Cross-Education of Arm Muscular Strength Is Unidirectional in Right-Handed Individuals
Bibliographic record
Abstract
PURPOSE: Cross-education of strength is a neural adaptation defined as the increase in strength of the untrained contralateral limb after unilateral training. The purpose was to determine the effect of the direction of transfer on cross-education in right-handed individuals. METHODS: Thirty-nine strongly right-handed females were randomized into a left-hand training (LEFT), right-hand training (RIGHT), or nontraining control (CON) group. Strength training was 6 wk of maximal isometric ulnar deviation, 4x wk(-1). Peak torque, muscle thickness (ultrasound), and EMG activity were assessed before and after training in both limbs. RESULTS: The change in strength in the untrained limb was greatest in the RIGHT group (39.2%; P < 0.01), whereas no significant changes in strength were observed for the untrained limb of the LEFT group (9.3%) or for either of the CON group limbs (10.4 and 12.2%). Strength training also increased trained limb strength in the LEFT (41.9%, P < 0.01) and the RIGHT (25.9%; P < 0.01) groups. Training groups increased trained limb muscle thickness (RIGHT and LEFT combined: 4.1%) compared to CON (-4.0%) (P < 0.01). There were no changes in muscle thickness of untrained limbs compared to CON. Trained limb agonist EMG activation increased with training (P < 0.05) with no change for the antagonist. Changes in untrained limb EMG were not different compared to CON. CONCLUSIONS: Cross-education with hand strength training occurs only in the right-to-left direction of transfer in right-handed individuals. We conclude that cross-education of arm muscular strength is most pronounced to the nondominant arm.
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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) | 0.003 | 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".