Operative timing is associated with increased morbidity and mortality in patients undergoing emergency general surgery: a multisite study of emergency general services in a single academic network
Bibliographic record
Abstract
Background: Despite the widespread implementation of the acute care surgery (ACS) model, limited access to operating room time represents a barrier to the optimal delivery of emergency general surgery (EGS) care. The objective of this study was to describe the effect of operative timing on outcomes in EGS in a network of teaching hospitals. Methods: We conducted a retrospective review of EGS operations performed at 3 teaching hospitals in a single academic network. Time of operation was categorized as daytime (8 am to 5 pm), after hours (5 pm to 11 pm) or overnight (11 pm to 8 am). Time to operation was calculated as the interval from admission to operative start time and categorized as less than 24 hours, 24-72 hours and greater than 72 hours. Results: After we excluded nonindex cases, trauma cases and cases occurring more than 5 days after admission, 1505 EGS cases were included. We found that 39.0% of operations were performed in the daytime, 46.3% after hours and 14.8% overnight. In terms of time to operation, 52.3% of operations were performed within 24 hours of admission, 33.4% in 24-72 hours and 14.3% in more than 72 hours. The overall complication rate was 20.6% (310 patients) and the overall mortality rate was 3.8% (57 patients). After multivariable analysis, time to operation more than 72 hours after admission was independently associated with increased odds of morbidity (odds ratio [OR] 1.64, 95% confidence interval [CI] 1.09-2.47), while overnight operating was associated with increased odds of death (OR 3.15, 95% CI 1.29-7.70). Conclusion: Increasing time from admission to operation and overnight operating were associated with greater morbidity and mortality, respectively, for EGS patients. Strategies to provide timely access to the operating room should be considered to optimize care in an ACS model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".