Motor unit number and transmission stability in octogenarian world class athletes: Can age-related deficits be outrun?
Bibliographic record
Abstract
Our group has shown a greater number of functioning motor units (MU) in a cohort of highly active older (∼65 yr) masters runners relative to age-matched controls. Because of the precipitous loss in the number of functioning MUs in the eighth and ninth decades of life it is unknown whether older world class octogenarian masters athletes (MA) would also have greater numbers of functioning MUs compared with age-matched controls. We measured MU numbers and neuromuscular transmission stability in the tibialis anterior of world champion MAs (∼80 yr) and compared the values with healthy age-matched controls (∼80 yr). Decomposition-enhanced spike-triggered averaging was used to collect surface and intramuscular electromyography signals during dorsiflexion at ∼25% of maximum voluntary isometric contraction. Near fiber (NF) MU potential analysis was used to assess neuromuscular transmission stability. For the MAs compared with age-matched controls, the amount of excitable muscle mass (compound muscle action potential) was 14% greater (P < 0.05), there was a trend (P = 0.07) toward a 27% smaller surface-detected MU potential representative of less collateral reinnervation, and 28% more functioning MUs (P < 0.05). Additionally, the MAs had greater MU neuromuscular stability than the controls, as indicated by lower NF jitter and jiggle values (P < 0.05). These results demonstrate that high-performing octogenarians better maintain neuromuscular stability of the MU and mitigate the loss of MUs associated with aging well into the later decades of life during which time the loss of muscle mass and strength becomes functionally relevant. Future studies may identify the concomitant roles genetics and exercise play in neuroprotection.
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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 | 0.001 | 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) | 0.001 | 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".