Complication Rates among Trauma Centers
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to examine the association between patient complications and admission to Level I trauma centers (TC) compared with nontrauma centers (NTC). STUDY DESIGN: This was a retrospective cohort study of data derived from the National Study on the Costs and Outcomes of Trauma (NSCOT). Patients were recruited from 18 Level I TCs and 51 NTCs in 15 regions encompassing 14 states. Trained study nurses, using standardized forms, abstracted the medical records of the patients. The overall number of complications per patient was identified, as was the presence or absence of 13 specific complications. RESULTS: Patients treated in TCs were more likely to have any complication compared with patients in NTCs, with an adjusted relative risk (RR) of 1.34 (95% CI, 1.03, 1.74). For individual complications, only the urinary tract infection RR of 1.94 (95% CI, 1.07, 3.17) was significantly higher in TCs. TC patients were more likely to have 3 or more complications (RR, 1.83; 95% CI, 1.16, 2.90). Treatment variables that are surrogates for markers of injury severity, such as use of pulmonary artery catheters, multiple operations, massive transfusions (> 2,500 mL packed red blood cells), and invasive brain catheters, occurred significantly more often in TCs. CONCLUSIONS: Trauma centers have a slightly higher incidence rate of complications, even after adjusting for patient case mix. Aggressive treatment may account for a significant portion of TC-associated complications. Pulmonary artery catheter use and intubation had the most influence on overall TC complication rates. Additional study is needed to provide accurate benchmark measures of complication rates and to determine their causes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".