Mechanomyographic and electromyographic responses to stimulated and voluntary contractions in the dorsiflexors of young and old men
Bibliographic record
Abstract
The effect of age on mechanomyography (MMG) has not been examined for electrically evoked contractions. Similar to torque, we expected that postactivation potentiation of the MMG would differ between young and old subjects. Additionally, under voluntary conditions, we compared normalized MMG and electromyographic (EMG) signals in relation to torque, and expected that MMG, unlike EMG, would be affected by age. In 10 young and 10 old men, electrical stimulation was delivered before and after a 10-s maximal voluntary contraction (MVC) to assess potentiation of contractile (twitch torque; Pt), electrical (M-wave amplitude), and mechanical (MMG amplitude) properties of the dorsiflexors. Subsequently, subjects performed voluntary contractions at 20%, 40%, 60%, 80%, and 100% MVC for calculation of normalized MMG-torque and EMG-torque relationships. Following the MVC, Pt and evoked MMG were larger than at rest in both groups, but M-wave amplitude was unchanged. Twitch potentiation was greater in young than old, whereas evoked MMG was unaffected by age. Under voluntary conditions, values for MMG and EMG were similar between groups, except for greater MMG at MVC in young men. The shape of MMG and EMG relationships to torque was similar only in young men. Using the aging model, our results indicate that potentiation of the mechanical components (MMG) differ from those of twitch torque. Furthermore, the comparison of normalized voluntary MMG with age provides additional support for the concept of age-related motor unit remodeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".