Botulinum toxin A treatment for post‑herpetic neuralgia: A systematic review and meta‑analysis
Bibliographic record
Abstract
The present meta-analysis study aimed to investigate the safety and efficacy of local administration of botulinum toxin (BTX-A) vs. lidocaine in the treatment of post-herpetic neuralgia. A systematic search of the Cochrane Library, PubMed, Embase, Chinese National Knowledge Infrastructure, Wanfang, Chongqing VIP Information Co. and Chinese Biomedical Literature Database was performed to identify randomized controlled trials (RCTs) comparing BTX-A and lidocaine in the treatment of post-herpetic neuralgia. The primary outcomes were Visual Analogue Scale (VAS) pain scores at 1, 2 and 3 months after treatment and the effective rate. Secondary outcomes were scores on the McGill pain questionnaire and adverse event rate. A total of 7 RCTs comprising 752 patients were included. The VAS pain score was significantly lower at 1 month [mean difference (MD)=-2.31; 95% CI: -3.06, -1.56; P<0.00001)], 2 months (MD=-2.18; 95% CI: -2.24, -2.11; P<0.00001) and 3 months (MD=-1.93; 95% CI: -3.05, -0.82; P=0.0007) after treatment, the effective rate was significantly higher (odds ratio=2.9; 95% CI: 1.71, 4.13; P<0.0001) and scores on the McGill pain questionnaire were significantly lower (MD=-10.93; 95% CI: -21.02, -0.83; Z=2.12; P=0.03) in patients who received BTX-A for post-herpetic neuralgia compared to those who received lidocaine. There was no difference in the adverse event rate between treatments. In conclusion, BTX-A has potential as a safe and effective treatment option for post-herpetic neuralgia. Further large and well-designed RCTs are required to confirm this conclusion.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".