{"id":"W4408116739","doi":"10.1161/jaha.124.039221","title":"Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair","year":2025,"lang":"en","type":"article","venue":"Journal of the American Heart Association","topic":"Aortic aneurysm repair treatments","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Artificial Intelligence in Medicine (Canada); University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Receiver operating characteristic; Aneurysm; Logistic regression; Endovascular aneurysm repair; Surgery; Aortic aneurysm; Thoracic aortic aneurysm; Abdominal aortic aneurysm; Radiology; Internal medicine","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":[0.006365374,0.0007346275,0.0006464228,0.001873828,0.0002134941,0.0009036366,0.0005658083,0.0006233616,0.0005937039],"category_scores_gemma":[0.01823278,0.0001787164,0.0006990933,0.0007034196,0.0004186667,0.0007316149,0.0007599453,0.001059103,0.0002527794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005081914,"about_ca_system_score_gemma":0.000715944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782027,"about_ca_topic_score_gemma":0.001676762,"domain_scores_codex":[0.9983517,0.0007504537,0.0001773248,0.0003057024,0.0002460853,0.0001687744],"domain_scores_gemma":[0.9870091,0.008562922,0.002350748,0.0005344627,0.0011027,0.0004400716],"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":[0.0003327286,0.000287542,0.9274276,0.00003665258,0.0003126518,0.00005726179,0.00004325031,0.03870191,0.0001665801,0.00009221165,0.0005276449,0.0320139],"study_design_scores_gemma":[0.00007319636,0.0008507847,0.3959833,0.00008217014,0.0002487288,0.0002819908,0.00009903372,0.5980635,0.001309486,0.00230654,0.000656356,0.00004491248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862956,0.0005150117,0.01145861,0.0003663847,0.000047541,0.00005507925,0.0005087054,0.00007546225,0.000677552],"genre_scores_gemma":[0.9961128,0.0001026368,0.00292331,0.00004286471,0.00003551607,0.00002388819,0.000678291,0.000004996591,0.00007562301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006365374,"threshold_uncertainty_score":0.03366369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306222852427444,"score_gpt":0.3398414825147028,"score_spread":0.3167792539904284,"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."}}