Component-based approach of enhanced recovery after surgery protocols in bariatric surgery: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Introduction: Enhanced recovery after surgery (ERAS) protocols are evidence-based care improvement processes designed to minimize and reduce the negative physiological consequences of surgery. While previous studies have investigated ERAS in bariatric surgery, none have evaluated which specific components contribute most significantly to improved outcomes. Methods: We performed a systematic review and meta-analysis following PRISMA 2020 guidelines. Six randomized controlled trials (RCTs) with total of 740 patients comparing ERAS protocols to standard care in bariatric surgery were included. We conducted component-specific meta-regression analysis of 14 individual ERAS elements, dose-response analysis across three implementation levels (low: ≤4 components, medium: 5-8 components, high: ≥9 components), and component clustering to identify synergistic combinations. Meta-regression was used to determine the relative impact of individual components on recovery and safety outcomes. Results: Six RCTs including a total of 740 patients were included. Patients randomized to ERAS protocols have experienced significant reductions in nausea and vomiting (OR: 0.42, 95 % CI: 0.19-0.95, P-value = 0.040), intraoperative time (MD: 5.40, 95 % CI: 3.05-7.77, P-value<0.001), time to mobilization (MD: 3.78, 95 % CI: 5.46 to -2.10, P-value<0.001), intensive care unit length of stay (MD: 0.70, 95 % CI: 0.13-1.27, P-value = 0.020), total hospital stay (MD: 0.42, 95 % CI: 0.69 to -0.16, P-value = 0.002), and functional hospital stay (MD: 0.60, 95 % CI: 0.98 to -0.22, P-value = 0.002). Component-based analysis demonstrated that early mobilization, anti-emetic protocols, optimized anesthesia, and multimodal analgesia contributed most significantly to improved outcomes. We observed a clear dose-response relationship, with greater benefits in studies implementing more ERAS components. Conclusion: ERAS protocols significantly improve recovery metrics following bariatric surgery, with certain components demonstrating greater impact than others. Early mobilization and anti-emetic protocols appear particularly beneficial, while the "Complete Recovery Bundle" demonstrates synergistic effects. We recommend a tiered implementation approach, prioritizing high-impact components, especially in resource-limited settings.
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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 | 0.040 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.056 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".