Elbow flexor fatigue modulates central excitability of the knee extensors
Bibliographic record
Abstract
The present study investigated the effects of exercise-induced elbow flexor fatigue on voluntary force output, electromyographic (EMG) activity and motoneurone excitability of the nonexercised knee extensor muscles. Eleven participants attended 3 testing sessions: (i) control, (ii) unilateral fatiguing elbow flexion and (iii) bilateral fatiguing elbow flexion (BiFlex). The nonfatigued knee extensor muscles were assessed with thoracic motor evoked potentials (TMEPs), maximal compound muscle action potential (Mmax), knee extensor maximal voluntary contractions (MVCs), and normalized EMG activity before and at 30 s, 3 min, and 5 min postexercise. BiFlex showed significantly lower (Δ = -18%, p = 0.03) vastus lateralis (VL) normalized EMG activity compared with the control session whereas knee extension MVC force did not show any statistical difference between the 3 conditions (p = 0.12). The TMEP·Mmax(-1) ratio measured at the VL showed a significantly higher value (Δ = +46%, p = 0.003) following BiFlex compared with the control condition at 30 s postexercise. The results suggest that the lower VL normalized EMG following BiFlex might have been due to a reduction in supraspinal motor output because spinal motoneuronal responses demonstrated substantially higher value (30 s postexercise) and peripheral excitability (compound muscle action potential) showed no change following BiFelex than control condition.
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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 | 0.000 |
| 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".