Effect of Repetitive Magnetic Stimulation and Transcutaneous Electrical Nerve Stimulation in Chronic Low Back Pain: A Pilot Study
Bibliographic record
Abstract
Objective : To evaluate the short and medium effect of peripheral repetitive magnetic stimulation therapy on chronic low back pain compared with transcutaneous electrical nerve stimulation therapy. Method : Twenty-three subjects with chronic low back pain were allocated randomly to repetitive magnetic stimulation group (n=13) and transcutaneous electrical nerve stimulation group (n=10). Each treatment consisted of 10-minutes sessions each day, totally 10 sessions over 2 weeks. Subjects were evaluated pre-treatment and post-treatment at 8 hours and 2 weeks. Outcome was measured with the Oswestry disability index, McGill pain questionnaire, and daily mean pain numeric rating scale. Results : At 8 hours and 2 weeks post-treatment, transcutaneous electrical nerve stimulation therapy group showed a significant improvement in the mean pain numeric rating scale. Two weeks post-treatment, transcutaneous electrical nerve stimulation therapy group showed a significant improvement in the Oswestry disability index. But there were no significant therapeutic effect of repetitive magnetic stimulation therapy group at all period. Conclusion : This study showed that repetitive magnetic stimulation therapy may be less effective than transcutaneous electrical nerve stimulation therapy for the treatment of chronic low back 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".