Median nerve travel and deformation in the transverse carpal tunnel increases with chuck grip force and deviated wrist position
Bibliographic record
Abstract
BACKGROUND: We assessed median nerve travel and deformation concurrently to better understand the influence of occupational risk factors on carpal tunnel dynamics, including forceful chuck gripping and deviated wrist positions. METHODS: Fourteen healthy right-hand dominant participants performed a chuck grip in 6 experimental conditions: two relative force levels (10% and 40% of maximum voluntary effort); three wrist positions (15° radial deviation, 0° neutral, 30° ulnar deviation). Chuck grip forces were measured with a load cell while the transverse cross-section of the carpal tunnel was imaged via ultrasound at the distal wrist crease. Images of the median nerve were analyzed in ImageJ to assess cross-sectional area, circularity, width, and height as well as travel in the anterior-posterior and medial-lateral axes. RESULTS: We found a main effect of deviated wrist position on both anterior-posterior and medial-lateral travel, with the greatest nerve travel occurring in 30° ulnar deviation. There was also a significant interaction between chuck grip force and deviated wrist position on cross-sectional area. Specifically, the area decreased with 40% vs. 10% chuck grip force when the wrist was in 30° ulnar deviation; however, there were no changes in 0° neutral and 15° radial deviation. DISCUSSION: Overall, we demonstrated that forceful chuck gripping in deviated wrist positions influenced carpal tunnel dynamics, resulting in both migratory and morphological changes to the median nerve. These changes may, in turn, increase local strain and stress with adjacent structures in the carpal tunnel. Future studies mapping contact stress between structures may further elucidate injury development of work-related carpal tunnel syndrome.
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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) | 0.000 | 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 | 0.001 |
| 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".