The efficacy of repetitive transcranial magnetic stimulation in postherpetic neuralgia: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Purpose This systematic review and meta-analysis aimed to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in postherpetic neuralgia (PHN). Methods Through an extensive search in four databases until October 2023, we selected five randomized controlled trials adhering to our specific criteria, involving 257 patients in total. For continuous outcomes, the standardized mean difference (SMD) was calculated. Heterogeneity among the studies was assessed using Cochran’s I2 and Q statistics, adopting a random-effects model for I2 values over 50%. For assessing potential publication bias, we utilized both funnel plot and Egger’s test. Results Our analysis found that rTMS reduced the overall visual analogue scale (VAS) (SMD: −1.52, 95% CI: −2.81 to −0.23, p = 0.02), VAS at 1 month post-treatment (SMD: −2.21, 95% CI: −4.31 to −0.10, p = 0.04), VAS at 3 months post-treatment (SMD: −1.51, 95% CI: −2.81 to −0.22, p = 0.02), as well as patients’ global impression of change scale (PGIC) (SMD: −1.48, 95% CI: −2.87 to −0.09, p = 0.04) and short-form McGill pain questionnaire (SF-MPQ) (SMD: −1.25, 95% CI: −2.41 to −0.09, p = 0.03) compared to the sham-rTMS group. Conclusion Our study suggests that rTMS might have a potential alleviating effect on PHN symptoms. However, due to the limited number of studies and variations in rTMS parameters, larger sample studies involving more diverse populations, as well as further clarification of the most appropriate stimulation protocol, are still needed. Systematic review registration https://www.crd.york.ac.uk/prospero/ , Identifier ID: CRD42023488420.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.049 |
| Bibliometrics | 0.009 | 0.008 |
| 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) | 0.003 | 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".