Analgesic effects of gabapentin after scoliosis surgery in children: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Postoperative pain remains an important challenge after scoliosis surgery in children. Opioids are the mainstay of treatment, and adult studies demonstrate gabapentin as a useful adjunct to opioids in the management of postoperative pain. METHOD: Adolescent patients undergoing idiopathic scoliosis surgery were randomized to receive a single preoperative dose of gabapentin 600 mg or placebo. The primary outcome measure was total morphine consumption in mg·kg(-1) between 0 and 24 h postoperatively. Secondary outcome measures included time to first rescue analgesia, pain intensity scores at rest and with movement, incidence of nausea, vomiting, pruritus, sedation, dizziness, presence of persisting pain symptoms, and patient satisfaction. Cumulative opioid consumption was calculated at each time point: 1, 4, 8, 12, 24, 48, and 72 h. RESULTS: The gabapentin group used 0.087 ± 0.06 mg·kg(-1) of morphine at 1 h, 0.24 ± 0.12 mg·kg(-1) at 4 h, 0.44 ± 0.17 mg·kg(-1) at 8 h, and 1.29 ± 0.44 mg·kg(-1) at 24 h. The placebo group used 0.121 ± 0.06 mg·kg(-1) of morphine at 1 h, 0.35 ± 0.16 mg·kg(-1) at 4 h, 0.56 ± 0.27 mg·kg(-1) at 8 h, and 1.46 ± 0.68 mg·kg(-1) at 24 h. There was no statistically significant reduction in opioid consumption in the patients receiving gabapentin. There were no significant differences in secondary outcomes. CONCLUSION: A single preoperative dose of gabapentin did not show a significant difference in opioid consumption or pain scores in adolescents undergoing idiopathic scoliosis surgery. This study is the first pediatric randomized controlled trial to assess the effectiveness of a single dose of gabapentin on morphine consumption and analgesia following major surgery.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".