{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439178,0.0001169435,0.0004571186,0.000125067,0.0001511438,0.00008595748,0.00008031005,0.0000290378,0.00002147324],"category_scores_gemma":[0.001072548,0.00007972195,0.0009337305,0.0004642513,0.00001727873,0.0002472493,0.00005549482,0.0003864629,0.0000053304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009825042,"about_ca_system_score_gemma":0.00009622345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001034512,"about_ca_topic_score_gemma":0.000004858614,"domain_scores_codex":[0.9981119,0.0003228679,0.0004168465,0.0001319548,0.0008275197,0.0001889295],"domain_scores_gemma":[0.9991608,0.000135563,0.0003681461,0.0001494814,0.0001110564,0.0000749839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001836555,0.00004593192,0.9899029,0.00003132309,0.0008521274,0.0000316066,0.000122136,0.001255472,0.006456096,0.00000229117,0.0001519897,0.001129776],"study_design_scores_gemma":[0.0005280577,0.0001026411,0.9415571,0.0004085967,0.001386317,0.00004994596,0.000115396,0.0509887,0.0001298092,0.00003052392,0.004596284,0.000106635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945899,0.0005962619,0.001202836,0.002521635,0.0008042782,0.0001730284,0.000001431987,0.00004905078,0.00006159701],"genre_scores_gemma":[0.9971952,0.00003610051,0.001371457,0.0006644104,0.0002658937,0.000001622722,0.000001783668,0.00002493709,0.0004386191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04973323,"threshold_uncertainty_score":0.3250966,"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."}}