Comparison of Hospital Performance in Emergency Versus Elective General Surgery Operations at 198 Hospitals
Bibliographic record
Abstract
BACKGROUND: Surgical quality improvement has focused on elective general surgery (ELGS) outcomes despite the substantial risk associated with emergency general surgery (EMGS) procedures. Furthermore, any differences in the quality of care provided to EMGS versus ELGS patients are not well described. We compared risk factors and risk-adjusted outcomes associated with EMGS and ELGS procedures to assess whether hospitals have comparable outcomes across these procedures. STUDY DESIGN: Using American College of Surgeons National Surgical Quality Improvement Program data (2005 to 2008), regression models were constructed for 30-day overall morbidity, serious morbidity, and mortality among all patients, EMGS patients, and ELGS patients. Observed-to-expected (O/E) ratios were calculated from models based on EMGS or ELGS patients. Association of hospital performance after EMGS versus ELGS procedures was assessed by evaluating correlations of O/E ratios; agreement in outlier status (hospitals where O/E confidence intervals [CI] do not overlap 1.0) was evaluated with weighted kappa. RESULTS: Of 473,619 procedures, 67,445 (14.2%) patients underwent an EMGS procedure. EMGS patients were more likely to experience any morbidity (odds ratio [OR] 1.20; 95% CI 1.16 to 1.23), serious morbidity (OR 1.26; 95% CI 1.21 to 1.30), and mortality (OR 1.39; 95% CI 1.30 to 1.48). Correlation between O/E ratios for EMGS and ELGS were moderate to low (overall morbidity = 0.48, p < 0.0001; serious morbidity = 0.41, p < 0.0001, mortality = 0.18, p = 0.01). Outlier status was not consistent across EMGS and ELGS, with only slight agreement (overall morbidity = 0.18, p < 0.0001; serious morbidity = 0.16, p = 0.001, mortality = 0.19, p = 0.01). CONCLUSIONS: EMGS patients are at substantially greater risk than ELGS patients for adverse events. Hospitals do not appear to have highly consistent performance across EMGS and ELGS outcomes. Processes of care that afford improved outcomes to EMGS patients need to be identified and disseminated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".