{"id":"W4387425812","doi":"10.1161/jaha.123.030508","title":"Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Journal of the American Heart Association","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Artificial Intelligence in Medicine (Canada); University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; University of Toronto; King Faisal Specialist Hospital and Research Centre; American University of Beirut; Physicians' Services Incorporated Foundation; Royal College of Surgeons in Ireland; Alfaisal University; Brigham and Women's Hospital","keywords":"Medicine; Brier score; Receiver operating characteristic; Carotid endarterectomy; Area under the curve; Stroke (engine); Logistic regression; Adverse effect; Perioperative; Myocardial infarction; Metric (unit); Emergency medicine; Machine learning; Internal medicine; Surgery; Carotid arteries","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.002774011,0.0007193818,0.0006771165,0.001830394,0.0001796127,0.0007770313,0.0004662095,0.0004568376,0.0004580655],"category_scores_gemma":[0.008429602,0.0001731485,0.0007729583,0.000900896,0.0002328358,0.0004618215,0.0004843571,0.0007468637,0.0001532288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005172433,"about_ca_system_score_gemma":0.0006952729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002899772,"about_ca_topic_score_gemma":0.002689993,"domain_scores_codex":[0.9989398,0.0004072831,0.00012593,0.0002164701,0.0001883889,0.0001221792],"domain_scores_gemma":[0.9928658,0.004817885,0.001131344,0.0002046058,0.0006989044,0.0002814049],"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":[0.0005695306,0.0003462909,0.8617855,0.0001283991,0.0004955743,0.0001681081,0.00004381266,0.06182841,0.0007277186,0.0001462233,0.001322756,0.07243776],"study_design_scores_gemma":[0.00006953587,0.001008808,0.3623258,0.00009324069,0.0003566875,0.000559354,0.00007920619,0.6292926,0.002773287,0.002524659,0.0008602159,0.00005665607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663929,0.001573937,0.02926912,0.000569957,0.00007734747,0.00008285188,0.0008569473,0.0002301582,0.0009468243],"genre_scores_gemma":[0.9929624,0.0003167743,0.005670195,0.00005740295,0.00005650308,0.0000310553,0.000779308,0.000005943217,0.0001204394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002899772,"threshold_uncertainty_score":0.01467055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157565756446956,"score_gpt":0.2636503485407469,"score_spread":0.2520746909762774,"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."}}