Clinical efficacy of electromagnetic field therapy combined with traditional Chinese pain-reducing paste in myofascial pain syndrome
Bibliographic record
Abstract
BACKGROUND: Pulsed electromagnetic field (PEMF) therapy is widely used to treat myofascial pain syndrome (MPS). Damp-clearing and pain-reducing paste (DPP) comprises medical herbs and has been a traditional method of reducing myofascial pain in China for a long time, and it is usually administered with heating. However, the synergistic effect of PEMF therapy on heating-DPP in patients with MPS is unclear. AIM: To investigate the synergistic effect of PEMF therapy plus heating-DPP in lumbar MPS. METHODS: = 60). Patients in both groups were treated with heating-DPP combined with PEMF therapy; however, the electromagnetic function of the therapeutic apparatus used in the CG was disabled. Each treatment lasted for 20 min and was applied five times a week for two weeks. The short-form McGill Pain Questionnaire was applied at five time points: pretest, end of the first and second weeks of treatment, and end of the first and fourth week after completing treatment. Visual analog scale (VAS), present pain intensity index (PPI), and pain rating index (PRI; total, affective pain, and sensory pain scores) scores were then analyzed. RESULTS: Compared with the CG, the VAS, PPI and PRI scores (total, affective pain and sensory pain scores) in the EG were significantly lower after treatment and during follow-up. CONCLUSION: PEMF therapy combined with heating-DPP showed better efficacy than heating-DPP alone in reducing the overall intensity of pain and sensory and affective 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".