Influence of biceps brachii tendon mechanical properties on elbow flexor force steadiness in young and old males
Bibliographic record
Abstract
Elbow flexor force steadiness (FS) depends on strength and decreases with age. Achilles tendon mechanics effect standing balance and isometric plantarflexion FS. This study investigated the influence of distal biceps brachii (BB) tendon mechanics and elbow flexor strength on age‐related decline in FS. Nine young (23 ± 2 years) and nine old (77 ± 5 years) males performed submaximal isometric elbow flexion tasks at low (2.5%, 5%, 10% maximal voluntary contraction (MVC)) and high (20%, 40%, 60%, 80%MVC) forces in a neutral forearm position. Distal BB tendon elongation and cross‐sectional area (CSA) were recorded on ultrasound to calculate mechanics of strain, stress, and stiffness. Coefficient of variation (CV) of force was used to assess relationship of FS to tendon mechanics and strength. Young were 22% stronger and 41% steadier than old (P < .05). Tendon stiffness (170.1 ± 132.9 N/mm; 113.0 ± 55.1 N/mm) did not differ with age (P > .05). Young had 40% less strain compared to old at 5% MVC, but 42% greater strain at 60% and 80% MVC (P ≤ .05). Stress was ~18% greater in young at 10%, 20%, and 80% MVC (P ≤ .05). At low forces, CV of force was predicted by stress (r2 = 0.56) in young, and stress and MVC (r2 = 0.641) in old. At high forces for both age groups, CV of force was predicted by MVC and stress (r2 = 0.39‐0.43). Stress and strain is greater in young compared with old males. Because strength influences tendon mechanics and is also associated with FS, absolute strength is a large and modifiable contributor to age‐related decline in FS.
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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) | 0.001 | 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".