Complication rates as a trauma care performance indicator: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Information on complication rates is essential to trauma quality improvement efforts. However, it is unclear which complications are the most clinically relevant. The objective of this study was to evaluate whether there is consensus on the complications that should be used to evaluate the performance of acute care trauma hospitals. METHODS: We searched the Medline, EMBASE, Cochrane Central, CINAHL, BIOSIS, TRIP and ProQuest databases and included studies using at least one nonfatal outcome to evaluate the performance of acute care trauma hospitals. Data were extracted in duplicate using a piloted electronic data abstraction form. Consensus was considered to be reached if a specific complication was used in ≥ 70% of studies (strong recommendation) or in ≥ 50% of studies (weak recommendation). RESULTS: Of 14,521 citations identified, 22 were eligible for inclusion. We observed important heterogeneity in the complications used to evaluate trauma care. Seventy-nine specific complications were identified but none were used in ≥ 70% of studies and only three (pulmonary embolism, deep vein thrombosis, and pneumonia) were used in ≥ 50% of studies. Only one study provided evidence for the clinical relevance of complications used and only five studies (23%) were considered of high methodological quality. CONCLUSION: Based on the results of this review, we can make a weak recommendation on three complications that should be used to evaluate acute care trauma hospitals; pulmonary embolism, deep vein thrombosis, and pneumonia. However, considering the observed disparity in definitions, the lack of clinical justification for the complications used, and the low methodological quality of studies, further research is needed to develop a valid and reliable performance indicator based on complications that can be used to improve the quality and efficiency of trauma care.
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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 | 0.029 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".