Unilateral elbow flexion fatigue modulates corticospinal responsiveness in non‐fatigued contralateral biceps brachii
Bibliographic record
Abstract
Exercise-induced fatigue can change motor performance in non-exercised muscles. The objective was to investigate unilateral elbow flexion (EF) fatigue effects on the maximal voluntary force (MVC) and corticospinal excitability of contralateral non-exercised biceps brachii (BB). Transcranial magnetic, transmastoid electrical, and brachial plexus electrical stimulation were used to elicit motor evoked potentials (MEP), cervicomedullary motor evoked potentials (CMEP), and compound muscle action potentials in the contralateral non-exercised BB of 12 participants before and after (i) two bouts of 100-s unilateral EF (fatigue) or (ii) control. Three stimuli were evoked every 1.5 s during a series of 6-s isometric EF at 100%, 50%, and 5% of MVC. The non-exercised EF MVC force, electromyographic activity, and voluntary activation were not significantly different between fatigue and control. Non-exercised BB MEP and CMEP amplitudes during 100% MVCs demonstrated significantly higher (P = 0.03) and lower values (P = 0.01), respectively, after fatigue compared with control. There was no difference between the two conditions for MEP and CMEP amplitudes during 50% and 5% MVCs. Unilateral exercise-induced EF fatigue did not lead to cross-over central fatigue to the contralateral homologous muscle but enhanced the supraspinal responsiveness (MEP/CMEP) of the neural circuitries supplying central commands to non-exercised muscles at higher contraction intensity.
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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".