Stress and stiffness as predictors of shear wave velocity in peripheral nerve
Bibliographic record
Abstract
Shear wave elastography (SWE) is a promising non-invasive indicator for diagnosing peripheral neuropathy. Emerging validation studies using ultrasound-based measures of shear wave velocity (SWV) in other biological tissues, such as muscle, demonstrate there is a concern of whether SWE is an accurate measure of tensile stress or stiffness. Distinguishing between these two parameters and their relationship with SWV is crucial if SWE is to be used as a biomarker for peripheral neuropathies, where changes in mechanical properties are known to occur. In this study, we use cat sciatic nerves to first evaluate SWV in situ at knee positions known to reduce (90° flexion) or increase (180° extension) stress, and then excise nerves to directly quantify the relationships between SWV, stress, and stiffness with ex vivo tensile testing. Our ex vivo findings show that although SWV can be predicted using either stress or stiffness, stress explains more variability in sciatic nerve SWV. However, while stress remains the better predictor of SWV ex vivo, within the SWV range established in situ, stiffness improves its accuracy at estimating SWV, especially when also accounting for factors related to nerve viscoelasticity. Therefore, if SWE is to be used in clinical settings as an indicator of nerve stiffness in peripheral neuropathy, it is essential to standardize parameters such as limb positioning and nerve preloading, which could potentially mask pathological changes in nerve stiffness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".