Heterogeneous Features of Keloids Assessed by Laser Speckle Contrast Imaging: A Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Keloids are described as benign dermal fibroproliferative lesions, and vascularization may play a significant role in their pathogenesis. In this study, laser speckle contrast imaging (LSCI) was used to assess perfusion within keloids and surrounding skin, and perfusion of keloids at different stages was compared. STUDY DESIGN/MATERIALS AND METHODS: A total of 59 patients with 110 untreated keloids on the anterior chest were enrolled in this study. Different keloid stages (progressive, stable, and regressive) were defined according to patients' descriptions of whether keloids became larger, stable, or smaller during the previous year. Vancouver Scar Scale (VSS) was assessed by a plastic surgeon, and patient reports on pain and itching were documented. LSCI was used to evaluate blood perfusion of keloids (K), skin adjacent to keloids (A), and nonadjacent skin (N). The mean perfusion of these regions was determined, and ratios (K/N, A/N) were calculated. RESULTS: A heterogeneous perfusion map was observed among the keloid groups, as well as within each keloid. A positive correlation was found between keloid perfusion and VSS. There were 62 (56.4%) keloids in the progressive stage, 33 (30.0%) keloids in the stable stage, and 15 (13.6%) keloids in the regressive stage. The mean K/N ratios in the progressive, stable, and regressive stages were 2.3 ± 0.5, 1.8 ± 0.3, and 1.5 ± 0.5, respectively. The mean A/N ratios were 1.2 ± 0.4, 1.2 ± 0.2, and 1.0 ± 0.5, respectively. Within each keloid, significantly higher perfusion was noted in the keloid and adjacent skin compared with nonadjacent skin. CONCLUSION: These results indicate that LSCI is a promising technique for evaluating keloid blood perfusion and distinguishing heterogeneous keloids. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.
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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) | 0.001 | 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".