Effect Of Trauma Center Status on 30-Day Outcomes After Emergency General Surgery
Bibliographic record
Abstract
BACKGROUND: Trauma surgeons increasingly care for emergency general surgery (EGS) patients. The extent to which trauma center (TC) performance improvement translates into improved quality for EGS is unknown. We hypothesized that EGS outcomes in TCs would be similar to outcomes in non-trauma centers (NTC); failure to support our hypothesis suggests that the effects of trauma performance improvement have extended beyond trauma patients. STUDY DESIGN: We retrospectively studied EGS procedures at TCs versus NTCs among American College of Surgeons National Surgical Quality Improvement Program participants (2005-2008). Thirty-day outcomes were overall morbidity, serious morbidity, and mortality. TC versus NTC outcomes were compared using regression modeling, observed-to-expected (O/E) ratios (among hospitals submitting ≥20 EGS procedures), and outlier status (hospitals whose O/E confidence interval excludes 1.0). RESULTS: Of 68,003 patients at 222 hospitals, 42,264 (62.2%) were treated at 121 TCs; 25,739 (37.8%) were treated at 101 NTCs. TCs had significantly higher overall morbidity (21.4% versus 17.2%; p < 0.0001), serious morbidity (15.8% versus 12.3%; p < 0.0001), and mortality (6.4% versus 4.8%; p < 0.0001) than NTCs. On adjusted analyses, TC status was a significant predictor of overall morbidity (odds ratio = 1.11; 95% CI, 1.01-1.21), but not serious morbidity (odds ratio = 1.08; 95% CI, 0.98-1.19) or mortality (odds ratio = 0.92; 95% CI, 0.82-1.04). Among 211 hospitals assigned O/E ratios, TCs were more likely, although not significantly so, to be high outliers for overall morbidity (7.6% versus 4.3%; p = 0.017), serious morbidity (5.1% versus 4.3%; p = 0.034), and mortality (3.4% versus 2.2%; p > 0.099). CONCLUSIONS: Although overall morbidity tended to favor NTCs, mortality was no different. This suggests that the trauma performance improvement processes have not been applied to EGS patients, despite being cared for by similar providers. Despite having processes for trauma, there remains the opportunity for quality improvement for EGS 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".