{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001955995,0.0001820076,0.0006995162,0.0007275091,0.0001146066,0.00005010812,0.0001073251,0.0000443102,0.0000974455],"category_scores_gemma":[0.001138773,0.0001460581,0.00120226,0.0006297928,0.00001410359,0.000176989,0.0001002994,0.0002827941,0.0000499989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523675,"about_ca_system_score_gemma":0.0001443942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003109842,"about_ca_topic_score_gemma":0.000002617911,"domain_scores_codex":[0.9977241,0.0001447711,0.0006427619,0.0001753344,0.0009297801,0.0003832537],"domain_scores_gemma":[0.9988876,0.0001946971,0.0002103544,0.0002168806,0.0001205302,0.0003699311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007566817,0.00009388255,0.9836438,0.00009111565,0.001312301,0.003257294,0.0002168119,0.003195437,0.002695308,0.0000025269,0.002322962,0.003092843],"study_design_scores_gemma":[0.001427527,0.0001882612,0.946503,0.0007533304,0.0009824724,0.00017877,0.0004232916,0.006745246,0.0001765851,0.00002584193,0.0423003,0.0002953],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961728,0.0003945762,0.0008658341,0.001345345,0.0008767862,0.0001485079,0.000001783528,0.00008314157,0.0001112387],"genre_scores_gemma":[0.9973139,0.00004823433,0.001172752,0.0007055298,0.0003105509,0.000003275987,0.000009558264,0.00004499217,0.0003912791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03997734,"threshold_uncertainty_score":0.5956077,"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."}}