Effects of resistance training at different intensities of load on cross-education of muscle strength
Bibliographic record
Abstract
The objectives of this study were to 1) compare the extent of cross-transfer of muscle strength of high- versus low-load unilateral resistance training performed with external pacing of the movement (URTEP) and 2) compare the time course of the 2 approaches. Fifty subjects were randomized to 1 of the following 3 groups: G80 [2 sets at 80% and 2 sets at 40% of 1 repetition maximum (1RM), 1 concentric second and 3 eccentric seconds controlled by a metronome]; G40 (4 sets at 40% of 1RM, 1 s and 3 s controlled by a metronome); or control group. At week 1, the G80 increased the elbow flexion 1RM (P < 0.05) in contralateral arm. At week 4, both G80 and G40 increased the elbow flexion 1RM (P < 0.05) in contralateral arm. However, a greater 1RM gain was observed in the G80 than in the G40 (P < 0.05). Thus, although higher-load URTEP seems to enhance the cross-education effect when compared with lower-load URTEP, the cross-education of dynamic strength can be achieved in the 2 approaches after 4 weeks. Many patients would benefit from cross-education of muscle strength through URPEP, even those who are unable to exercise with high loads and in short periods of immobilization. Novelty: Unilateral resistance training promotes cross-education of dynamic muscle strength. However, higher-load resistance training enhances the effects of cross-education of muscle strength.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".