A placebo-controlled randomized clinical trial of perioperative administration of gabapentin, rofecoxib and their combination for spontaneous and movement-evoked pain after abdominal hysterectomy
Bibliographic record
Abstract
Current treatments for post-injury movement-evoked pain are inadequate. Non-opioids may complement opioids, which preferentially reduce spontaneous pain, but most have incomplete efficacy as single agents. This trial evaluates efficacy of a gabapentin-rofecoxib combination following hysterectomy. In addition to IV-PCA morphine, 110 patients received either placebo, gabapentin (1800 mg/day), rofecoxib (50 mg/day) or a gabapentin-rofecoxib combination (1800/50 mg/day) starting 1 h pre-operatively for 72 h. Outcomes included pain at rest, evoked by sitting, peak expiration and cough, morphine consumption and peak expiratory flow (PEF). For placebo, gabapentin, rofecoxib and combination, 24 h pain (100 mm VAS) was: at rest-23.6 (P<0.05 vs. all treatments), 13.8, 14.4 and 12.1; during cough-50.7 (P<0.05 vs. all treatments), 41.5, 44.8 and 30.8; 48 h morphine consumption (mg) was: 130.4 (P<0.05 vs. all treatments), 81.7, 75.6 and 57.2 (P<0.05 vs. gabapentin and rofecoxib) and 48 h PEF (% baseline) was: 63.9 (P<0.05 vs. all treatments), 77.2, 76.7 and 87.5 (P<0.05 vs. gabapentin and rofecoxib). Adverse effects were similar in all groups except sedation which was more frequent with gabapentin. Combination and rofecoxib reduced pain interference with movement, mood and sleep (P<0.05) and combination was superior to gabapentin for all these three (P<0.05). These data suggest that a gabapentin-rofecoxib combination is superior to either single agent for postoperative pain. Other benefits include opioid sparing, reduced interference with movement, mood and sleep and increased PEF suggesting accelerated pulmonary recovery. Future research should identify optimal dose-ratios for this and other analgesic combinations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".