Analgesic Effects of Repetitive Transcranial Magnetic Stimulation at Different Stimulus Parameters for Neuropathic Pain: A Randomized Study
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to investigate the analgesic effects of repetitive transcranial magnetic stimulation over the primary motor cortex (M1-rTMS) using different stimulation parameters to explore the optimal stimulus condition for treating neuropathic pain. MATERIALS AND METHODS: We conducted a randomized, blinded, crossover exploratory study. Four single sessions of M1-rTMS at different parameters were administered in random order. The tested stimulation conditions were as follows: 5-Hz with 500 pulses per session, 10-Hz with 500 pulses per session, 10-Hz with 2000 pulses per session, and sham stimulation. Analgesic effects were assessed by determining the visual analog scale (VAS) pain intensity score and Short-Form McGill Pain Questionnaire 2 (SF-MPQ2) score immediately before and immediately after intervention. RESULTS: We enrolled 22 adults (age: 59.8 ± 12.1 years) with intractable neuropathic pain. Linear-effects models showed significant effects of the stimulation condition on changes in VAS pain intensity (p = 0.03) and SF-MPQ2 (p = 0.01). Tukey multiple comparison tests revealed that 10-Hz rTMS with 2000 pulses provided better pain relief than sham stimulation, with greater decreases in VAS pain intensity (p = 0.03) and SF-MPQ2 (p = 0.02). CONCLUSIONS: The results of this study suggest that high-dose stimulation (specifically, 10-Hz rTMS at 2000 pulses) is more effective than lower-dose stimulation for treating neuropathic pain.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".