{"id":"W2797746628","doi":"10.1503/cmaj.68993","title":"The overall culture of residency selection needs fixing","year":2018,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Selection (genetic algorithm); Computer science; Matching (statistics); Medical education; Service (business); World Wide Web; Data science; Medicine; Artificial intelligence; Pathology; Marketing; Business","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01197304,0.0002856429,0.0004503115,0.001039407,0.01666702,0.006708637,0.002076961,0.006040722,0.007113147],"category_scores_gemma":[0.03446767,0.0003775218,0.0003645346,0.001416113,0.01915108,0.005607635,0.009501663,0.02046848,0.001026296],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01405231,"about_ca_system_score_gemma":0.02463985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08109066,"about_ca_topic_score_gemma":0.1162606,"domain_scores_codex":[0.980251,0.006058169,0.0006451264,0.00123635,0.008036312,0.003772905],"domain_scores_gemma":[0.9627358,0.006644191,0.003530809,0.001199851,0.007950296,0.01793912],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005698069,0.0001440874,0.03721369,0.0002002482,0.00007612354,0.003674423,0.1424856,0.0001381901,0.0009546425,0.05206949,0.6947654,0.06822117],"study_design_scores_gemma":[0.00002256917,0.0001363779,0.01694051,0.0009350757,0.00002263528,0.006736077,0.2829994,0.0003778742,0.0002893939,0.02249869,0.6688341,0.0002072816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02496344,0.001116515,0.0004260915,0.9530388,0.003221588,0.00001108491,0.00002611418,0.00002321124,0.01717323],"genre_scores_gemma":[0.3787731,0.002197076,0.0007316546,0.604243,0.003864927,0.00003885567,0.00003867453,0.00009445807,0.01001834],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.988027,"threshold_uncertainty_score":0.1612373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014746808706125,"score_gpt":0.2463546236440387,"score_spread":0.2362071555569774,"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."}}