The effects of posture on forearm muscle loading during gripping
Bibliographic record
Abstract
The purpose of this study was to quantify the response of the forearm musculature to combinations of wrist and forearm posture and grip force. Ten healthy individuals performed five relative handgrip efforts (5%, 50%, 70% and 100% of maximum, and 50 N) for combinations of three wrist postures (flexed, neutral and extended) and three forearm postures (pronated, neutral and supinated). 'Baseline' extensor muscle activity (associated with holding the dynamometer without exerting grip force) was greatest with the forearm pronated and the wrist extended, while flexor activity was largest in supination when the wrist was flexed. Extensor activity was generally larger than that of flexors during low to mid-range target force levels, and was always greater when the forearm was pronated. Flexor activation only exceeded the extensor activation at the 70% and 100% target force levels in some postures. A flexed wrist reduced maximum grip force by 40-50%, but EMG amplitude remained elevated. Women produced 60-65% of the grip strength of men, and required 5-10% more of both relative force and extensor activation to produce a 50 N grip. However, this appeared to be due to strength rather than gender. Forearm rotation affected grip force generation only when the wrist was flexed, with force decreasing from supination to pronation (p < 0.005). The levels of extensor activation observed, especially during baseline and low level grip exertions, suggest a possible contributing mechanism to the development of lateral forearm muscle pain in the workplace.
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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) | 0.001 | 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) | 0.002 | 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".