Preliminary Study: Short-term Beneficial Effects of Thai Tok Sen Massage on Pain, Pressure Pain Threshold, and Upper Trapezius Muscle Thickness Among People with Shoulder Pain
Bibliographic record
Abstract
Background: Shoulder muscle pain and spasm is the most common problem in people after prolonged working, similar to that resulting from office syndrome. Various medicinal treatments with analgesic drugs, hot packs, therapeutic ultrasound, or deep friction techniques can be clinically applied. Alternatively, traditional Thai massage (TM) with deep compression gentle technique also can help to release that problem. In addition, an traditional Thai treatment with Tok Sen (TS) massage has been generally performed in the Northern part of Thailand without any scientific evidence support. Thus, the aim of this preliminary study was to reveal the scientific value of Tok Sen massage on shoulder muscle pain and upper trapezius muscle thickness among people with shoulder pain. Materials & Methods: Twenty participants (6 males and 14 females) who suffered from shoulder pain were randomized into TS (n =10, aged 34.2 ± 7.34 yrs) or TM (n=10, aged 32.8 ± 7.24 yrs). Each group received two times 5–10 minutes of treatment, one week apart. At the baseline and after completing two times of each intervention, pain score, pain pressure threshold (PPT), and specific trapezius muscle thickness were evaluated. Results: Before both TM and TS interventions, pain score, PPT, and muscle thickness were not statistically different between groups. After two times of intervention, pain scores significantly reduced in TM (3.1 ± 0.56; p = .02, 2.3 ± 0.48; p < .001), as same as in TS (2.3 ± 0.67; p = .01, 1.3 ± 0.45; p < .001) when compared to baseline. This was the same as the results of PPT in TM (4.02 ± 0.34; p = .012, 4.55 ± 0.42; p = .001) and TS (5.67 ± 0.56; p = .001, 6.8 ± 0.72; p < .001). However, the trapezius muscle thickness reduced significantly after two interventions by TS (10.42 ± 1.04; p = 0.002 & 9.73 ± 0.94 mm, p < .001), but did not change in TM (p > .05). Moreover, when compared between intereventions at the first and second periods, TS showed a significant difference in pain score (p = .01 & p <.001), muscle thickness (p = .008 & p = .001) as well as PPT (p < .001 & p < .001) when compared to TM. Conclusion: Tok Sen massage improves upper trapezius thickness from muscle spasms and reduces pain perception and increases the pressure threshold pain among participants who suffer from shoulder pain similar to that of office syndrome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".