Trauma Resident Exposure in Canada and Operative Numbers (TraumaRECON): a study protocol for a national multicentre study of operative, nonoperative and structured educational exposures in Canada
Bibliographic record
Abstract
BACKGROUND: Canada's shift toward nonoperative trauma management, coupled with the implementation of competency-based medical education, has highlighted the lack of quantitative knowledge about the volume and quality of exposure to operative trauma training experiences among Canadian general surgery residents. We aim to quantify the exposure to specific operative trauma domains during residency over time and across participating Canadian training programs and to perform an environmental scan of the nonoperative clinical exposure and other formal and informal trauma education provided to general surgery residents across Canadian training programs. METHODS: Trauma Resident Exposure in Canada and Operative Numbers (TraumaRECON) is a retrospective, multicentre study of operative trauma procedures involving the participation of general surgery residents in Canada. Participating sites will populate a data abstraction form outlining operative trauma data points as abstracted from eligible trauma operative charts via each site's trauma registry. They will also complete a survey of the nonoperative clinical and other educational opportunities in trauma care to which general surgery residents are exposed in participating general surgery training programs. The primary outcome of this study will be the volume of operative trauma cases that general surgery residents are exposed to during their residency in Canada. Secondary outcomes will include the association between time of occurrence during the day for trauma operations and resident participation, operative volume stratified by postgraduate year of training, volume of missed operative trauma opportunities, volume of operative trauma cases by type, and the operative role of residents involved in trauma operations. INTERPRETATION: The need for competency in operative trauma management will always exist; however, with potentially limited operative trauma volume, this standard may prove difficult to achieve for the next generation of general surgery residents in Canada. Results of TraumaRECON will provide a quantitative commentary on the operative trauma volume experienced by general surgery residents in Canada to inform future teaching practices in the context of competency-based medical education.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".