Acute Myotonometric Changes in the Masseter and Upper Trapezius Muscles After Upper Body Quarter Stretching and Coordination Exercises or Chewing
Bibliographic record
Abstract
Pathologies in neck and masticatory muscles affect muscle tone and biomechanical and viscoelastic properties, necessitating precise assessment for treatment. This study evaluated the impact of two guided interventions—relaxing exercises targeting the neck and masticatory muscles (“Exercise”) and heavy chewing using six chewing gums (“Chewing”)—on the masseter and upper trapezius muscles. Twelve participants (aged 19–40 years) underwent myotonometric assessment pre- and post-intervention, measuring tone, stiffness, decrement, relaxation time, and creep. The results showed significant changes in the masseter muscle after exercise, with increased stiffness (14.46%, p < 0.001) and tone (7.03%, p < 0.001) but decreased creep (−9.71%, p < 0.001) and relaxation time (−11.36%, p < 0.001). Conversely, chewing decreased stiffness (−8.82%, p < 0.001) and tone (−5.53%, p < 0.001), while it increased creep (9.68%, p < 0.001) and relaxation time (9.98%, p < 0.001). In the trapezius muscles, tone decreased after both interventions (Exercise: −7.65%, p < 0.001; Chewing: −1.06%, p = 0.003), while relaxation increased (Exercise: 1.78%, p < 0.001; Chewing: 2.82%, p < 0.001). These findings reveal the distinct effects of exercise and chewing on muscle properties, emphasising the complexity of their therapeutic potential and the need for further investigation.
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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".