Trauma surgical educational opportunities in Canada: a week in the life of a trauma service
Bibliographic record
Abstract
Background: Trauma educational opportunities for general surgery residents in Canada are uncharacterized. We aimed to characterize these opportunities for and identify factors associated with such opportunities. Methods: We performed a prospective cross-sectional study characterizing trauma educational opportunities within Canadian trauma programs. Data were collected during 1 summer week and 1 winter week. We summarized educational opportunities by trauma site and season and used multivariable modelling to evaluate factors associated with increased likelihood of procedure opportunities. Results: Nine academic trauma centres participated. Most consults (93.9%) and trauma team activations (TTAs) (72.3%) were for blunt injuries, and most presentations were during the summer (67.2% TTAs + consults, 69.3% TTAs). Trauma services cared for a median of 14 (interquartile range [IQR] 10–20) inpatients, 4 (IQR 1–6) patients in the intensive care unit, and 0 (IQR 0–2) patients admitted to another service but subsequently followed by a trauma physician (i.e., consulting patients), which varied across hospitals (p < 0.001). Consult, TTA, nonoperative, and operative procedure volumes varied across sites. The most common operative procedures were laparotomies (36.4%), with 1.33 laparotomies per week per site. For nonlaparotomy operations, the maximum volume was 6 over 2 weeks. More operations occurred during summer (74.2%) than winter. Multivariable modelling determined that penetrating mechanisms (odds ratio [OR] 1.87, 95% confidence interval [CI] 1.11–3.15) and TTAs with a trauma surgeon present (OR 2.37, 95% CI 1.59–3.54) were associated with increased likelihood of procedures. Conclusion: Trauma educational opportunities remain heterogeneous across Canada. Higher volumes of patients with trauma were seen during the summer. Penetrating mechanism and TTAs with a trauma surgeon present appear to increase opportunities to perform procedures. Our results can inform general surgery training programs to optimize resident trauma 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".