Implementation of the World Health Organization Trauma Care Checklist Program in 11 Centers Across Multiple Economic Strata: Effect on Care Process Measures
Bibliographic record
Abstract
BACKGROUND: Trauma contributes more than ten percent of the global burden of disease. Initial assessment and resuscitation of trauma patients often requires rapid diagnosis and management of multiple concurrent complex conditions, and errors are common. We investigated whether implementing a trauma care checklist would improve care for injured patients in low-, middle-, and high-income countries. METHODS: From 2010 to 2012, the impact of the World Health Organization (WHO) Trauma Care Checklist program was assessed in 11 hospitals using a stepped wedge pre- and post-intervention comparison with randomly assigned intervention start dates. Study sites represented nine countries with diverse economic and geographic contexts. Primary end points were adherence to process of care measures; secondary data on morbidity and mortality were also collected. Multilevel logistic regression models examined differences in measures pre- versus post-intervention, accounting for patient age, gender, injury severity, and center-specific variability. RESULTS: Data were collected on 1641 patients before and 1781 after program implementation. Patient age (mean 34 ± 18 vs. 34 ± 18), sex (21 vs. 22 % female), and the proportion of patients with injury severity scores (ISS) ≥ 25 (10 vs. 10 %) were similar before and after checklist implementation (p > 0.05). Improvement was found for 18 of 19 process measures, including greater odds of having abdominal examination (OR 3.26), chest auscultation (OR 2.68), and distal pulse examination (OR 2.33) (all p < 0.05). These changes were robust to several sensitivity analyses. CONCLUSIONS: Implementation of the WHO Trauma Care Checklist was associated with substantial improvements in patient care process measures among a cohort of patients in diverse settings.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".