Changes in motor unit discharge rate are not associated with the amount of twitch potentiation in old men
Bibliographic record
Abstract
This study examined, in nine old men (82 +/- 4 yr), whether there is an association between the magnitude of change in motor unit discharge rate and the amount of twitch potentiation after a conditioning contraction (CC). The evoked twitch force and motor unit discharge rate during isometric ramp-and-hold contractions (10-18 s) of the triceps brachii muscle at 10, 20, and 30% of the maximal voluntary contraction were determined before and 10 s, 2 min, 6 min, and 11 min after a 5-s CC at 75% maximal voluntary contraction. After the CC, there was a potentiation of twitch force (approximately twofold), and the discharge rate of the 47 sampled motor units declined (P < 0.05) an average of 1 Hz 10 s after the CC, compared with the control condition. The CC had no effect on the variability (coefficient of variation) of both force and discharge rate, as well as the electromyographic activity recorded over the triceps brachii and biceps brachii muscles. In contrast to our earlier study of young men (Klein CS, Ivanova TD, Rice CL, and Garland SJ, Neurosci Lett 316: 153-156, 2001), the magnitude of the reduction in discharge rate after the CC was not associated (r = 0.06) with the amount of twitch potentiation. These findings suggest an age-related alteration in the neural strategy for adjusting motor output to a muscle after a CC.
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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) | 0.001 | 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".