Greater Short-Time Recovery of Peripheral Fatigue After Short- Compared With Long-Duration Time Trial
Bibliographic record
Abstract
The kinetics of recovery from neuromuscular fatigue resulting from exercise time-trials (TT) of different durations are not well known. The aim of this study was to determine if TTs of three different duration would result in different short-term recovery in MVC and evoked peak forces. Twelve trained subjects performed repetitive concentric right knee extensions on an isokinetic dynamometer self-paced to last 3, 10, and 40 min (TTs). Neuromuscular function was assessed immediately (< 2 s) and 1, 2, 4 and 8 min after completion of each TT using maximal voluntary contractions (MVC) and electrical stimulation. Electrical stimulations consisted of single stimulus (SS), paired stimuli at 10 Hz (PS10), and paired stimuli at 100 Hz (PS100). Electrically evoked forces including the ratio of low to high frequency doublets were similar between trials at exercise cessation, but subsequently increased more (P < 0.05) after the 3-min TT compared with either the 10-min or 40-min TT when measured at one or two minutes of recovery. MVC force was not different between trials. The results demonstrate that recovery of peripheral fatigue including low-frequency fatigue depends on the duration and intensity of the preceding self-paced exercise. These differences in recovery probably indicate differences in the mechanisms of fatigue for these different time-trials. Since recovery is faster after a 3-min TT than a 40-min TT, delayed assessment of fatigue will detect a difference in peripheral fatigue between trials that was not present at exercise cessation.
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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 | 0.002 |
| 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".