Variation in Case Exposure During Internal Medicine Residency
Bibliographic record
Abstract
Importance: Variation in residency case exposure affects resident learning and readiness for future practice. Accurate reporting of case exposure for internal medicine (IM) residents is challenging because feasible and reliable methods for linking patient care to residents are lacking. Objective: To develop an integrated education-clinical database to characterize and measure case exposure variability among IM residents. Design, Setting, and Participants: In this cohort study, an integrated educational-clinical database was developed by linking patients admitted during overnight IM in-hospital call shifts at 5 teaching hospitals to senior on-call residents. The senior resident, who directly cares for all overnight IM admissions, was linked to their patients by the admission date, time, and hospital. The database included IM residents enrolled between July 1, 2010, and December 31, 2019, in 1 Canadian IM residency. Analysis occurred between August 1, 2023, and June 30, 2024. Main Outcomes and Measures: Case exposure was defined by patient demographic characteristics, discharge diagnoses, volumes, acuity (eg, critical care transfer), medical complexity (eg, Charlson Comorbidity Index), and social determinants of health (eg, from long-term care). Residents were grouped into quartiles for each exposure measure, and the top and bottom quartiles were compared using standardized mean difference (SMD). Variation between hospitals was evaluated by calculating the SMD between the hospitals with the highest and lowest proportions for each measure. Variation over time was assessed using linear and logistic regression. Results: The integrated educational-clinical database included 143 632 admissions (median [IQR] age, 71 [55-83] years; 71 340 [49.7%] female) linked to 793 residents (median [IQR] admissions per shift, 8 [6-12]). At the resident level, there was substantial variation in case exposure for demographic characteristics, diagnoses, volumes, acuity, complexity, and social determinants. For example, residents in the highest quartile had nearly 4 times more admissions requiring critical care transfer compared with the lowest quartile (3071 of 30 228 [10.2%] vs 684 of 25 578 [2.7%]; SMD, 0.31). Hospital-level variation was also significant, particularly in patient volumes (busier hospital vs less busy hospital: median [IQR] admissions per shift, 10 [8-12] vs 7 [5-9]; SMD, 0.96). Over time, residents saw more median (IQR) admissions per shift (2010 vs 2019: 7.6 [6.6-8.4] vs 9.0 [7.6-10.0]; P = .04) and more complex patients (2010 vs 2019: Charlson Comorbidity Index ≥2, 3851 of 13 762 [28.0%] vs 2862 of 8188 [35.0%]; P = .03), while working similar shifts per year (median [IQR], 11 [8-14]). Conclusions: In this cohort study of IM residents in a Canadian residency program, significant variation in case exposure was found between residents, across sites, and over time.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".