Quantitative Ultrasound Assessment of Myofascial Pain Syndrome Affecting the Trapezius: A Reliability Study
Bibliographic record
Abstract
OBJECTIVES: Myofascial pain syndrome is one of the most common causes of chronic pain and is highlighted by the presence of myofascial trigger points. The current practice of diagnosing myofascial pain syndrome among clinicians involves manual detection of myofascial trigger points, which can be inconsistent. However, the detection process can be strengthened with the assistance of ultrasound (US). Therefore, this study aimed to characterize the upper trapezius by using quantitative techniques in healthy asymptomatic individuals with neck pain. METHODS: Study participants were recruited on the basis of the inclusion and exclusion criteria established, and US images of the trapezius, along the axial and longitudinal orientations, were obtained. Each set was obtained by 2 investigators: experienced and inexperienced personnel. RESULTS: Fifteen participants were recruited. The mean gray scale US echo intensity distribution obtained was 41.9. A paired t test of the global mean echo intensity value obtained for each image from the US operators did not show any significant difference (P = .77). A t test was performed, comparing the echo intensity of the group of patients with neck pain and healthy control participants, and the difference was found to be significant (P = .052). The median blob area was 2.71. The quartile range for the blob area was 1.72 for the 25th percentile to 4.90 for the 75th percentile. CONCLUSIONS: This study demonstrated that quantitative analysis of the echo intensity of US images can provide important information. However, further research is necessary to explore the relationships among sex, age, blob area, count, body mass index, regional anatomy, and extent of training or exercise of the particular muscle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".