Impact of trauma centre accreditation on mortality and complications in a Canadian trauma system: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Periodic external accreditation visits aiming to determine whether trauma centres are fulfilling the criteria for optimal care are part of most trauma systems. However, despite the growing trend towards accreditation of trauma centres, its impact on patient outcomes remains unclear. In addition, a recent systematic review found inconsistent results on the association between accreditation and patient outcomes, mostly due to the lack of robust controls. We aim to address these gaps by assessing the impact of trauma centre accreditation on patient outcomes, specifically in-hospital mortality and complications, using an interrupted time series (ITS) design. METHODS: We included all major trauma admissions to five level I and four level II trauma centres in Quebec, Canada between 2008 and 2017. In order to perform ITS, we first obtained monthly and quarterly estimates of the proportions of in-hospital mortality and complications, respectively, for level I and level II centres. Prognostic scores were used to standardise these proportions to account for changes in patient case mix and segmented regressions with autocorrelated errors were used to estimate changes in levels and trends in both outcomes following accreditation. RESULTS: There were 51 035 admissions, including 20 165 for major trauma during the study period. After accounting for changes in patient case mix and secular trend in studied outcomes, we globally did not observe an association between accreditation and patient outcomes. However, associations were heterogeneous across centres. For example, in a level II centre with worsening preaccreditation outcomes, accreditation led to -9.08 (95% CI -13.29 to -4.87) and -9.60 (95% CI -15.77 to -3.43) percentage point reductions in mortality and complications, respectively. CONCLUSION: Accreditation seemed to be beneficial for centres that were experiencing a decrease in performance preceding accreditation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".