Myotonometry Reliably Measures Muscle Stiffness in the Thenar and Perineal Muscles
Bibliographic record
Abstract
Purpose: The authors investigated the reliability of myotonometry-measured muscle tone in the thenar and perineal muscles. Methods: Participants were women aged 18–50 years who were asymptomatic for thumb and pelvic floor dysfunction (interrater study n=20; intrarater study n=43) or who were symptomatic for vulvodynia (interrater study n=14; intrarater study n=32). Mechanical properties (stiffness, frequency, decrement, relaxation time, and creep) of the muscles were measured using a myotonometer (MyotonPRO) while the muscles were in a relaxed state. Measures were performed twice by two assessors. Intra- and interrater reliability were determined using intra-class correlation coefficients (ICCs) and absolute reliability using the standard error of measurement and a minimum detectable change. Results: The primary property of interest, muscle stiffness, showed very good interrater (ICC 0.85–0.86) and intrarater (ICC 0.82–0.88) reliability in the thenar eminence. In the perineal muscles, reliability results ranged from good to very good for interrater (ICC 0.70–0.86) and intrarater (ICC 0.80–0.91) reliability for muscle stiffness. Absolute reliability was confirmed, with all measures showing minimal variance. Conclusions: Muscle stiffness of the smaller muscles of the body can be reliably measured using the MyotonPRO. The device could be used as a reference standard in the development of a digital palpation scale that would facilitate accurate diagnosis of muscle tone.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".