Impact of the Acute Care Surgery Model on Disease- and Patient-Specific Outcomes in Appendicitis and Biliary Disease: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The acute care surgery (ACS) model was developed to acknowledge the complexity of a traditionally fractured emergency general surgery patient population, however, there are variations in the design of ACS service models. This meta-analysis analyzes the impact of implementation of different ACS models on the outcomes for appendicitis and biliary disease. STUDY DESIGN: A systematic, English-language search of major databases was conducted. From 1,827 papers, 2 independent reviewers identified 25 studies that reported on outcomes for patients with appendicitis (n = 13), biliary disease (n = 7), or both (n = 5), before and after implementation of an ACS service. The Newcastle-Ottawa Scale was used to score quality. Outcomes were analyzed using random effect methodology and sensitivity analyses were performed. RESULTS: Significant heterogeneity existed between studies and ACS designs. The overall study quality rating was fair to poor with a moderate risk of bias. After implementation of an ACS service, there was an overall reduction in length of stay by 0.51 days (95% CI -0.81 to -0.20 days) and 0.73 days (95% CI 0.09 to 1.36 days) for appendicitis and biliary disease, respectively. Complication rates were lower after implementing ACS (odds ratio 0.65; 95% CI 0.49 to 0.86 and odds ratio 0.46; 95% CI 0.34 to 0.61). There was no difference in after-hours operating for either appendicitis or biliary disease, except when considering ACS models with dedicated theater time, which favors an ACS model (odds ratio 0.49; 95% CI 0.33 to 0.73) in appendicitis. CONCLUSIONS: The ACS model has been shown to benefit acute care surgery patients with improved access to care, fewer complications, and decreased length of stay for 2 common disease processes. The design and implementation of an ACS service can impact the magnitude of effect.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.098 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".