The clinical effects of repetitive transcranial magnetic stimulation with deep TMS H system for the treatment of intractable pain in lower extremities
Bibliographic record
Abstract
Background: In a previous study, we revealed that repetitive transcranial magnetic stimulation (rTMS)using figure-8 shaped coil could relieve neuropathic pain (NP) less effectively in the lower extremities than the upper ones. We speculated that it depended on the depth of the targeted primary cortex (M1).Deep rTMS is a novel development that can stimulate deep neuronal regions effectively, termed the H-coil. In this study, we compared the clinical effects for NP patients in their lower extremities between rTMS with H-coil, rTMS with figure-8 shaped coil and sham stimulation. Methods: This was a randomized, double-blind, three-way crossover trial.12 NP patients in their lower extremities received three types of stimulations for 5 consecutive days with 17 days follow-up. In each rTMS session, 5-Hz rTMS to M1 corresponding to the painful lower extremity was administered. Outcome measures were visual analogue scale (VAS) and Japanese version of the short form of the McGill pain questionnaire 2 (SF-MPQ2). Results: H-coil rTMS, compared with the sham, showed significant pain improvement soon and one hour after rTMS in VAS (p<0.001). On the other hand, pain improvement after rTMS with figure-8 shaped coil was not significant in VAS. Both types of rTMS didn't show significant pain improvement in SF-MPQ2.No serious adverse events were observed. Conclusions: Our findings demonstrated that rTMS with H-coil could be tolerable and provide modest pain relief in NP patients in their lower extremities.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".