External Compression and Partial Ischemia Decrease Human Finger Flexor Tendon and Subsynovial Connective Tissue Relative Motion
Bibliographic record
Abstract
Cumulative shear strain of the subsynovial connective tissue (SSCT) surrounding finger flexor tendons plays a significant role in the development and progression of carpal tunnel syndrome. Biomechanical risk factors can alter tendon-SSCT shear strain but the effects of external mechanical compression and localized ischemia have yet to be investigated. In a laboratory study with 19 healthy participants, color Doppler ultrasound imaging was used to quantify relative motion between the flexor digitorum superficialis tendon and SSCT during repetitive finger flexion-extension under various conditions of external mechanical compression (palmar and forearm compression), ischemia and different movement speeds (0.75 and 1.25 Hz). Forearm compression reduced tendon displacement (baseline = 28.5 ± 4.1 mm vs. forearm = 27.0 ± 4.6 mm; p = 0.043) and showed a trend for reduced SSCT displacement, while palmar compression had no significant effects on tendon-SSCT motion. Compared with baseline, partial ischemia decreased SSCT displacement (baseline = 22.9 ± 3.3 mm vs. ischemia = 22.0 ± 3.3 mm; p = 0.015), while tendon displacement remained unchanged. In all experimental conditions, faster movements elicited greater tendon-SSCT relative motion. Our findings suggest that palmar compression may not negatively impact tendon-SSCT relative motion, but forearm compression may require further investigation. Localized ischemia in the forearm may alter the gliding conditions within the carpal tunnel and affect tendon-SSCT relative motion, which bridges an important gap between blood flow in the carpal tunnel and shear injury risk. These findings contribute to the growing body of literature, supporting the role that cumulative tendon-SSCT shear injury may have on the pathomechanics of carpal tunnel syndrome. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:1038-1044, 2020.
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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) | 0.003 | 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".