Development and Evaluation of Evidence-Informed Quality Indicators for Adult Injury Care
Bibliographic record
Abstract
In Brief Objective: To develop and evaluate evidence-informed quality indicators of adult injury care. Background: Injury is a leading cause of morbidity and mortality, but there is a lack of consensus regarding how to evaluate injury care. Methods: Using a modification of the RAND/UCLA Appropriateness Methodology, a panel of 19 injury and quality of care experts serially rated and revised quality indicators identified from a systematic review of the literature and international audit of trauma center quality improvement practices. The quality indicators developed by the panel were sent to 133 verified trauma centers in the United States, Canada, Australia, and New Zealand for evaluation. Results: A total of 84 quality indicators were rated and revised by the expert panel over 4 rounds of review producing 31 quality indicators of structure (n = 5), process (n = 21), and outcome (n = 5), designed to assess the safety (n = 8), effectiveness (n = 17), efficiency (n = 6), timeliness (n = 16), equity (n = 2), and patient-centeredness (n = 1) of injury care spanning prehospital (n = 8), hospital (n = 19), and posthospital (n = 2) care and secondary injury prevention (n = 1). A total of 101 trauma centers (76% response rate) rated the indicators (1 = strong disagreement, 9 = strong agreement) as targeting important health improvements (median score 9, interquartile range [IQR] 8–9), easy to interpret (median score 8, IQR 8–9), easy to implement (median score 8, IQR 7–8), and globally good indicators (median score 8, IQR 8–9). Conclusions: Thirty-one evidence-informed quality indicators of adult injury care were developed, shown to have content validity, and can be used as performance measures to guide injury care quality improvement practices. Injury is a leading cause of morbidity and mortality, but there is a lack of consensus regarding how to evaluate injury care. Thirty-one evidence-informed quality indicators of adult injury care were developed, shown to have content validity, and can be used as performance measures to guide quality improvement practices.
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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 | 0.429 | 0.528 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".