Comparison of Hospital Performance in Trauma vs Emergency and Elective General Surgery
Bibliographic record
Abstract
HYPOTHESES: As emergency general surgery (EMGS) and trauma care are increasingly being provided by the same personnel with overlapping resources, we postulated that the quality of care provided to EMGS and trauma patients would be similar. We also evaluated the relationship between trauma and elective general surgery (ELGS) care, believing that performance would be similar across these services as it reflects institutional culture. DESIGN: Retrospective cohort study comparing hospital performance in trauma and EMGS care and in trauma and ELGS care. Regression models for mortality and serious morbidity were constructed for trauma, EMGS, and ELGS hospitals contributing to both the National Trauma Data Bank (2007) and American College of Surgeons National Surgical Quality Improvement Program (2005-2008). SETTING: Forty-six hospitals. MAIN OUTCOME MEASURES: Correlations of observed to expected ratios were examined. Outlier status (hospitals with CIs of observed to expected ratios excluding 1.0) was compared using weighted . RESULTS: There was no significant relationship between trauma and EMGS mortality (r=-0.01, P=.94; =-0.10, P=.61) or between trauma and ELGS mortality (r=0.23, P=.12; =0.07, P=.62). There was no significant relationship between trauma and EMGS morbidity (r=0.21, P=.17; =0.04, P=.63) or between trauma and ELGS morbidity (r=0.16, P=.30; =0.11, P=.37). No hospitals were consistently low or high outliers across all 3 groups. CONCLUSIONS: Trauma performance improvement programs are well established compared with those for EMGS. Although EMGS patients use similar structures and processes as trauma patients, there is a lack of correlation between the quality of care provided to trauma and EMGS patients; EMGS should be incorporated into trauma performance improvement programs.
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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 | 0.004 | 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 | 0.001 | 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".