Evaluation of the Long‐term Trend in Mortality from Injury in a Mature Inclusive Trauma System
Bibliographic record
Abstract
BACKGROUND: Organized trauma systems are designed to improve the quality and efficiency of trauma care. Several studies have reported mortality reductions during or immediately after implementation of a trauma system but little data are available on long-term trends. The aim of this study was to evaluate the long-term trend in risk-adjusted mortality in a mature inclusive trauma system. METHODS: The trauma system of the province of Quebec, Canada, was implemented in 1992 and completed in 1996. Data were drawn from the Quebec Trauma Registry with mandatory participation of all 59 centres, uniform inclusion criteria, and standardized data collection and validation procedures. Temporal trends from 1999 to 2006 were evaluated using adjusted estimates of hospital mortality generated with a random-intercept hierarchical logistic regression model. Estimates were adjusted using the Trauma Risk Adjustment Model (TRAM) score. RESULTS: The study population comprised 88,235 patients, including 4731 hospital deaths (5.4%). Crude mortality risk varied between 5.2% in 1999 and 4.6% in 2006. Adjusted mortality risk remained stable between 1999 and 2002, but a statistically significant decrease of 4.6% per year (95% CI = 0.9-8.5) was observed between 2002 and 2006. CONCLUSIONS: The risk of hospital mortality in the Quebec trauma system decreased by 24% between 2002 and 2006. Results suggest that as inclusive and regionalized trauma systems mature, they may provide better care to trauma victims. Further research should attempt to identify determinants of the observed decrease within the system and evaluate quality of life among survivors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".