A Perioperative Course of Gabapentin Does Not Produce a Clinically Meaningful Improvement in Analgesia after Cesarean Delivery
Bibliographic record
Abstract
BACKGROUND: Studies examining the efficacy of a single preoperative dose of gabapentin for analgesia after cesarean delivery (CD) have been inconclusive. The authors hypothesized that a perioperative course of gabapentin would improve analgesia after CD. METHODS: This single-center, randomized, double-blind, placebo-controlled, parallel-group, superiority trial was designed to determine the analgesic efficacy of a perioperative course of gabapentin when added to a multimodal analgesic regimen. Women scheduled for elective CD during spinal anesthesia were randomized to receive a perioperative oral course of either gabapentin (600 mg preoperatively followed by 200 mg every 8 h for 2 days) or placebo. Postoperative pain was measured at 24 and 48 h, at rest and on movement, on a visual analogue scale (VAS, 0 to 100 mm). The primary outcome was pain on movement at 24 h. Neonatal outcomes, opiate consumption, VAS satisfaction (0 to 100 mm), adverse effects, and persistent pain were also assessed. RESULTS: Baseline characteristics were similar between groups. There was a statistically significant but small reduction in VAS pain score (mean [95% CI]) on "movement" (40 mm [36 to 45] vs. 47 mm [42 to 51]; difference, -7 mm [-13 to 0]; P = 0.047) at 24 h in the gabapentin (n = 100) compared with control group (n = 97). There was more sedation in the gabapentin group at 24 h (55 vs. 39%, P = 0.026) but greater patient VAS satisfaction (87 vs. 77 mm, P = 0.003). CONCLUSIONS: A perioperative course of gabapentin produces a clinically insignificant improvement in analgesia after CD and is associated with a higher incidence of sedation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".