The evolution of trauma surgery at a high-volume Canadian centre: implications for public health, prevention, clinical care, education and recruitment
Bibliographic record
Abstract
BACKGROUND: Trauma centres continue to evolve with respect to clinical care and their impact on public health. Despite improvements in patient outcomes, operative volumes, and therefore maintenance of surgical skills, has become a challenging issue. We sought to determine whether injury demographics and treatments at a high-volume centre changed over time. METHODS: We used the Alberta Trauma Registry to analyze all severely injured (injury severity score [ISS] ≥ 12) patient admissions over a 16-year period (1995-2011). RESULTS: Of the 12,879 severely injured patients requiring admission, there was a 1.5- fold increase in the annual admission rate despite population normalization (p = 0.001). Over the 16-year interval, patients were older with a subsequent lower mortality (p = 0.001) and length of hospital stay (p = 0.007). In patients with the most severe ISS (≥ 48), there was no change in mortality (27%, p = 0.26). In 2011, falls were the most common mechanism compared with motor vehicle crashes (41% v. 23%; p < 0.001); this was a complete reversal compared with 1995 (25% v. 41%). Motorized recreational vehicle and motorcycle injuries also increased (p < 0.001). The mean number of operations performed by trauma surgeons decreased (laparotomies: 67 [17%] in 1995 v. 47 [5%] in 2011, p < 0.001). Thoracotomies and tracheostomies remained unchanged (p = 0.19). CONCLUSION: Clinical care has improved despite an increasing overall volume of severely injured patient admissions. The number of operative interventions performed by trauma surgeons continues to decrease concurrent to a change in injury mechanisms. Despite these improvements, maintenance of technical skills among trauma surgeons has become an important issue.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".