Trauma system accreditation and patient outcomes in British Columbia: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: We aim to assess the impact of several accreditation cycles of trauma centers on patient outcomes, specifically in-hospital mortality, complications and hospital length of stay. DESIGN: Interrupted time series. SETTING: British Columbia, Canada. PARTICIPANTS: Trauma patients admitted to all level I and level II trauma centers between January 2008 and March 2018. EXPOSURE: Accreditation. MAIN OUTCOMES AND MEASURES: We first computed quarterly estimates of the proportions of in-hospital mortality, complications and survival to discharge standardized for change in patient case-mix using prognostic scores and the Aalen-Johansen estimator of the cumulative incidence function. Piecewise regressions were then used to estimate the change in levels and trends for patient outcomes following accreditation. RESULTS: For in-hospital mortality and major complications, the impact of accreditation seems to be associated with short- and long-term reductions after the first cycle and only short-term reductions for subsequent cycles. However, the 95% confidence intervals for these estimates were wide, and we lacked the precision to consistently conclude that accreditation is beneficial. CONCLUSIONS: Applying a quasi-experimental design to time series accounting for changes in patient case-mix, our results suggest that accreditation might reduce in-hospital mortality and major complications. However, there was uncertainty around the estimates of accreditation. Further studies looking at clinical processes of care and other outcomes such as patient or health staff satisfaction are needed.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".