009954395,0.0003965524,0.0006297564,0.0007412385],"domain_scores_gemma":[0.9983633,0.00003517573,0.0002998353,0.000248463,0.000263011,0.0007901909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005377625,0.00259248,0.005075375,0.0009314668,0.002105891,0.003972861,0.005642143,0.03416105,0.09450551,0.0001045997,0.003300468,0.8470704],"study_design_scores_gemma":[0.01298623,0.01239223,0.1631827,0.0158615,0.001914094,0.04013784,0.006693776,0.2194841,0.03891386,0.0006255837,0.4827473,0.00506078],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9016764,0.00259217,0.08629492,0.002389708,0.0002157145,0.0006881857,0.0000296779,0.0001116392,0.006001545],"genre_scores_gemma":[0.9218962,0.0003322772,0.07392862,0.002843856,0.000293576,0.000003372858,0.0000441744,0.0001038012,0.0005540706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8420096,"threshold_uncertainty_score":0.999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05031416966398888,"score_gpt":0.3297902879970327,"score_spread":0.2794761183330439,"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."}}