Ultrasound-Guided Dry Needling for Trigger Point Inactivation in the Treatment of Postherpetic Neuralgia Mixed with Myofascial Pain Syndrome: A Prospective and Controlled Clinical Study
Bibliographic record
Abstract
Objective: To evaluate the safety and effectiveness of ultrasound-guided dry needling for trigger point inactivation in the treatment of postherpetic neuralgia (PHN) mixed with myofascial pain syndrome (MPS). Methods: = 26). Visual analogue score (VAS) and McGill Pain Questionnaire (MPQ) were taken as primary indicators. Ultrasound-guided inactivation of myofascial trigger points (MTrPs) with dry needling and intradermal needling combined with press needling were applied on group D and pharmacotherapeutic only treatment on group P respectively. The VAS score <3 and/or the MPQ score <2 represents effective treatment. The VAS score >3 and/or the MPQ score >2 represents recurrent in follow-up study three months after the treatment. Results: After four weeks treatment, the effective rate of one month later of the group D was 92.9% and the effective rate of group P was 38.5%, respectively. The recurrent rate of group D was 7.1% and 34.6% for group P, respectively, for follow-up three months later. The satisfactory rate of group D was higher than that of group P. Conclusion: Ultrasound-guided dry needling and intradermal needling combined with press needling were more effective than only pharmacotherapeutic treatment for PHN mixed with MPS, with lower recurrent rate and higher patient's satisfactory rate.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".