High-Frequency Repetitive Transcranial Magnetic Stimulation Reduces Pain in Postherpetic Neuralgia
Bibliographic record
Abstract
OBJECTIVES: Postherpetic neuralgia (PHN) is one of the most intractable pain disorders, especially in elderly patients. There is evidence that repetitive transcranial magnetic stimulation (rTMS) reduces neuropathic pain; however, its effectiveness for PHN is unknown. This study investigated the efficacy of high-frequency rTMS in patients with PHN. DESIGN: A total of 40 patients were randomly assigned to receive 10 sessions of real or sham rTMS of the primary motor cortex. Each stimulation session consisted of a series of 300 five-second pulses with a frequency of 10 Hz and an interval of 3 seconds between each train, giving a total of 1500 pulses per session. The primary outcome was pain intensity measured before stimulation from first intervention (T0) to the final stimulation (T10), and 1 and 3 months after final stimulation (T11 and T12). Other outcomes measured included scores on the short form McGill pain questionnaire, self-rating depression scale, quality of life (QOL), sleep quality, the patient global impression of change, medication regulation, and reported adverse events. RESULTS: The real rTMS group demonstrated greater reduction of visual analogue scale (VAS) than the sham group at each time point except for T0 (P = 0.399) and T1 (P = 0.091). Mean VAS reduction in the real rTMS group was 16.89% for duration of disease longer than 6 months. These analgesic effects were associated with long-term improvement in rating-scale items related to QOL. CONCLUSION: The results suggest that rTMS is an effective and safe therapy in patients with PHN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".