Blood perfusion in hypertrophic scars and keloids studied by laser speckle contrast imaging
Bibliographic record
Abstract
BACKGROUND: This study used laser speckle contrast imaging (LSCI) to evaluate the difference in blood perfusion between hypertrophic scars and keloids. MATERIALS AND METHODS: A total of 30 keloids, 21 early hypertrophic scars, 20 proliferative hypertrophic scars, 20 regressive hypertrophic scars, and 20 mature hypertrophic scars were enrolled into this study. Vancouver Scar Scale (VSS) was assessed by a plastic surgeon. LSCI was used to evaluate perfusion of the whole (W), marginal (M), central (C) regions, and surrounding normal skin of the scars, and ratios (M/N, C/N) were calculated. RESULTS: The perfusion of the marginal region in the keloid was significantly higher than that of the central region. Nevertheless, there was no significant difference in perfusion between the central and marginal regions in the early, proliferative, regressive, and mature hypertrophic scars. The degree of perfusion and perfusion ratio in the marginal region of keloid was similar to that of proliferative hypertrophic scars, and the degree of perfusion and perfusion ratio in central region of keloid group was similar to that of early and regressive hypertrophic scars. CONCLUSIONS: The difference in perfusion distribution in keloids and hypertrophic scars may provide ideas for their identification. LSCI may be a useful method for differentiating between keloids and hypertrophic scars.
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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 | 0.001 |
| 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".