Resident Case Load Assessment between Academic General and Subspecialty Sign-out Sites at the University of Toronto Anatomical Pathology Program
Bibliographic record
Abstract
Junior residents at the University of Toronto Anatomical Pathology program rotate through a core curriculum at academic training sites with general and subspecialty sign-out practices. The impact of both practice types on residency training is currently unknown. Our goal was two-fold: First, we evaluated the feasibility of a data mining approach to assess resident case loads. Next, we compared the nature of cases seen by residents when substratified by training site. Laboratory information systems (LIS) from two general and two subspecialty training sites were mined for cases by three junior residents during 2010–2013. Total numbers, consults vs in-house cases, and “small” vs “big” cases were compared. The types of diagnoses were broadly re-coded as neoplastic vs non-neoplastic as well as benign, premalignant and malignant. Data was further substratified by anatomic sub-site. A total of 2,050 cases were identified. General sign-out sites showed that >60% of cases seen were gastrointestinal (GI) and gynecological (Gyne), with an average of 20 GI and 18 Gyne cases per month per resident. At GI and Gyne subspecialty rotations each resident averaged 97 and 55 cases, respectively. Focusing on Gyne cases, there was an increase in consult cases seen in subspecialty sites. There was no difference in the ratio of small and big cases. The distribution between benign, premalignant, and malignant diagnoses varied by resident; in fact, one Gyne rotation saw no malignant cases. This distribution also changed by site with an increase in malignant Gyne diagnoses in subspecialty vs. general sites (43% vs. 8%, respectively). Our study shows that mining LIS for resident cases is feasible and yields a robust dataset. As an example, we highlight key differences in Gyne case numbers, case types, as well as diagnoses seen. Both general and subspecialty practise settings appear to add distinct value to residency training.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".