Does alvimopan enhance return of bowel function in laparoscopic gastrointestinal surgery? A meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Postoperative ileus (POI) remains a major impediment in patient recovery and leads to longer lengths of stay at the hospital, readmission rates, and hospital costs. Alvimopan, a mu-opioid receptor antagonist, lowers POI incidence following open gastrointestinal surgery, however, little is known about its role on POI prevention among patients undergoing laparoscopic gastrointestinal surgery. METHODS: A comprehensive search of PubMed/MEDLINE, Scopus, CINAHL, and Cochrane databases was performed (December 2014). Meta-analysis was performed using the Mantel-Haenszel (fixed effects) model with odds ratio (OR) to assess prevention of POI and hospital readmission. RESULTS: Five studies were included in the final analysis. Pooling 4 of 5 studies, there was over a 75% relative risk reduction in POI development when patients were given alvimopan compared to placebo (OR 0.24, 95%CI 0.12-0.51, P=0.02). The number needed to treat with alvimopan to prevent one POI episode was 11 patients. There was a modest reduction in the length of hospitalization between 0.2 and 1.6 days. There did not appear to be a difference in frequency of 30-day readmission rate among the alvimopan group compared to placebo (OR 1.15, 95%CI 0.54-2.45, P=0.62). CONCLUSION: Overall, there was a 75% relative risk reduction in POI development among patients undergoing laparoscopic gastrointestinal surgery. However, there did not appear to be a significant reduction in all-cause 30-day readmission rate or length of hospitalization. Future studies will need to address which subset of patients undergoing laparoscopic gastrointestinal surgery will benefit most from alvimopan.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".