The impact of ERAS protocols on postoperative outcomes in robotic, laparoscopic, and open colorectal surgery: A multicenter retrospective study
Bibliographic record
Abstract
BACKGROUND: Enhanced recovery after surgery (ERAS) protocols improve outcomes in colorectal surgery, but the impact of ERAS on comparative effectiveness across robotic, laparoscopic, and open approaches is unclear. METHODS: In this multicenter retrospective study, 5503 patients undergoing elective colorectal surgery from 13 centers could be included. Patients were compared regarding surgical technique and ERAS adherence. Primary outcomes included postoperative complications and anastomotic leakage; secondary outcomes included length of hospital stay (LOS) and Comprehensive Complication Index (CCI). Multivariate regression assessed ERAS impact. RESULTS: ERAS was used in 890 patients and associated with significantly shorter LOS across all modalities (robotic: 5.5 vs. 8.5 days; laparoscopic: 8.5 vs. 9.0 days; open: 13.1 vs. 14.5 days; p ≤ 0.02). ERAS independently reduced complication rates (p = 0.016) and anastomotic leaks (p = 0.007). Robotic surgery was protective against complications and linked to the greatest LOS reduction. ERAS did not significantly affect CCI. CONCLUSIONS: ERAS protocols improve postoperative outcomes across colorectal surgical techniques, with the greatest benefits in robotic surgery, supporting their broad clinical implementation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".