Measurements of scar properties by SkinFibroMeter<sup>®</sup>, SkinGlossMeter<sup>®</sup>, and Mexameter<sup>®</sup> and comparison with Vancouver Scar Scale
Bibliographic record
Abstract
BACKGROUND: An objective measurement of scar is important for evaluating treatment outcomes. However, to date, there is no 'gold standard' for quantitative measurement of properties of hypertrophic scar. Existing objective modalities are neither portable nor easy to use. OBJECTIVE: and subjective assessment with Vancouver Scar Scale (VSS) of keloid and hypertrophic scar. METHODS: A total of 25 patients with keloids and hypertrophic scars were enrolled in this study. Patients were treated with intralesional triamcinolone acetonide at 2-6 week intervals. Scar assessments using VSS, Skinfibrometer, Skinglossmeter, and Mexameter were performed in both scars and contralateral normal skin at each treatment session. Correlations between the measurements by these tools and VSS parameters were examined. RESULTS: We found statistically significant differences between scar and contralateral normal skin using Skinfibrometer, Skinglossmeter, and Mexameter. A strong correlation was found between the VSS pliability scores and the stiffness of skin of Skinfibrometer (r = 0.628, P < 0.001). VSS vascularity scores showed weak correlation with erythema index of Mexameter (r = 0.372, P < 0.001). However, no correlation appeared to exist between any parameters of VSS and Skinglossmeter and between VSS pigmentation scores and the melanin index of Mexameter. CONCLUSION: In our study, Skinfibrometer can be an objective noninvasive evaluation tool for pliability of the scar.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".