Evolution of Patient Outcomes Over 14 Years in a Mature, Inclusive Canadian Trauma System
Bibliographic record
Abstract
BACKGROUND: The introduction of trauma systems in many countries worldwide has been shown to improve injury survival. However, few data are available on the long-term evolution of outcomes other than mortality. The objective of this study was to describe trends in mortality, unplanned readmission, complications, and length of stay in a mature inclusive trauma system from 1999 to 2012. METHODS: This retrospective cohort study was based on the inclusive trauma system of Quebec, Canada. Data were drawn from the trauma registry linked to the hospital discharge database. Time trends were evaluated using generalized linear mixed models with a correction for hospital clusters and cohort effects. RESULTS: Between 1999 and 2012, risk-adjusted mortality decreased from 5.8 to 4.2% for all patients and from 14.9 to 13.1% for major trauma (p < 0.0001). Mean LOS decreased from 9.5 days to 8.0 days for all patients and from 15.5 days to 11.5 days for major trauma (p < 0.0001). Unplanned readmission and complication rates remained stable over the observation period at around 6.6 and 11.6% for all patients and 7.6 and 25.6% for major trauma, respectively. CONCLUSION: The results of this study suggest that there have been significant decreases in patient mortality and hospital length of stay in the inclusive trauma system of Québec over the last decade. Results also suggest that efforts should be made to reduce in-hospital complications and unplanned readmissions. Future research should attempt to identify determinants of observed decreases in mortality and LOS and assess whether similar improvements have occurred in functional outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".