Possibilities of elastography for assessing the stiffness of post-traumatic scars in the process of complex treatment
Bibliographic record
Abstract
Background. Elastography is a modern variant of ultrasound imaging, which allows to study the mechanical properties of tissue. Given the urgency of the problem of treatment of posttraumatic scars, the study of their objective characteristics is of scientific and practical interest. Aims. To investigate the use of shear wave elastography as a tool for quantitative assessment and visualization of stiffness of posttraumatic scars in the process of complex treatment. Methods. A prospective open clinical study was conducted between December 2023 and May 2024. The state of posttraumatic scars was studied using ultrasound in shear wave elastography mode in the process of complex treatment with laser technology and carboxytherapy. Clinically unchanged contralateral skin areas served as a control. Clinical indices of scar lesions severity and dermatologic quality of life index were calculated in parallel with elastography indices. Results. 45 people diagnosed with post-traumatic scars were included in the study. In comparison with normal skin, significant differences in tissue stiffness were obtained according to shear wave elastography, which persisted throughout the study. An increasing decrease in the stiffness of scar tissues in the process of complex treatment was registered, and significant differences in comparison with the initial level were obtained already after 1 month (p 0,001). Elastography values correlate with the clinical index of POSAS scar assessment performed by the doctor (before treatment rs = 0.36; p = 0.016; after treatment rs = 0.35; p = 0.017), partially (only after treatment) with the Stony Brook scale (rs = –0.36; p = 0.015). No correlations were obtained with the Vancouver scale and POSAS in patient performance. Conclusion. Ultrasound in the mode of shear wave elastography is reasonable to use for objective assessment of scar condition in the course of treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".