Variation in use of damage control laparotomy for trauma by trauma centers in the United States, Canada, and Australasia
Bibliographic record
Abstract
BACKGROUND: It is unknown how frequently damage control (DC) laparotomy is used across trauma centers in different countries. We conducted a cross-sectional survey of trauma centers in the United States, Canada, and Australasia to study variations in use of the procedure and predictors of more frequent use of DC laparotomy. METHODS: A self-administered, electronic, cross-sectional survey of trauma centers in the United States, Canada, and Australasia was conducted. The survey collected information about trauma center and program characteristics. It also asked how often the trauma program director estimated DC laparotomy was performed on injured patients at that center on average over the last year. Multivariable logistic regression was used to identify predictors of a higher reported frequency of use of DC laparotomy. RESULTS: Of the 366 potentially eligible trauma centers sent the survey, 199 (51.8%) trauma program directors or leaders responded [United States = 156 (78.4%), Canada = 26 (13.1%), and Australasia = 17 (8.5%)]. The reported frequency of use of DC laparotomy was highly variable across trauma centers. DC laparotomy was used more frequently in level-1 than level-2 or -3 trauma centers. Further, high-volume level-1 centers used DC laparotomy significantly more often than lower volume level-1 centers (p = 0.02). Nearly half (48.4%) of high-volume volume level-1 trauma centers reported using the procedure at least once weekly. Significant adjusted predictors of more frequent use of DC laparotomy included country of origin [odds ratio (OR) for the United States vs. Canada = 7.49; 95% confidence interval (CI) 1.39-40.27], level-1 verification status (OR = 6.02; 95% CI 2.01-18.06), and the assessment of a higher number of severely injured (Injury Severity Scale score > 15) patients (OR per-100 patients = 1.62; 95% CI 1.20-2.18) and patients with penetrating injuries (OR per-5% increase = 1.27; 95% CI 1.01-1.58) in the last year. CONCLUSIONS: The reported frequency of use of DC laparotomy was highly variable across trauma centers. Those centers that most need to evaluate the benefit-to-risk ratio of using DC laparotomy in different scenarios may include high-volume, level-1 trauma centers, particularly those that often manage penetrating injuries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".