{"id":"W4394954598","doi":"10.1161/jaha.123.033194","title":"Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Journal of the American Heart Association","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":13,"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; Physicians' Services Incorporated Foundation; Brigham and Women's Hospital","keywords":"Medicine; Receiver operating characteristic; Brier score; Revascularization; Adverse effect; Perioperative; Angioplasty; Surgery; Internal medicine; Machine learning; Myocardial infarction","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":[0.002594376,0.0005738807,0.0004564923,0.001373744,0.0001446489,0.0006052139,0.0004590864,0.0003550343,0.0004497587],"category_scores_gemma":[0.008557092,0.0001381654,0.0004383453,0.0006176974,0.0002821239,0.0004134956,0.0004585513,0.0005737572,0.0001804391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004792634,"about_ca_system_score_gemma":0.0006158829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001819839,"about_ca_topic_score_gemma":0.002037163,"domain_scores_codex":[0.9990657,0.0004035747,0.0000901041,0.0001741815,0.0001713789,0.00009508126],"domain_scores_gemma":[0.9938677,0.003662396,0.001420623,0.0002179905,0.0006269987,0.0002042218],"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":[0.0003209634,0.0003393648,0.8751112,0.0000705172,0.000192976,0.0001197973,0.00004899639,0.06780985,0.0005885932,0.0001248819,0.0008667591,0.05440608],"study_design_scores_gemma":[0.00005515217,0.0006948035,0.3664724,0.00008316879,0.0001291588,0.000384617,0.00006744033,0.6263822,0.002560442,0.00237378,0.0007586256,0.00003820687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773828,0.0005147241,0.02035847,0.0002715128,0.00002404028,0.00006344514,0.0006346521,0.0001191485,0.0006312891],"genre_scores_gemma":[0.9931718,0.0001378753,0.005643917,0.00003738343,0.00003203911,0.00002665128,0.00085853,0.000004382371,0.00008747741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002594376,"threshold_uncertainty_score":0.01372057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281152561746122,"score_gpt":0.281593203373948,"score_spread":0.2687816777564868,"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."}}