{"id":"W4401694750","doi":"10.2196/53337","title":"Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Innovations in Medical Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Training (meteorology); Psychology; Medical education; Data science; Medicine; Computer science; Geography; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006855286,0.000147678,0.0002782032,0.0001704364,0.0002986467,0.0001065009,0.0001360762,0.0001079629,0.000222984],"category_scores_gemma":[0.004413249,0.0001024458,0.00002999417,0.0006484392,0.0002148932,0.0001137011,0.00006633372,0.000776782,0.00001394732],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004149323,"about_ca_system_score_gemma":0.008310505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058647,"about_ca_topic_score_gemma":0.000100109,"domain_scores_codex":[0.9969594,0.0003054258,0.0008609985,0.0005632474,0.0009708493,0.0003400148],"domain_scores_gemma":[0.9989307,0.0002374101,0.00008513917,0.0002839917,0.000146244,0.0003164964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002614735,0.0003730434,0.01966906,0.0001323923,0.00003264194,0.000001622648,0.1948275,2.233063e-8,0.000002672373,0.0001902442,0.01251254,0.7722322],"study_design_scores_gemma":[0.0005027374,0.0006102941,0.4855105,0.00123913,0.00004085703,0.0000991679,0.3926719,0.0002206815,0.00001059141,0.0003797431,0.1185304,0.0001838879],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272086,0.001510039,0.0005006324,0.06756323,0.001682798,0.001372824,1.053114e-7,0.00007028449,0.00009150362],"genre_scores_gemma":[0.9887412,0.00009594599,0.000948094,0.00813222,0.0007553883,0.001067234,0.00001084911,0.0000162225,0.0002328718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7720482,"threshold_uncertainty_score":0.9973115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07877068259246997,"score_gpt":0.4797019613359046,"score_spread":0.4009312787434347,"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."}}