Effects of 10 Hz Repetitive Transcranial Magnetic Stimulation in Acute Human Muscle Pain Model
Bibliographic record
Abstract
Objective: To investigate the analgesic effect of high frequency repetitive transcranial magnetic stimulation (rTMS) on the experimental human muscle pain and its underlying mechanism. Method: Twenty healthy Korean volunteers participated in this study. The acute muscle pain was induced by infusion of hypertonic saline (5%) into the left extensor carpi radialis longus (ECRL) muscle. During the hypertonic saline injection, 10 Hz rTMS were applied on the hot spot of left ECRL. The changes of visual analogue scale (VAS) of muscle pain and motor evoked potential (MEP) were measured from the start of saline injection to 70 minutes after the start of stimulation. At 90 minutes after the first stimulation, the subjects completed the Korean version of the McGill Pain Questionnaire (MPQ). The sham stimulation was applied with the same method as rTMS experiment. Results: In rTMS, the VAS of muscle pain was significantly decreased from 2.5 minutes and continued until 3 minutes after the last rTMS. While the amplitude of MEP was significantly increased, the latency of MEP was significantly decreased after the start of rTMS and the effect on MEP continued until 5 minutes after the last rTMS. The quality of pain experiment by rTMS and sham stimulation showed no difference in MPQ. Conclusion: The present results suggested that 10 Hz rTMS over primary motor cortex decreased the perception of muscle pain and increased the excitability of corticospinal pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".