Stimulation of myofascial trigger points with ultrasound induces segmental antinociceptive effects: A randomized controlled study
Bibliographic record
Abstract
Musculoskeletal pain affects a significant proportion of the general population. The myofascial trigger point is recognized as a key factor in the pathophysiology of musculoskeletal pain. Ultrasound is commonly employed in the treatment and management of soft tissue pain and, in this study, we set out to investigate the segmental antinociceptive effect of ultrasound. Subjects (n=50) with identifiable myofascial trigger points in the supraspinatus, infraspinatus and gluteus medius muscles were selected from an outpatient rehabilitation clinic and randomly assigned to test or control groups. Test subjects received a therapeutic dose of ultrasound to the right supraspinatus trigger point while control groups received a sham (null) exposure. Baseline pain pressure threshold (PPT) readings were recorded at the ipsilateral infraspinatus and gluteus medius trigger-point sites prior to ultrasound exposure. The infraspinatus point was chosen due to its segmental neurologic link with the supraspinatus point; the gluteus medius acted as a segmental control point. Following the ultrasound intervention, PPT readings were recorded at 1, 3, 5, 10 and 15 min intervals at both infraspinatus and gluteus medius trigger points; the difference between infraspinatus and gluteus medius PPT values, PPT seg, represents the segmental influence on the PPT. The ultrasound test group demonstrated statistically significant increases in PPT seg (decreased infraspinatus sensitivity) at 1, 3 and 5 min, when compared with PPT seg in the sham ultrasound group. These results establish that low-dose ultrasound evokes short-term segmental antinociceptive effects on trigger points which may have applications in the management of musculoskeletal 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".