Covert age-related differences in agility are related to both muscle strength and integrity of the corticospinal tract
Bibliographic record
Abstract
Background: Agility involves moving efficiently without losing balance, requiring muscular strength and neuromuscular capacity. Maintaining agility promotes aging with vitality, living without frailty, and reduced fear of falling. Factors that influence age-related differences in agility are unknown. Methods: Participants were recruited to determine whether quadriceps strength or integrity of the corticospinal tract (CST) influenced age-related differences in agility. Participants underwent Transcranial Magnetic Stimulation to measure CST integrity and completed a lower limb agility hopping task. CST excitability was calculated as active motor threshold intensity, the lowest stimulator output that produced a motor evoked potential. We used regression modelling to predict the contribution of quadriceps strength and CST integrity to lower limb agility, when controlling for sex. Results: Greater quadriceps strength correlated with longer hop length (r = .581,p <.001) and reduced hop length variability (r=-.384,p=.039). Lower active motor threshold correlated with longer hop length (r=-.364,p=.048) and reduced hop length variability (r=.478,p=.007). Decreased quadriceps strength significantly predicted shorter hop length (R²=.393,p=.002) while higher active motor threshold predicted greater hop variability (R²=.182,p=.036). Conclusions: Agility involves a combination of muscle power and coordination, which can be tested with a hopping agility task. CST integrity predicted coordination on the task, but not strength, even when controlling for sex.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".