Modeling age-related neuromuscular changes in humans
Bibliographic record
Abstract
With aging, motoneurons and muscle tissue undergo significant changes, which influence function in terms of strength, mobility, and overall independence. Mathematical modeling provides a practical method of studying the relationships among recruitment, rate-coding, and force output in motor units, and may be used to predict functional neuromuscular changes related to aging. For this study, the Heckman-Binder model was used to examine changes in human quadriceps motor units. Relationships among current input, firing frequency, and force output were defined for both a younger and an older individual. Included in the model were age-related effects associated with reduced muscle contractile speed; reduced muscle-fibre number, size, and specific tension; reduced gain of the frequency-current relationship; decreased size of motoneurons; and altered motor unit remodeling. Adjustment of this model to reflect age-related changes resulted in a leftward shift of the force-frequency function, lower firing frequency for any given current injected into the motoneuron, and a reduction in maximal force output. The model suggests that older individuals are capable of reaching force levels up to approximately 50% of those attained by younger individuals, with relatively similar or even slightly lower levels of current input. This could mean that the sense of effort and the contribution of factors other than degree of effort from afferent inputs to the pool, including conscious supraspinal centres, might be different in the older adult.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".