{"id":"W2616497583","doi":"10.1017/cem.2017.233","title":"P031: Using machine learning algorithms for predicting future performance of emergency medicine residents","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Machine learning; Artificial intelligence; Artificial neural network; Predictive analytics; Analytics; Computer science; Set (abstract data type); Medicine; Deep learning; Algorithm; Data set; Data mining","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":[0.004684358,0.00135014,0.0006212146,0.002185178,0.0003966982,0.001029899,0.00109345,0.001280147,0.002253683],"category_scores_gemma":[0.014262,0.0002920008,0.0006939254,0.001362277,0.0003242843,0.001041157,0.0006423169,0.001062146,0.0008734572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000924497,"about_ca_system_score_gemma":0.001141226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01099093,"about_ca_topic_score_gemma":0.006418687,"domain_scores_codex":[0.9986594,0.0005674097,0.0001238502,0.0003035701,0.0002472151,0.00009857244],"domain_scores_gemma":[0.9924581,0.005365762,0.0005061055,0.0002646067,0.001242268,0.0001631714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008231623,0.001039723,0.2321988,0.0002426546,0.0004314062,0.0001882786,0.0001654466,0.4360934,0.001749348,0.0005714323,0.007891799,0.3186046],"study_design_scores_gemma":[0.0000159255,0.0001830424,0.01613682,0.0000300602,0.00002576263,0.00005491549,0.00003514785,0.9815024,0.001085142,0.0005032187,0.0004122835,0.00001534852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8210397,0.001106901,0.1656003,0.001204333,0.0001924141,0.0003431294,0.003512006,0.002751565,0.004249557],"genre_scores_gemma":[0.9060375,0.0002441928,0.08816493,0.0001260506,0.00009726259,0.0002740433,0.003508046,0.00007741903,0.001470567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01099093,"threshold_uncertainty_score":0.02477354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1787734255301912,"score_gpt":0.4278185265185196,"score_spread":0.2490451009883285,"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."}}