{"id":"W2761154732","doi":"10.1016/j.jmig.2017.08.618","title":"Modelling the Learning Curves of Incoming Surgical Trainees","year":2017,"lang":"en","type":"article","venue":"Journal of Minimally Invasive Gynecology","topic":"Innovations in Medical Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Learning curve; Selection (genetic algorithm); Medical education; Medical physics; Machine learning; 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":[0.001483713,0.0005996702,0.000462973,0.0008219644,0.0004019687,0.001570093,0.001257197,0.002192297,0.00979102],"category_scores_gemma":[0.01956405,0.0004580943,0.0006664364,0.000808196,0.000586466,0.001321981,0.001124681,0.001523487,0.001088738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00158201,"about_ca_system_score_gemma":0.001787291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02881472,"about_ca_topic_score_gemma":0.01421093,"domain_scores_codex":[0.9994341,0.0002070277,0.00002025178,0.00007959644,0.00006831165,0.0001908298],"domain_scores_gemma":[0.9883823,0.00836023,0.0006105519,0.0002779594,0.001031265,0.001337648],"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":[0.0002572157,0.0001910459,0.01099458,0.00003999791,0.00001584461,0.0001015369,0.0002499057,0.9688404,0.000308234,0.00458722,0.001228634,0.01318547],"study_design_scores_gemma":[0.00002183281,0.0000942703,0.002368422,0.00001533791,0.000006006635,0.00002875759,0.0001338382,0.9931022,0.000166283,0.00357103,0.000479697,0.00001242084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485869,0.0002732981,0.03847518,0.00133821,0.0000868467,0.0000947894,0.0007226533,0.0001277304,0.01029433],"genre_scores_gemma":[0.9868366,0.0001548168,0.004085798,0.0000550057,0.00001721597,0.00006107069,0.0003431525,0.00003974977,0.00840668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02881472,"threshold_uncertainty_score":0.05729401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04145710978248483,"score_gpt":0.3394204864872177,"score_spread":0.2979633767047329,"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."}}