{"id":"W2601604015","doi":"10.1017/cjn.2017.30","title":"Operative Landscape at Canadian Neurosurgery Residency Programs","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Ottawa; University of British Columbia; McMaster University; Western University; University of Toronto; Université de Sherbrooke; Calgary Laboratory Services; University of Manitoba; Dalhousie University; Université Laval; University of Alberta; University of Saskatchewan; University of Calgary","funders":"Université de Sherbrooke; McGill University; University of Ottawa; University of Toronto; Dalhousie University; Hamilton Health Sciences; University of Alberta; Université Laval","keywords":"Neurosurgery; Subspecialty; Medicine; Residency training; Graduate medical education; General surgery; Medical education; Surgery; Family medicine; Accreditation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.001289453,0.0003496802,0.000212682,0.004964308,0.00295585,0.001493806,0.001585925,0.0003722384,0.005848372],"category_scores_gemma":[0.0079495,0.0002969285,0.0003724022,0.006880173,0.001068234,0.0006505239,0.001577415,0.0007245375,0.0004323988],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02942896,"about_ca_system_score_gemma":0.05134177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9352465,"about_ca_topic_score_gemma":0.9721828,"domain_scores_codex":[0.9951885,0.0002449607,0.0001720017,0.0004550005,0.002379429,0.001560037],"domain_scores_gemma":[0.9897102,0.0006084368,0.002187717,0.0001409156,0.003814915,0.003537773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001225371,0.00006839367,0.8842821,0.0004212212,0.00008481782,0.0007344338,0.004458993,0.001634022,0.0009159652,0.001859772,0.02995389,0.07546387],"study_design_scores_gemma":[0.000004915524,0.00005187916,0.9764737,0.0002157768,0.0000213583,0.0007301038,0.004916317,0.000634974,0.0001944357,0.0001988904,0.01651037,0.00004730256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.946267,0.003848032,0.001247981,0.006614433,0.0001380341,0.0001503018,0.01810914,0.0002104962,0.02341448],"genre_scores_gemma":[0.9854274,0.003441723,0.001888468,0.0007380829,0.00009397363,0.00006510518,0.006083122,0.00004819726,0.002214034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.970571,"threshold_uncertainty_score":0.213523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07866447469199055,"score_gpt":0.3269989782071184,"score_spread":0.2483345035151279,"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."}}