{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002677506,0.0008477059,0.000482073,0.001937594,0.0002309399,0.0007536041,0.0005472682,0.0005211654,0.0005712694],"category_scores_gemma":[0.008816625,0.0001764114,0.0006326884,0.000735289,0.0002573394,0.0005650284,0.000514298,0.0006899287,0.0002394566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006255702,"about_ca_system_score_gemma":0.0009283915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004607236,"about_ca_topic_score_gemma":0.00348703,"domain_scores_codex":[0.9992955,0.0002596774,0.00008394255,0.0001476327,0.0001257017,0.00008758646],"domain_scores_gemma":[0.9949542,0.003517867,0.000683829,0.0001394969,0.0005627485,0.0001418808],"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":[0.0004426071,0.0005832221,0.5459023,0.0001217251,0.0003592593,0.0001789787,0.00008865483,0.3283249,0.0007688429,0.000240345,0.001825578,0.1211636],"study_design_scores_gemma":[0.00002406138,0.000264869,0.04670508,0.00004443046,0.0000693317,0.00010806,0.00003171422,0.9499468,0.001010876,0.001472072,0.000303373,0.00001928044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9472479,0.0008850264,0.04816346,0.0007843068,0.00005722577,0.00012792,0.001001455,0.0004653912,0.001267413],"genre_scores_gemma":[0.986923,0.0001899526,0.0115335,0.00007343945,0.00005334631,0.00005420851,0.0009862063,0.00001068319,0.0001757202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004607236,"threshold_uncertainty_score":0.01416016,"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."}}