Sex differences in wrist strength: a systematic review
Bibliographic record
Abstract
Sex differences in strength have been attributed to differences in body anthropometrics and composition; these factors are often ignored when generating workplace guidelines. These differences directly impact the upper extremity, leaving female workers exposed to injury risk. The wide range of tools and techniques for measuring upper extremity strength presents a challenge to ergonomists and work task designers; collating outcomes to provide a clear outlook of differences between males and females is essential and the purpose of this work. Four online databases were searched (PROSPERO ID: CRD42022339023) with a focus on articles assessing sex differences in wrist strength. A total of 2,378 articles were screened for relevancy; 25 full-text articles were included in this systematic review. Articles examined movement pairs (ulnar/radial deviation, pronation/supination, and flexion/extension), as well as contraction types (isometric and isokinetic) to observe sex differences in wrist strength. Across all articles, females produced ∼60-65% of male flexion/extension strength, ∼55-60% pronation/supination strength, and ∼60-70% ulnar/radial deviation strength. Overall, females presented lower strength-producing abilities than males, but when considering strength relative to body mass, male-female differences were less pronounced and occasionally females surpassed male strength metrics; typically, this occurred during flexion/extension, particularly in isokinetic contractions. This review has identified a scarcity of articles examining ulnar/radial deviation, pronation/supination, as well as isokinetic contractions; these are needed to supplement workplace exposure guidelines.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".