The effect of dominant first dorsal interosseous fatigue on the force production of a contralateral homologous and heterologous muscle
Bibliographic record
Abstract
Crossover and nonlocal muscle fatigue (NLMF) has generally focused on large muscle groups. It is unclear if fatigue of a small muscle can result in NLMF of a larger muscle. The purpose of the present study was to examine the effect of small muscle (first dorsal interosseous; FDI) fatigue on the force and activation of contralateral homologous and larger heterologous muscles (biceps brachii; BB). Fifteen right-handed male subjects performed 3 pre-test index finger abduction or elbow flexion maximum voluntary isometric contractions (MVICs) on the nondominant side. Subsequently, they performed two 100-s index finger abduction MVICs on the dominant side (experimental (fatigue) group) or rested for 5 min (control group). Afterwards, a single MVIC and a 12-repetition MVIC fatiguing protocol were completed with index finger abduction or elbow flexion on the nondominant side. Force and electromyography (EMG) were measured from both sides. The force and EMG (median frequency; MDF) of nonexercised index finger abductors (IFA)/FDI and elbow flexors (EF)/BB significantly decreased after the fatiguing protocol. Compared with the control condition, the nonexercised IFA (12.5% and 5.7%) had significantly greater force and MDF fatigue indexes than the EF (5.2% and 1.7%). There were no significant force differences with the single MVIC test between conditions. The small muscle fatiguing protocol produced NLMF effects on both contralateral homologous and larger heterologous muscles, with the force decrements greater with the homologous muscle.
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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 | 0.001 |
| 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".