Grip Force Modulation on Median Nerve Morphology Changes
Bibliographic record
Abstract
Compression on the median nerve can lead to carpal tunnel syndrome (CTS), and median nerve indicators measured from ultrasound images can be used for CTS diagnosis. The aim of this study was to investigate the relationship between grip force modulation and dynamic morphological changes of the median nerve. We used a digital grip dynamometer to measure grip force while simultaneously conducting ultrasound examinations. Ultrasound images were sampled for both the dominant and nondominant hands of all participants (n = 20) during a baseline condition at approximately 0% maximum voluntary force (MVF), during sustained grip force conditions at 25%, 50%, 75%, and 100% MVF, and during the return to a relaxed state (≈0% MVF) directly following each grip force condition. Regardless of hand dominance, grip force level, and grip force modulation, median nerve cross-sectional area (MNCSA) during the grip tasks was smaller relative to the initial baseline condition without grip force. With respect to shape change, the median nerve became more flattened, including increased longitudinal diameter (D1) and decreased vertical diameter (D2), when grip force was relaxed compared to the preceding sustained grip force condition for the dominant hand; however, there were no significant shape changes for the nondominant hand. As morphological changes to tissue result in strain, our results indicate that median nerve injury development may be associated with more hand usage (dominant hand, grip exertion, and grip force modulation), and further suggests the evaluative potential for median nerve dynamics within the carpal tunnel.
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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) | 0.000 | 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 | 0.000 |
| 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".