Influence of a New Province-Wide Trauma System on Motor Vehicle Trauma Care and Mortality
Bibliographic record
Abstract
BACKGROUND: Mature trauma systems have evolved to respond to high rates of major injury morbidity and mortality. Characterized by prehospital care, triage, transportation, aggressive resuscitation, surgery, and rehabilitation, trauma systems have been found to improve survival for seriously injured patients. In Nova Scotia, a province-wide trauma system was implemented between 1995 and 1998. This study investigated the influence of the province-wide trauma system on motor vehicle trauma care and mortality in its first 2 years of existence. METHODS: Subjects over the age of 15 years were identified using E-codes pertaining to motor vehicle traffic crashes from population-based hospital claims and vital statistics data. Individuals who were hospitalized or died because of a motor vehicle crash in 1993 through 1994, before trauma system implementation, were compared with those who were hospitalized or died in 1999 through 2000, after the trauma system was implemented. RESULTS: In the 2-year period after trauma system implementation, there was a 21% increase in the number of seriously injured individuals with a primary admission to tertiary care. This increase was both clinically and statistically significant even after adjustment for age, gender, multiple injuries, head injury, municipality of residence, and vital status at discharge (RR, 1.21, 95% CI, 1.05-1.35). There was no evidence that the probability of dying while in hospital significantly changed in the first 2 years after trauma system implementation. INTERPRETATION: These results indicate that individuals seriously injured in motor vehicle crashes in Nova Scotia are more likely to be admitted to tertiary care in the postimplementation period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".