The Right Treatment at the Right Time in the Right Place
Bibliographic record
Abstract
In Brief Objective: To evaluate the implementation of an all-inclusive philosophy of trauma care in a large Canadian province. Background: Challenges to regionalized trauma care may occur where transport distances to level I trauma centers are substantial and few level I centers exist. In 2008, we modified our predominantly regionalized model to an all-inclusive one with the hopes of increasing the role of level III trauma centers. Methods: We conducted a population-based, before-and-after study of patient admission and transfer practices and outcomes associated with implementation of an all-inclusive provincial trauma system using multivariable Poisson and linear regression and Cox proportional hazard models. Results: In total, 21,772 major trauma patients were included. Implementation of the all-inclusive model of trauma care was associated with a decline in transfers directly to level I trauma centers [risk ratio (RR) = 0.91; 95% confidence interval (CI): 0.88–0.94; P < 0.001] and an increase in transfers from level III to level I centers (RR = 1.10; 95% CI: 1.00–1.21; P = 0.04). These changes in trauma care occurred in conjunction with a 12% reduction in the hazard of mortality (hazard ratio = 0.88; 95% CI: 0.84–0.98; P = 0.003) and a decrease in mean trauma patient hospital length of stay by 1 day (95% CI: 1.02–1.11; P = 0.02) after adjustment for differences in case mix. Conclusions: In this study, introduction of an all-inclusive provincial trauma system was associated with an increased number of injured patients cared for in their local systems and improved trauma patient mortality and hospital length of stay. In this population-based, before-and-after study of 21,772 major trauma patients, the implementation of an all-inclusive model of provincial trauma care was associated with an increased proportion of injured patients cared for in their local systems, a significant 12% reduction in the hazard of mortality, and a decrease in trauma patient length of hospital stay.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".