Quantitative Ultrasound Texture Feature Changes With Conservative Treatment of the Trapezius Muscle in Female Patients With Myofascial Pain Syndrome
Bibliographic record
Abstract
OBJECTIVE: We set out to assess whether quantitative ultrasound could be used to assess changes that occur after physical therapy in patients experiencing myofascial pain syndrome. METHODS: We consecutively recruited female subjects experiencing myofascial pain syndrome of the neck and shoulder region and provided 10 sessions of conservative physical therapy. A control group was recruited for textural analyses. We measured change in pain ratings, range of motion, and ultrasound texture features before and after the intervention and after 3 mos. RESULTS: We recruited 63 female myofascial pain syndrome subjects and 20 healthy controls. After treatment, the mean blob size (an ultrasound texture feature) value for each subject decreased from 30.84 ± 5.00 to 25.86 ± 5.67 on the right and decreased from 31.70 ± 5.51 to 28.08 ± 5.53 on the left (P < 0.0005). The blob count showed a significant increase only on the left side (P < 0.01). Corresponding to this were reductions in pain and disability scores after treatment and at 3 mos compared with retreatment (P < 0.0005 for all checkpoints). Cervical range of motion values were significantly increased only at 3 mos compared with pretreatment except for mean flexion range of motion. CONCLUSIONS: Ultrasound texture feature of blob size and count changes correspond to routine clinical outcomes after conservative physical therapy of myofascial pain syndrome in female individuals.
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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) | 0.000 | 0.000 |
| 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".