Impact of the acute care surgery model on resident operative experience in emergency general surgery
Bibliographic record
Abstract
Background: The acute care surgery (ACS) model has been shown to improve patient, hospital and surgeon-specific outcomes. To date, however, little has been published on its impact on residency training. Our study compared the emergency general surgery (EGS) operative experiences of residents assigned to ACS versus elective surgical rotations. Methods: Resident-reported EGS case logs were prospectively collected over a 9-month period across 3 teaching hospitals. Descriptive statistics were tabulated and group comparisons were made using χ2 statistics for categorical data and t tests for continuous data. Results: Overall, 1061 cases were reported. Resident participation exceeded 90%). Appendiceal and biliary disease accounted for 49.7% of EGS cases. Residents on ACS rotations reported participating in twice as many EGS cases per block as residents on elective rotations (12.64 v. 6.30 cases, p < 0.01). Most cases occurred after hours while residents were on call rather than during daytime ACS hours (78.8% v. 21.1%, p < 0.01). Senior residents were more likely than junior residents to report having a primary operator role (71.3% v. 32.0%, p < 0.01). Although the timing of cases made no difference in the operative role of senior residents, junior residents assumed the primary operator role more often during the daytime than after hours (50.0% v. 33.1%, p = 0.01). Conclusion: Despite implementation of the ACS model, residents in our program obtained most of their EGS operative experience after hours while on call. Although further research is needed, our study suggests that improved daytime access to the operating room may represent an opportunity to improve the quantity and quality of the EGS operative experience at our academic network.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".