Do aerobic characteristics explain isometric exercise-induced neuromuscular fatigue and recovery in upper and lower limbs?
Bibliographic record
Abstract
This study investigated the relationships between aerobic characteristics and (i) neuromuscular fatigue induced by 2-min sustained isometric maximal voluntary contractions (MVC) and (ii) subsequent recovery, in the upper and lower limbs. In a pseudo-randomized order, eleven healthy males completed four sessions on different days: maximal incremental cycling test (100 W + 40 W every 2 min); maximal arm-cranking test (50 W + 20 W every 2 min); and 2-min sustained isometric MVCs of the knee extensors (KE) and elbow flexors (EF). Neuromuscular assessment was performed with transcranial magnetic and peripheral nerve stimulation to evaluate central and peripheral neuromuscular factors of fatigue and the subsequent recovery. Peak oxygen uptake, gas exchange threshold and the corresponding power outputs were correlated with recovery of voluntary force after the 2-min KE MVC. Regression analysis showed that power output at the gas exchange threshold alone explained 72% of the variability in ∆recovery of KE voluntary force. No relationships with fatigue or recovery in EF were observed. These results suggest that participants with greater aerobic capacities experience the same amount of fatigue and faster recovery of voluntary force in KE but not EF. The potential reasons behind the relationship in KE but not EF are discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".