{"id":"W4409591774","doi":"10.1177/15266028251333670","title":"Predicting Outcomes Following Carotid Artery Stenting Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Endovascular Therapy","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Physicians' Services Incorporated Foundation; Ontario Ministry of Health and Long-Term Care","keywords":"Medicine; Mace; Receiver operating characteristic; Logistic regression; Carotid stenting; Stroke (engine); Brier score; Perioperative; Myocardial infarction; Internal medicine; Surgery; Carotid endarterectomy; Carotid arteries; Machine learning; Conventional PCI","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":[0.000975962,0.0002537938,0.0008007172,0.0004255334,0.0002293359,0.00007577131,0.0001621184,0.00007898736,0.00006238252],"category_scores_gemma":[0.0002759826,0.0001949948,0.002226162,0.0003224497,0.00003214103,0.0002461976,0.0000439039,0.0005223411,0.00000210991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083493,"about_ca_system_score_gemma":0.0001979441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004265162,"about_ca_topic_score_gemma":0.000003797616,"domain_scores_codex":[0.9978552,0.0002090321,0.000732539,0.000214823,0.0006518067,0.0003365758],"domain_scores_gemma":[0.9988908,0.0001188061,0.0003169174,0.0003063018,0.0002128213,0.0001544001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005541599,0.0001770357,0.9672002,0.00005686139,0.00324476,0.0000862862,0.0001652083,0.0004607752,0.02473645,0.000009632749,0.000009619894,0.003797707],"study_design_scores_gemma":[0.02368734,0.0006860046,0.9356852,0.002025163,0.003229353,0.001387946,0.002121577,0.003702848,0.02120583,0.00007211163,0.00562667,0.0005699327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876976,0.008257251,0.00245289,0.0001385794,0.0007208748,0.000291225,0.000001213369,0.00004924878,0.0003911331],"genre_scores_gemma":[0.997192,0.0006435405,0.001286288,0.0004198183,0.0002132292,0.000003504752,0.000005264134,0.00003936264,0.0001969808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03151501,"threshold_uncertainty_score":0.7951658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752800554044102,"score_gpt":0.2797868004754859,"score_spread":0.2622587949350448,"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."}}