Effect of a Preoperative Gabapentin on Postoperative Analgesia in Patients with Inflammatory Bowel Disease Following Major Bowel Surgery: A Randomized, Placebo‐Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Postoperative pain management for patients with inflammatory bowel disease (IBD) can be challenging. These patients have a high tolerance to pain medication, and relative contraindications to the use of epidural analgesia, limiting the pain management options. We evaluated the effect of a single preoperative gabapentin dose on opioid consumption for patients with IBD undergoing abdominal surgery. Secondary outcomes were postoperative pain scores, opioid-related side effects, and patient's length of hospital stay. METHODS: Following Research Ethics Board approval and informed written consent, patients were randomly allocated into 2 groups receiving either 600 mg of oral gabapentin or placebo 1 hour before the surgery. A blinded anesthesiologist recorded pain scores at rest and movement twice daily for 2 postoperative days. Also recorded were opioid consumption, time of return of bowel function, time to discharge, and opioid-related side effects on the opioid-related symptom distress scale (ORSDS). RESULTS: Seventy-two patients completed the study. The difference in opioid consumption (P = 0.4169) and pain scores measured at rest and movement on all 4 postoperative visits was not statistically significant. There was no significant difference between gabapentin and placebo on all the 11 symptoms reported on the ORSDS. There was a slight increase in length of hospital stay in the placebo group, but the return of bowel function was similar between the groups. CONCLUSIONS: This study examined the effect of a single preoperative administration of gabapentin in patients with IBD undergoing major bowel surgery. Our results suggest a single preoperative oral dose of gabapentin 600 mg does not reduce postoperative pain scores, opioid consumption, or opioid-related side effects.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".