Objective quantification of burn scar stiffness using shear-wave elastography: Initial evidence of validity
Bibliographic record
Abstract
Shear-wave elastography (SWE) is an ultrasound based technology that can provide reliable measurements (velocity) of scar stiffness. The aim of this research was to evaluate the concurrent validity of using both the measured velocity and the calculated difference in velocity between scars and matched controls, in addition to evaluating potential patient factors that may influence the interpretation of the measurements. METHODS: A cross-sectional study of 32 participants, with 48 burn scars and 48 matched contralateral control sites were evaluated with SWE, the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS) tactile sub-scores. RESULTS: Spearman's rho demonstrated high correlations (r > 0.7) between the measured scar velocity and both the POSAS and VSS pliability sub-scores, whereas moderate correlations (r > 0.6) were found with the calculated difference in velocity. Regression analysis indicated that the association of increased velocity in scars, varied by length of time after burn injury and gender. Body location and Fitzpatrick skin type also demonstrated significant associations with velocity, whereas age did not. CONCLUSION: SWE shows potential as a novel tool to quantify burn scar stiffness, however patient factors need to be considered when interpreting results. Further research is recommended on a larger variety of scars to support the findings.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".