CONSIDERATIONS OF MUSCLE QUALITY AND ARCHITECTURE IN RELATION TO MUSCLE WEAKNESS IN HYPERMOBILITY-RELATED DISORDERS
Bibliographic record
Abstract
Hypermobility related conditions (HM), such as hypermobile Ehlers-Danlos Syndrome, often report pain and fatigue during exercise. In a recent study, we assessed the active and passive force-length relations of the ankle plantarflexors and found that HM are significantly weaker compared to age- and sex-matched controls (CON) across all ankle angles. Echogenicity is a potential surrogate measure for muscle quality using ultrasonography. It provides a measure of the amount of intramuscular fat deposited in muscle tissue by measuring the echo intensity (EI), which is the brightness of the reflected ultrasound pixels. It is debated whether EI is related to muscle strength across different clinical populations, but this has not yet been determined in HM. Similarly, muscle thickness (MT) may offer a convenient estimate of muscle volume and therefore strength. Pennation angle (PA) may also influence strength, since for a given muscle force. a smaller component of that muscle force acts along the muscle-tendon line of action. PURPOSE: To assess differences in EI, MT, and PA between HM and CON, as a potential mechanism for muscle weakness in HM. METHODS: EI was determined in 9 HM (32 ± 13 years, 169.8 ± 6.8 cm, 70.0 ± 11.9 kg) and 9 CON participants (34 ± 15 years, 164.4 ± 10.3 cm, 65.9 ± 15.9 kg) using grayscale histogram analysis within ImageJ from resting ultrasound images of each participant’s medial gastrocnemius muscle with the leg fully extended and the ankle at 90⸰. Mean EI was calculated as a value between 0 (black) and 255 (white). MT of the medial gastrocnemius was measured as the largest distance between the superficial and deep aponeurosis and PA was measured as the angle between a muscle fascicle and the deep aponeurosis. Group comparisons were made using an independent samples t-test. RESULTS: EI was not significantly different between HM (63.4 ± 26.3) and CON (59.8 ± 16.4, p = 0.723). We found no significant differences between HM (18.3 ± 3.3 mm) and CON (16.9 ± 2.4 mm) in muscle thickness (p = 0.303), or pennation angle (21.9 ± 5.7°, 20.7 ± 2.7⸰, p = 0.518), respectively. CONCLUSION: These findings refute our hypothesis that weakness in HM may be due to muscle atrophy and/or reduced muscle quality. Future studies will examine the extent of voluntary muscle activation as a potential explanation for reduced muscle strength in HM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".