Bibliographic record
Abstract
Loss of cells from the motor system occurs during the normal aging process, leading to reduction in the complement of motor neurons and muscle fibers. The latter age-related decrease in muscle mass has been termed "sarcopenia" and is often combined with the detrimental effects of a sedentary lifestyle in older adults, leading to a significant reduction in reserve capacity of the neuromuscular system, which is the primary subject of this review. Clear evidence of this aging effect is seen when voluntary or stimulated muscle strength is compared across the adult lifespan, with a steady decline of approximately 1-2% per year occurring after the sixth decade. Interestingly, when compared with isometric contractions, the effect of aging is more pronounced for concentric movements and less for eccentric movements (i.e., muscle shortening versus lengthening). This phenomenon appears to be linked to the stiffer muscle structures and prolonged myosin crossbridge cycles of aged muscles. It is encouraging that the capability of physiological adaptations in the motor pathways remains into very old age--when an appropriate exercise stimulus is given--and long-term prevention strategies are advocated to avoid excessive physical impairments and activity restrictions in this age group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".