Postoperative ERAS Interventions Have the Greatest Impact on Optimal Recovery
Bibliographic record
Abstract
BACKGROUND: Enhanced recovery after surgery (ERAS) programs incorporate evidence-based practices to minimize perioperative stress, gut dysfunction, and promote early recovery. However, it is unknown which components have the greatest impact. OBJECTIVE: This study aims to determine which components of ERAS programs have the largest impact on recovery for patients undergoing colorectal surgery. METHODS: An iERAS program was implemented in 15 academic hospitals. Data were collected prospectively. Patients were considered compliant if >75% of the preoperative, intraoperative, and postoperative predefined interventions were adhered to. Optimal recovery was defined as discharge within 5 days of surgery with no major complications, no readmission to hospital, and no mortality. Multivariable analysis was used to model the impact of compliance and technique on optimal recovery. RESULTS: Overall, 2876 patients were enrolled. Colon resections were performed in 64.7% of patients and 52.9% had a laparoscopic procedure. Only 20.1% of patients were compliant with all phases of the pathway. The poorest compliance rate was for postoperative interventions (40.3%) which was independently associated with an increase in optimal recovery (RR = 2.12, 95% CI 1.81-2.47). Compliance with ERAS interventions remained associated with improved outcomes whether surgery was performed laparoscopically (RR = 1.55, 95% CI 1.23-1.96) or open (RR = 2.29, 95% CI 1.68-3.13). However, the impact of ERAS compliance was significantly greater in the open group (P < 0.001). CONCLUSIONS: Postoperative compliance is the most difficult to achieve but is most strongly associated with optimal recovery. Although our data support that ERAS has more effect in patients undergoing open surgery, it also showed a significant impact on patients treated with a laparoscopic approach.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".