Management of Myofascial Pain of Upper Trapezius: A Three Group Comparison Study
Bibliographic record
Abstract
UNLABELLED: It is important to identify the most effective therapeutic modality in the management of myofascial trigger points (MTPt). Thus we aimed to study the effect of therapeutic ultrasound, laser and ischemic compression in reducing pain and improving cervical range of motion among patients with MTPt. Experimental study comparing three groups was designed as a 5 days trial, a co-relational design was considered. OUTCOME MEASURES: VAS for pain, provocative pain test using "soft tissue tenderness grading scheme" and active cervical lateral flexion using inch tape. Methods- Patients were divided into 3 groups, Gr 1 underwent treatment using therapeutic ultrasound, Gr 2 with therapeutic laser and Gr 3 with ischemic compression. Assessments were done on day 1 and day 5 of treatment respectively. RESULTS: ANOVA revealed improvement among all 3 groups as statistically significant difference (p<0.05) between the start and end of trial. Analysis using Chi square test shows a statistically significant difference in the improvement between laser and the other 2 groups. Mean difference in the change of scores between the assessments showed laser therapy to have a tendency towards progressive improvement over the treatment period and a better improvement than the other 2 groups. We conclude that laser can be used as an effective treatment regimen in the management of myofascial trigger points thereby reducing disability caused due to musculoskeletal pathology.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".