Impact of an enhanced recovery after surgery program with a multimodal analgesia care pathway on opioid prescribing and clinical outcomes for patients undergoing colorectal surgery
Bibliographic record
Abstract
Background Opioids are a mainstay for acute pain management, but their side effects can adversely impact patient recovery. Multimodal analgesia (MMA) is recommended for treatment of postoperative pain and has been incorporated in enhanced recovery after surgery (ERAS) protocols. The objective of this quality improvement study was to implement an MMA care pathway as part of an ERAS program for colorectal surgery and to measure the effect of this intervention on patient outcomes and costs.Methods This pre-post study included 856 adult inpatients who underwent an elective colorectal surgery at three hospitals within an integrated healthcare system. The impact of ERAS program implementation on opioid prescribing practices, outcomes, and costs was examined after adjusting for clinical and demographic confounders.Results Improvements were seen in MMA compliance (34.0% vs 65.5%, P < 0.0001) and ERAS compliance (50.4% vs 57.6%, P < 0.0001). Reductions in mean days on opioids (4.2 vs 3.2), daily (51.6 vs 33.4 mg) and total (228.8 vs 112.7 mg) morphine milligram equivalents given during hospitalization, and risk-adjusted length of stay (4.3 vs 3.6 days, P < 0.05) were also observed.Conclusions Implementing ERAS programs that include MMA care pathways as standard of care may result in more judicious use of opioids and reduce patient recovery time.
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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 | 0.003 | 0.009 |
| 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) | 0.001 | 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".