A comprehensive scoping review and meta-analysis of upper limb strength asymmetry
Bibliographic record
Abstract
The "10% rule" of handedness asserts the dominant hand is 10% stronger than the non-dominant hand. Primarily derived from handgrip data, it is unclear if a generalized asymmetry exists across the upper limb. Understanding how strength asymmetry may be affected by handedness, sex, and exertion type has important implications for ergonomics design, sports performance, and clinical rehabilitation. The purpose of this study was to systematically synthesize currently available evidence examining upper limb strength asymmetry. 10,061 results were retrieved, and 174 studies remained after title/abstract screening. 87 studies were synthesized. Results are compiled by exertion type and manner of asymmetry comparison (i.e. right/left, dominant/non-dominant). Asymmetry ratios were calculated to examine the effects of handedness, exertion side, arm region, and sex. Strength differences were most frequently reported for grip exertions (n = 49). 25 studies reported other joint strength asymmetries. Overall, the right limb was 6.7% stronger than the left limb (n = 9342) and the dominant limb was 11.6% stronger than the non-dominant limb (n = 9327), though strength asymmetry varied across joints and movements (2.1% to 19.5%). This research demonstrates that the 10% rule is a good approximation for upper limb strength asymmetry. However, several factors, including joint, movement type, and sex, can affect this relationship.
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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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".