Prevention of Complications and Successful Rescue of Patients With Serious Complications: Characteristics of High-Performing Trauma Centers
Bibliographic record
Abstract
BACKGROUND: "Failure to rescue" patients with complications is a factor contributing to high mortality rates after elective surgery. In trauma, where early deaths are the primary contributors to a trauma center's mortality rate, the rescue of patients with complications might not be related to overall trauma center mortality. We assessed the extent to which trauma center mortality was reflected by the center's ability to rescue patients with major complications. METHODS: Data were derived from the National Trauma Databank, and limited to adults with an Injury Severity Score ≥9 and to centers with adequate complication reporting. Regression models were used to produce center-level adjusted rates for mortality and complications. Centers were ranked on their adjusted mortality rate and divided into quintiles. RESULTS: Of 76,048 patients, 9.6% had a major complication and 7.9% died. The mean complication rate in the quintile of centers with the highest mortality rates was 11.1%, compared with 7.7% in the quintile of centers with the lowest mortality rates (p=0.03). In addition, mortality among patients with complications differed significantly across quintiles. The mean mortality among patients with complications was 20.3% in the quintile of centers with the highest overall mortality rates, compared with 11.1% in the quintile of centers with the lowest overall mortality rates (p<0.001). CONCLUSIONS: Unlike reports from elective surgery, complication rates after severe injury differ across centers and parallel mortality rates. Centers with low overall mortality are more successful at rescuing patients who experience complications. A lower risk of complications and better care of those with complications are both at play in high-performing trauma centers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".