Motor Unit Number Estimates in Masters Runners
Bibliographic record
Abstract
INTRODUCTION: A contributing factor to the loss of muscle mass and strength during aging is the reduction in the number of functioning motor units (MU). It has been shown that lifelong physically active older rats have greater numbers of MU compared with age-matched sedentary controls, suggesting that chronic exercise may preserve MU function with advancing age. This has not previously been examined in humans. PURPOSE: Thus, the purpose of this study was to estimate the number of functioning MU in the tibialis anterior of masters runners (approximately 65 yr) and to compare the values with recreationally active young (approximately 25 yr) and healthy age-matched controls (approximately 65 yr). METHODS: Decomposition-enhanced spike-triggered averaging was used to collect surface and intramuscular EMG signals during dorsiflexion at 25% of maximum voluntary isometric contraction. RESULTS: The estimated number of MU did not differ between masters runners and young, but MU number estimates were lower in the old (91 +/- 22 MU) compared with the masters runners (140 +/- 53 MU) and young (150 +/- 43 MU). CONCLUSION: These results demonstrate that lifelong high-intensity physical activity could potentially mitigate the loss of MU associated with aging well into the seventh decade of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".