{"id":"W2965933834","doi":"10.1097/acm.0000000000002899","title":"Using Longitudinal Milestones Data and Learning Analytics to Facilitate the Professional Development of Residents: Early Lessons From Three Specialties","year":2019,"lang":"en","type":"article","venue":"Academic Medicine","topic":"Innovations in Medical Education","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Milestone; Graduation (instrument); Medicine; Formative assessment; Specialty; Cohort; Odds; Longitudinal study; Medical education; Graduate medical education; Logistic regression; Psychology; Family medicine; Accreditation; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001765418,0.0001459964,0.0003648956,0.0001992308,0.000175802,0.000004216166,0.0003541168,0.0001418381,0.000258238],"category_scores_gemma":[0.002829158,0.00009244261,0.00001156486,0.0004635402,0.0002912602,0.0001179724,0.000323934,0.001005562,0.00002722761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008560892,"about_ca_system_score_gemma":0.0003848223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005368362,"about_ca_topic_score_gemma":0.00004654494,"domain_scores_codex":[0.9978848,0.00007193898,0.0006788143,0.000360599,0.0007872925,0.0002165497],"domain_scores_gemma":[0.9986239,0.0003464787,0.0002251781,0.0004979959,0.0002169282,0.0000895113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001831602,0.00002478861,0.9212219,0.0001899885,0.0001653221,0.000003505349,0.03726012,0.00002702783,0.009523062,0.0005361227,0.01040795,0.02045713],"study_design_scores_gemma":[0.0008479581,0.00009805608,0.9741278,0.001878546,0.0001579652,0.00001373581,0.01171104,0.002135324,0.0002127146,0.0004481154,0.008251806,0.0001169743],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804272,0.0006487789,0.003999273,0.01346117,0.0008428678,0.0004448508,0.000008738361,0.00001674753,0.0001503512],"genre_scores_gemma":[0.9883151,0.00007643674,0.007494846,0.0007521904,0.000672029,0.000008014342,0.0001695451,0.00001691821,0.002494936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05290594,"threshold_uncertainty_score":0.4368724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2864747268338032,"score_gpt":0.4439817866038193,"score_spread":0.1575070597700162,"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."}}