Efficacy of Myofascial Release Therapy and Positional Release Therapy in Patients with Upper Trapezius Trigger Points: Study Protocol of a Doubleblinded Randomized Clinical Trial
Bibliographic record
Abstract
Background: Myofascial trigger points are incredibly prevalent and are a painful aspect of almost everyone's life at some point. Myofascial trigger point pain can be excruciating and severely impair the quality of life. Therefore, in patients with neck pain caused by upper trapezius trigger, this current clinical trial will demonstrate the effectiveness of myofascial release therapy and positional release therapy in improving the level of pain, neck impairment, pain threshold, and standard of life. Methods: A double-blinded randomized clinical trial will be conducted. Fifty-two participants with active myofascial trigger points in the upper trapezius muscle will be recruited based on selection criteria. They will be randomly allocated into group A (conservative treatment + myofascial release technique) or group B (conservative treatment + positional release technique). Both groups will receive the intervention three times a week for 2 weeks. The study will use the Numeric Pain Rating Scale, pressure algometer, Neck Disability Index, and a 36-Item Short-form Questionnaire as outcome measures. Discussion: This trial will help identify the effectiveness of the positional and myofascial release techniques in active upper trapezius muscle trigger points and their effect on physical parameters. Trial Registration: This trial has been prospectively registered at the Clinical Trials Registry-India (CTRI/2023/07/055126) on 12 July 2023.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".