Age‐related reductions in the estimated numbers of motor units are minimal in the human soleus
Bibliographic record
Abstract
The documented impact of contractile level on decomposition-enhanced spike-triggered averaging motor unit number estimates (MUNEs) in young adults demonstrates the importance of selecting an objective contraction intensity that yields the most representative MUNE for a given muscle. Whether the same contraction intensity would be ideal in an altered system (e.g., by aging or disease) has yet to be examined. Thus, the main purpose of this study was to compare the effects of contraction intensity on MUNEs from the soleus muscle in young ( approximately 27 years) and old ( approximately 75 years) men. Using decomposition-enhanced spike-triggered averaging, surface and intramuscular electromyographic signals were collected from the soleus during a range of submaximal isometric plantar-flexion contractions (threshold, 10%, 20%, and 30% of maximum voluntary contraction; MVC). Five MUNEs were calculated, one for each of the four contraction intensities and an ensemble MUNE was derived from all MUs collected. Although MUNE decreased similarly with increased effort in both groups, MUNEs were not significantly reduced in the old men compared to the young men. Consequently, the ensemble MUNE was extrapolated to an intensity of approximately 15% MVC in both young and old. The results suggest that, in the soleus, the use of the same contraction intensity across age groups is a valid comparison.
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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 | 0.000 | 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".