Development of a kinematic model to predict finger flexor tendon and subsynovial connective tissue displacement in the carpal tunnel
Bibliographic record
Abstract
Finger flexor tendinopathies and carpal tunnel syndrome are histologically characterised by non-inflammatory fibrosis of the subsynovial connective tissue (SSCT) in the carpal tunnel, which is indicative of excessive and repetitive shear forces between the finger flexor tendons and SSCT. We assessed flexor digitorum superficialis (FDS) tendon and adjacent SSCT displacements with colour Doppler ultrasound as 16 healthy participants completed long finger flexion/extension movements captured by a motion capture system. FDS tendon displacements fit a second-order regression model based on metacarpophalangeal and proximal interphalangeal joint flexion angles (R(2) = 0.92 ± 0.01). SSCT displacements were 33.6 ± 1.7% smaller than FDS tendon displacements and also fit a second-order regression model (R(2) = 0.89 ± 0.01). FDS tendon and SSCT displacement both correlated with finger joint thickness, enabling participant-specific anthropometric scaling. We propose the current regression models as an ergonomic method to determine relative displacements between the finger flexor tendons and SSCT. PRACTITIONER SUMMARY: Relative displacements between the finger flexor tendons and SSCT provide insight into gliding and friction in the carpal tunnel. Our regression models represent a move towards mechanistic-based ergonomic risk assessment of the wrist/hand. This is a natural evolution of ergonomic methods based on tendon-joint interaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".