{"id":"W7111979066","doi":"","title":"GEMINI MedED: Leveraging ‘Big Data’ to Understand Clinical Practice Variation in Resident Physicians","year":2024,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Clinical Reasoning and Diagnostic Skills","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clinical Practice; Variation (astronomy); Medical record; MEDLINE; Descriptive statistics; Cohort; Quality (philosophy); Health care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01485124,0.0004798181,0.0006281252,0.004515559,0.0003835325,0.002429462,0.001184674,0.0006168143,0.002135727],"category_scores_gemma":[0.07022708,0.0004464418,0.0007747802,0.007221532,0.0004398001,0.001664445,0.003105964,0.0008608408,0.0006404937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130206,"about_ca_system_score_gemma":0.002724148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149123,"about_ca_topic_score_gemma":0.020828,"domain_scores_codex":[0.9911829,0.004224167,0.001379038,0.001538838,0.001315411,0.0003596119],"domain_scores_gemma":[0.9252395,0.03636163,0.0183594,0.01174373,0.004938915,0.003356816],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004240599,0.0000652253,0.9079782,0.0007625911,0.0007408563,0.0001594037,0.001476625,0.002367807,0.0004039892,0.001882948,0.03071809,0.05302018],"study_design_scores_gemma":[0.0001502898,0.0003318925,0.9101152,0.000744871,0.0003988985,0.0003853309,0.001361288,0.02329309,0.001047008,0.006956957,0.05511784,0.00009734356],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5668709,0.006869061,0.04757092,0.01853141,0.000570962,0.001093914,0.3452577,0.002812441,0.01042267],"genre_scores_gemma":[0.8053978,0.001666884,0.07186316,0.001770734,0.0007267822,0.0009437408,0.116448,0.00026391,0.0009188834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9851488,"threshold_uncertainty_score":0.07854182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1673254627957222,"score_gpt":0.3904986882754963,"score_spread":0.2231732254797741,"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."}}