Effectiveness of trauma centre verification: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: There is a growing trend toward verification of trauma centres, but its impact remains unclear. This systematic review aimed to synthesize available evidence on the effectiveness of trauma centre verification. Methods: We conducted a systematic search of the CINAHL, Embase, HealthStar, MEDLINE and ProQuest databases, as well as the websites of key injury organizations for grey literature, from inception to June 2019, without language restrictions. Our population consisted of injured patients treated at trauma centres. The intervention was trauma centre verification. Comparison groups comprised nonverified trauma centres, or the same centre before it was first verified or re-verified. The primary outcome was in-hospital mortality; secondary outcomes included adverse events, resource use and processes of care. We computed pooled summary estimates using random-effects meta-analysis. Results: Of 5125 citations identified, 29, all conducted in the United States, satisfied our inclusion criteria. Mortality was the most frequently investigated outcome (n = 20), followed by processes of care (n = 12), resource use (n = 12) and adverse events (n = 7). The risk of bias was serious to critical in 22 studies. We observed an imprecise association between verification and decreased mortality (relative risk 0.74, 95% confidence interval 0.52 to 1.06) in severely injured patients. Conclusion: Our review showed mixed and inconsistent associations between verification and processes of care or patient outcomes. The validity of the published literature is limited by the lack of robust controls, as well as any evidence from outside the US, which precludes extrapolation to other health care jurisdictions. Quasiexperimental studies are needed to assess the impact of trauma centre verification. Systematic reviews registration: PROSPERO no. CRD42018107083.
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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 | 0.040 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".