{"id":"W4387362617","doi":"10.1016/j.ejvs.2023.09.028","title":"Using machine learning to predict outcomes following carotid endarterectomy","year":2023,"lang":"en","type":"article","venue":"European Journal of Vascular and Endovascular Surgery","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Carotid endarterectomy; Perioperative; Endarterectomy; Machine learning; Medical physics; Intensive care medicine; Carotid arteries; Artificial intelligence; Surgery; Computer science","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":[0.002731368,0.0005139401,0.0007418508,0.00133536,0.0003840385,0.001380952,0.0007106216,0.001274838,0.001142791],"category_scores_gemma":[0.01276818,0.0002064248,0.0007978738,0.0007552598,0.0004133606,0.00110109,0.0006951785,0.001688814,0.0004678939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000482104,"about_ca_system_score_gemma":0.0006020474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002030315,"about_ca_topic_score_gemma":0.002751174,"domain_scores_codex":[0.9987397,0.0004684702,0.0001717992,0.0001844638,0.0002290355,0.0002065432],"domain_scores_gemma":[0.9915314,0.005108334,0.001517856,0.0004237048,0.0007666685,0.0006521055],"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":[0.000965627,0.0004541434,0.9772921,0.0000342621,0.0003425137,0.00007559412,0.00004260739,0.003693595,0.0001736409,0.00009369046,0.0007068489,0.01612538],"study_design_scores_gemma":[0.00008787915,0.001055582,0.9005399,0.00006777731,0.000506449,0.0003260847,0.0002619317,0.09332645,0.0006744924,0.002337544,0.0007597569,0.00005623102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957861,0.0005707626,0.001595964,0.000401304,0.0001376114,0.00001399147,0.0004438182,0.00002738461,0.001023139],"genre_scores_gemma":[0.9980718,0.0001684378,0.0004764113,0.00004836302,0.0001136767,0.00001163081,0.0008451989,0.000005823796,0.0002586219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002731368,"threshold_uncertainty_score":0.01444501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028839109592799,"score_gpt":0.2606826164814595,"score_spread":0.2303942253855315,"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."}}