{"id":"W4401917145","doi":"10.1161/jaha.124.035425","title":"Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting","year":2024,"lang":"en","type":"article","venue":"Journal of the American Heart Association","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Artificial Intelligence in Medicine (Canada); University of Toronto; St. Michael's Hospital","funders":"Alfaisal University; University of Toronto; King Faisal Specialist Hospital and Research Centre; Brigham and Women's Hospital","keywords":"Medicine; Carotid arteries; Carotid stenting; Cardiology; Internal medicine; Coronary artery disease; Radiology; Carotid endarterectomy","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":[0.004212748,0.0007972046,0.000532791,0.00170759,0.0002093437,0.0008145595,0.0004598706,0.0005775966,0.0003767404],"category_scores_gemma":[0.01407676,0.0001873551,0.0006161081,0.0005837796,0.0003575965,0.0006488118,0.0005907796,0.0008534843,0.0002288616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980623,"about_ca_system_score_gemma":0.0007654772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076829,"about_ca_topic_score_gemma":0.001856358,"domain_scores_codex":[0.9988142,0.0005658386,0.0001085033,0.0001966006,0.0001867189,0.0001282006],"domain_scores_gemma":[0.9929578,0.004707343,0.001117662,0.0002881159,0.0007245347,0.0002045422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005205731,0.0004247509,0.8114986,0.0000654213,0.0004512245,0.000110204,0.000065189,0.0983199,0.0006751838,0.0001565631,0.0008952072,0.08681713],"study_design_scores_gemma":[0.00005837217,0.0009355023,0.1747928,0.00007390174,0.0002463363,0.000275423,0.00005714656,0.8178962,0.002509504,0.002522948,0.0005910248,0.00004086281],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741193,0.0007491304,0.02335576,0.0004465735,0.0000465315,0.0000644876,0.0003393515,0.0001648577,0.0007139519],"genre_scores_gemma":[0.9942457,0.0001508606,0.004954041,0.00004497587,0.00004026658,0.0000221028,0.0004369633,0.000006086549,0.00009902408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004212748,"threshold_uncertainty_score":0.02227944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0161409056341575,"score_gpt":0.2893644186445313,"score_spread":0.2732235130103738,"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."}}