{"id":"W4387378676","doi":"10.1016/j.jvs.2023.09.037","title":"Using machine learning to predict outcomes following suprainguinal bypass","year":2023,"lang":"en","type":"article","venue":"Journal of Vascular Surgery","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Physicians' Services Incorporated Foundation","keywords":"Medicine; Logistic regression; Brier score; Receiver operating characteristic; Thrombolysis; Machine learning; Internal medicine","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":[0.001892312,0.0004585146,0.0006345378,0.0009657955,0.0003342206,0.001158995,0.0005662158,0.0008590876,0.001096895],"category_scores_gemma":[0.008594907,0.0001877989,0.0006605076,0.0004803107,0.0003187535,0.0008161457,0.0005679487,0.00168532,0.0003771431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004590085,"about_ca_system_score_gemma":0.0005938222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001539146,"about_ca_topic_score_gemma":0.002389695,"domain_scores_codex":[0.9992651,0.0002683768,0.00009044117,0.0001147312,0.0001215754,0.0001396765],"domain_scores_gemma":[0.9944454,0.003245494,0.001021803,0.000222429,0.000492945,0.0005720793],"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":[0.00119587,0.00063729,0.9696903,0.00003919224,0.0002902831,0.0001265876,0.00004093352,0.004292418,0.0002910842,0.0001380733,0.0009423195,0.02231569],"study_design_scores_gemma":[0.0001250152,0.002229777,0.8154918,0.0001093953,0.0005625368,0.0005826231,0.0004215538,0.1749813,0.001322199,0.003192143,0.0009212999,0.00006038159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960443,0.0004889585,0.001783528,0.0004327204,0.0001250905,0.00001615468,0.0003390846,0.00002317254,0.000746922],"genre_scores_gemma":[0.9980703,0.0001961438,0.0006783191,0.00006014975,0.0001001583,0.0000143764,0.0006408083,0.000005414614,0.000234344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001892312,"threshold_uncertainty_score":0.01000762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04204145220047227,"score_gpt":0.3105240282300722,"score_spread":0.2684825760296,"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."}}