Trauma resident exposure in Canada and operative numbers (TraumaRECON): a national multicentre retrospective review of operative and nonoperative trauma teaching
Bibliographic record
Abstract
BACKGROUND: General surgeons play an important role in the provision of trauma care in Canada and the current extent of their trauma experience during training is unknown. We sought to quantify the operative and nonoperative educational experiences among Canadian general surgery trainees. METHODS: We conducted a multicentre retrospective study of major operative exposures experienced by general surgery residents, as identified using institutional trauma registries and subsequent chart-level review, for 2008-2018. We also conducted a site survey on trauma education and structure. RESULTS: We collected data on operative exposure for general surgery residents from 7 programs and survey data from 10 programs. Operations predominantly occurred after hours (73% after 1700 or on weekends) and general surgery residents were absent from a substantial proportion (25%) of relevant trauma operations. The structure of trauma education was heterogeneous among programs, with considerable site-specific variability in the involvement of surgical specialties in trauma care. During their training, graduating general surgery residents each experienced around 4 index trauma laparotomies, 1 splenectomy, 1 thoracotomy, and 0 neck explorations for trauma. CONCLUSION: General surgery residents who train in Canada receive variable and limited exposure to operative and nonoperative trauma care. These data can be used as a baseline to inform the application of competency-based medical education in trauma care for general surgery training in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".