Comparing resident operative volumes for routine general surgery cases at academic, urban community, and rural training sites
Bibliographic record
Abstract
BACKGROUND: Surgical training traditionally took place at academic centres, but changed to incorporate community and rural hospitals. As little data exist comparing resident case volumes between these locations, the objective of this study was to determine variations in these volumes for routine general surgery procedures. METHODS: We analyzed senior resident case logs from 2009 to 2019 from a general surgery residency program. We classified training centres as academic, community, and rural. Cases included appendectomy, cholecystectomy, hernia repair, bowel resection, adhesiolysis, and stoma formation or reversal. We matched procedures to blocks based on date of case and compared groups using a Poisson mixed-methods model and 95% confidence intervals (CIs). RESULTS: We included 85 residents and 28 532 cases. Postgraduate year (PGY) 3 residents at academic sites performed 10.9 (95% CI 10.1-11.6) cases per block, which was fewer than 14.7 (95% CI 13.6-15.9) at community and 15.3 (95% CI 14.2-16.5) at rural sites. Fourth-year residents (PGY4) showed a greater difference, with academic residents performing 8.7 (95% CI 8.0-9.3) cases per block compared with 23.7 (95% CI 22.1-25.4) in the community and 25.6 (95% CI 23.6-27.9) at rural sites. This difference continued in PGY5, with academic residents performing 8.3 (95% CI 7.3-9.3) cases per block, compared with 18.9 (95% CI 16.8-21.0) in the community and 14.5 (95% CI 7.0-21.9) at rural sites. CONCLUSION: Senior residents performed fewer routine cases at academic sites than in community and rural centres. Programs can use these data to optimize scheduling for struggling residents who require exposure to routine cases, and help residents complete the requirements of a Competence by Design curriculum.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".