Methodology paper for the General Medicine Inpatient Initiative Medical Education Database (GEMINI MedED): a retrospective cohort study of internal medicine resident case-mix, clinical care and patient outcomes
Bibliographic record
Abstract
INTRODUCTION: Unwarranted variation in patient care among physicians is associated with negative patient outcomes and increased healthcare costs. Care variation likely also exists for resident physicians. Despite the global movement towards outcomes-based and competency-based medical education, current assessment strategies in residency do not routinely incorporate clinical outcomes. The widespread use of electronic health records (EHRs) may enable the implementation of in-training assessments that incorporate clinical care and patient outcomes. METHODS AND ANALYSIS: The General Medicine Inpatient Initiative Medical Education Database (GEMINI MedED) is a retrospective cohort study of senior residents (postgraduate year 2/3) enrolled in the University of Toronto Internal Medicine (IM) programme between 1 April 2010 and 31 December 2020. This study focuses on senior IM residents and patients they admit overnight to four academic hospitals. Senior IM residents are responsible for overseeing all overnight admissions; thus, care processes and outcomes for these clinical encounters can be at least partially attributed to the care they provide. Call schedules from each hospital, which list the date, location and senior resident on-call, will be used to link senior residents to EHR data of patients admitted during their on-call shifts. Patient data will be derived from the GEMINI database, which contains administrative (eg, demographic and disposition) and clinical data (eg, laboratory and radiological investigation results) for patients admitted to IM at the four academic hospitals. Overall, this study will examine three domains of resident practice: (1) case-mix variation across residents, hospitals and academic year, (2) resident-sensitive quality measures (EHR-derived metrics that are partially attributable to resident care) and (3) variations in patient outcomes across residents and factors that contribute to such variation. ETHICS AND DISSEMINATION: GEMINI MedED was approved by the University of Toronto Ethics Board (RIS#39339). Results from this study will be presented in academic conferences and peer-reviewed journals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".