Neuromuscular Transmission Stability In Very Old World-class Masters Athletes
Bibliographic record
Abstract
Natural adult aging is associated with the gradual loss of functioning motor units (MUs), a process that accelerates in the 8th decade of life. It has been shown in only a few studies that endurance trained masters athletes (MA) between 60 and 95 years of age had more MUs than age-matched controls, but both groups had lower numbers than young adults. However, it is not known how physical activity may affect the fidelity of neuromuscular transmission in old age. PURPOSE: To explore electrophysiologic factors that reflect MU stability in world-class masters athletes, 75 years of age and older, in comparison with age-matched controls. METHODS: A maximal compound muscle action potential (CMAP) was recorded from the tibialis anterior muscle (TA), in 5 healthy controls (aged 75 to 95 years) with a comparable group of 6 MA of the same age range. Decomposition-enhanced spike-triggered averaging was used to analyze surface and intramuscular EMG from the TA during a series of submaximal (20% MVC) voluntary dorsiflexion contractions in order to derive a motor unit number estimation (MUNE). Near fibre (NF) motor unit potential (MUP) analysis was performed to provide a detailed assessment of neuromuscular status. Near fibre jiggle is a parameter that measures the variability of consecutive isolated MUs. RESULTS: Compared with controls, the MA were 39% stronger and had greater MUNEs (51 vs. 73, respectively). Furthermore, preliminary results indicate that controls had a significantly higher jiggle value than the MA (66.9% vs. 46.3%, respectively). CONCLUSIONS: Greater MU stability in MA may be due to the high-intensity or volume of training and indicative of more stable MUs (lower degree of MU remodeling), which could reflect the physical activity-associated relative preservation of MUs compared with controls. Supported by NSERC.
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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 | 0.002 | 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".