Influence of a province-wide trauma system on motor vehicle collision process of trauma care and mortality: a 10-year follow-up evaluation
Bibliographic record
Abstract
BACKGROUND: Mature trauma systems have evolved to respond to major injury-related morbidity and mortality. Studies of mature trauma systems have demonstrated improved survival, especially among seriously injured patients. From 1995 to 1998, a province-wide trauma system was implemented in the province of Nova Scotia. We measured the proportion of admissions to a tertiary level trauma centre and the proportion of in-hospital deaths among patients with major injuries as a result of a motor vehicle collisions (MVCs) before and 10 years after provincial trauma systems implementation. METHODS: We identified major trauma patients aged 16 years and older using external cause of injury codes pertaining to MVCs from population-based hospital claims and vital statistics data. Individuals who were admitted to hospital or died because of an MVC in 1993-1994 (preimplementation), were compared with those who were admitted to hospital or died in 2003-2005 (postimplementation). RESULTS: Postimplementation, there was a 9% increase in the number of seriously injured individuals with primary admission to tertiary care. This increase was statistically significant even after we adjusted for age, head injury and municipality of residence (relative risk [RR] 1.09, 95% confidence interval [CI] 1.04-1.14). The probability of dying while in hospital in the postimplementation period decreased by 29% (adjusted RR 0.57, 95% CI 0.32-1.03), although this difference was not statistically significant. CONCLUSION: Individuals seriously injured in MVCs in Nova Scotia were more likely to be admitted to tertiary care after the implementation of a province-wide trauma system. There was a trend toward decreased mortality, but further research is warranted to confirm the survival benefit and delineate other contributing factors.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".