Meta-analysis of gabapentin in the treatment of postherpetic neuralgia
Bibliographic record
Abstract
Objective To evaluate the effects and tolerability of gabapentin in the treatment of postherpetic neuralgia (PHN). Methods The randomized controlled trials (RCTs) of gabapentin for the treatment of PHN were retrieved from PubMed, Cochrane Central Register of Controlled Trials, EMBASE, Chinese Biology Medicine (CBM), VIP, China National Knowledge Infrastructure (CNKI) and Wanfang Data. Two reviewers independently evaluated the quality of the included articles and abstracted the data. Meta-analysis was performed using RevMan 5.0. Results According to the enrollment criteria, 5 prospective, randomized controlled clinical trials including 1225 subjects were finally selected. The mean changes for average daily pain score were significant in gabapentin group compared with placebo group (SMD = -0.920, 95%CI: -1.330~-0.520; P = 0.000), as well as the mean change for Short-Form McGill Pain Questionnaire (SF-MPQ) visual analogue scale (SMD = -2.650, 95%CI: -3.410~-1.890; P = 0.000) and average daily sleep intervention score (SMD = -2.480, 95%CI: -3.750~-1.200; P = 0.000). There was no significance between gabapentin group and placebo group in the withdrawl rate (P > 0.05). The common adverse reactions during gabapentin treatment included dizziness (OR = 3.710, 95%CI: 2.530-5.430; P = 0.000), somnolence (OR = 2.430, 95%CI: 1.530-3.860; P = 0.000) and peripheral edema (OR = 13.570, 95%CI: 4.190-43.970; P = 0.000). Conclusion It is indicated clinically that gabapentin is effective and well tolerated for the treatment of postherpetic neuralgia with high retention rate. However, adverse reactions, such as dizziness, somnolence and edema, should be paid attention.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.064 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".