Evidence of nonlocal muscle fatigue in male youth
Bibliographic record
Abstract
Evidence for nonlocal muscle fatigue (NLMF) has been inconsistent in adults, with no studies investigating youth. The objective was to examine NLMF in youth. Forty-two young males (age, 10–13 years) were tested for maximal voluntary isometric contraction (MVIC) force of the ipsilateral and contralateral knee extensors at 90° and 120° knee flexion, elbow flexors at 90°, handgrip, knee extensor isokinetic torque (300°·s−1 analyzed at 90° and 120° knee flexion), as well as a unilateral countermovement jump (CMJ) and Y Balance test (YBT). Isokinetic fatigue group (n = 15) had unilateral fatigue induced with 10 sets of 20 repetitions of maximal isokinetic knee extensor contractions at 300°·s−1. Isometric fatigue group (n = 15) used 10 repetitions of 6-s knee extensor MVIC whereas the control group (n = 12) were not fatigued. There was no significant difference in the response to the isometric- or isokinetic-fatigue intervention protocols. Main time effects indicated that NLMF was evident with the contralateral knee extensor MVIC at 90° (p = 0.008; 8.9%), knee extensor isokinetic torque at 90° (p < 0.001; 11.4%), and 120° (p = 0.05; 5.4%), CMJ (p = 0.02; 11.5%), handgrip (p = 0.06; 4.5%), elbow flexors (p < 0.001; 7.7%), and YBT (p = 0.001; 5.6%). Ipsilateral NLMF deficits occurred with handgrip (p < 0.001; 7.3%), elbow flexors MVICs (p < 0.001; 10.7%), CMJ (p = 0.02; 12.2%), and YBT (p = 0.002; 3.8%). NLMF with similar relative fatigue-induced deficits of fatigued and nonfatigued limbs suggest that youth fatigue is highly dependent upon the extent of activation or inhibition of the nervous system. Coaches of young athletes might consider developing technical motor skills before fatiguing exercise components, which might hinder the proficiency of their performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".