{"id":"W4382631851","doi":"10.1097/sla.0000000000005978","title":"Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Annals of Surgery","topic":"Aortic aneurysm repair treatments","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Brier score; Abdominal aortic aneurysm; Receiver operating characteristic; Logistic regression; Perioperative; Endovascular aneurysm repair; Area under the curve; Aneurysm; Aortic aneurysm; Surgery; Internal medicine; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002944802,0.0007512391,0.0005156904,0.001063734,0.0001463641,0.0006973257,0.0005207614,0.0004129542,0.0006145733],"category_scores_gemma":[0.01057815,0.0001407759,0.0005339732,0.0004775249,0.0002215201,0.0005992505,0.0005477367,0.0006810075,0.0002535139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004610854,"about_ca_system_score_gemma":0.0006794005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002121113,"about_ca_topic_score_gemma":0.00247817,"domain_scores_codex":[0.9992468,0.0003079717,0.00006651392,0.0001618634,0.0001371754,0.00007964425],"domain_scores_gemma":[0.9963815,0.002089146,0.000896839,0.0001179145,0.0003880462,0.0001264343],"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":[0.0006800935,0.0006611944,0.7818008,0.0001873474,0.0004093312,0.0001735819,0.00007591257,0.1294311,0.0007062061,0.000197751,0.001804986,0.08387163],"study_design_scores_gemma":[0.00007255989,0.001083003,0.2249104,0.0001322181,0.0001665919,0.0002699133,0.00009025911,0.7686419,0.002047037,0.001770718,0.0007805746,0.00003469864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650824,0.0008973064,0.03128835,0.0006600111,0.00005590967,0.00008597431,0.001026786,0.0001992117,0.000704094],"genre_scores_gemma":[0.991683,0.0001923722,0.00663183,0.00008084089,0.0000473576,0.00004517265,0.001130844,0.000008143754,0.00018037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002944802,"threshold_uncertainty_score":0.01557374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1691269736329688,"score_gpt":0.3611429084916992,"score_spread":0.1920159348587304,"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."}}