Multi‐directional <i>in vivo</i> tensile skin stiffness measurement for the design of a reproducible tensile strain elastography protocol
Bibliographic record
Abstract
BACKGROUND/AIMS: Elastography is a promising new medical imaging modality, displaying spatial distribution of biomechanical properties such as local tissue strain response to an applied stress. To develop a reproducible test protocol for skin elastography, the effect of various parameters on skin stiffness measurements was investigated. METHODS: The parameters investigated were: history of skin loading before test loading (preconditioning), direction of test loading (anisotropy) and posture (pre-stress). If a sample of skin is loaded, its stiffness will temporarily change. Finally, the reproducibility of skin stiffness and anisotropy measurements, using the developed techniques, was investigated. RESULTS: By measuring how the stiffness changed with different time delays between loading cycles, the time required for healthy skin to return to its original pre-loaded state was in the region of 125 s. A second finding, which supports and extends previous work, was that skin stiffness varied with direction, by an approximate factor of 2, and that anisotropy was less apparent with preconditioned skin than non-preconditioned skin. Study of the effect of posture showed that care needs to be taken over which stiffness measure is used. For example, measurement of the load at a given displacement was found to be highly dependent on posture, whereas measurement of the phase III stiffness was independent of posture. CONCLUSION: It was shown that when the measurement variables and methods of analysis were standardised, skin stiffness could be measured reproducibly enough to distinguish between the stiffest and softest directions, and that these methods allowed formation of skin elastograms free from confounding influences.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".