Cause for concern: Resident experience in operative trauma during general surgery residency at a Canadian centre
Bibliographic record
Abstract
BACKGROUND: The ability to provide competent operative trauma care is a core objective of general surgery training but recent publications question the ability of graduates to meet this standard. To assess the adequacy of operative trauma exposure during residency, we constructed and analyzed a retrospective trauma operative case log for general surgery residents at a Canadian trauma centre. METHODS: The Hamilton General Hospital Trauma Registry was used to identify all patients from July 2008 to June 2018 who underwent a trauma operation on the neck, chest, or abdomen. Medical records were reviewed to determine procedure type and resident presence. RESULTS: In our study, 417 patients underwent 570 operations (422 abdominal, 103 thoracic, and 45 neck). For the 35 residents that completed their general surgery residency during the study, the median number of trauma laparotomies was 5, with only 14/35 (40%) present for ≥10 trauma operations. Only 10 residents (29%) were exposed to a neck exploration and 18 (51%) exposed to a thoracic operation for trauma. CONCLUSIONS: Operative trauma exposure amongst general surgery residents at an academic Canadian trauma centre was limited. Cumulative operative trauma surgery exposure of a typical graduating resident was inadequate when compared to Canadian and American accrediting-body standards.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".