Ultrasound-Guided Inactivation of Trigger Points Combined with Muscle Fascia Stripping by Liquid Knife in Treatment of Postherpetic Neuralgia Complicated with Abdominal Myofascial Pain Syndrome: A Prospective and Controlled Clinical Study
Bibliographic record
Abstract
Objective: To evaluate ultrasound-guided inactivation of myofascial trigger points (MTrPs) combined with abdominal muscle fascia stripping by liquid knife in the treatment of postherpetic neuralgia (PHN) complicated with abdominal myofascial pain syndrome (AMPS). Methods: = 33) including those with PHN complicated with AMPS. All groups received zero-grade treatment: routine oral drugs and weekly paraspinal nerve block. PL and PA groups were also treated step by step once a week: primary ultrasound-guided inactivation of MTrPs with dry needling, secondary ultrasound-guided inactivation of MTrPs with dry and wet needling, and tertiary ultrasound-guided dry and wet needling combined with muscle fascia stripping by liquid knife. At one week after primary treatment, patients with a VAS score > 2 proceeded to secondary treatment. If the VAS score was <2, the treatment was maintained, and so on, until the end of the four treatment cycles. Pain assessment was performed by specialized nurses at one week after each treatment, including VAS score, McGill pain questionnaire (MPQ) score, pressure pain sensory threshold (PPST), and pressure pain tolerance threshold (PPTT). VAS score was used as the main index and VAS <2 indicated effective treatment. At 3 months after treatment, outpatient and/or telephone follow-up was performed. The recurrence rate was observed and VAS > 2 was regarded as recurrence. Results: > 0.05). Conclusion: About 57% of PHN patients with mild to moderate pain are complicated with MPS, and ultrasound-guided inactivation of MTrPs with dry and wet needling can effectively treat PHN patients complicated with LMPS. However, patients with PHN complicated with AMPS need to be treated with ultrasound-guided MTrPs inactivation combined with muscle fascia stripping by liquid knife as soon as possible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".