A clinical study of ultrasound-guided acupotomy combined with mindfulness meditation to improve lumbar myofascial pain syndrome
Bibliographic record
Abstract
Objective To evaluate the efficacy and safety of ultrasound-guided acupotomy combined with mindfulness meditation for the treatment of lumbar myofascial pain syndrome (MPS). Methods This blinded randomized controlled trial lasted for 3 weeks and included a 90-day follow-up. The participants were 120 patients with lumbar MPS. These patients were randomized into three groups: Group A (ultrasound-guided acupotomy combined with mindfulness meditation, n = 40), Group B (ultrasound-guided acupotomy, n = 40), and Group C (celecoxib, n = 40). Data were collected at baseline, week 1, week 2, week 3 (posttreatment) and day 90 (follow-up). Results Group A was superior to Group B and significantly superior to Group C in terms of pain level (measured via the McGill Pain Questionnaire), lumbar spine mobility and mood disorders (measured via the Hospital Anxiety and Depression Scale), and quality of sleep (measured via the Pittsburgh Sleep Quality Index) (p < 0.05). Furthermore, the treatment efficacy was more durable in Group A (there was no significant rebound at 90 days of follow-up). The TNF-α and IL-1β serum levels were reduced in all three groups at week 3 but were more pronounced in the celecoxib group. Compared with ultrasound-guided acupotomy and celecoxib, ultrasound-guided acupotomy combined with mindfulness meditation can rapidly relieve pain, improve lumbar spine function, and permanently improve patients’ psychological state and sleep quality through the dual mechanism of “peripheral relaxation-central regulation.” This approach can treat the lumbar MPS from the root and provides new ideas for the clinical diagnosis and treatment of chronic 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".