Assessment of Hair Density and Sub-epidermal Tissue Thickness in Burn Scars Using High-Definition Ultrasound Imaging
Bibliographic record
Abstract
Burn scars show significant differences in structure, pigment, and hair density/sparsity from unburned skin, yet no formal documentation of these changes can be found in the literature. Evaluation of these differences is essential to assessing future intervention outcomes. The study was a prospective controlled clinical trial. Included were 19 adult burn survivors (18-63 years old, average age 47; 15 male, 4 female, 14 Caucasian, 2 African American, 1 Hispanic; 11 flame burns, 5 scald burns, 2 grease burns and 1 electrical burn, 2%-60% TBSA) with conspicuous, mature scars. All study subjects had either skin-grafted or nongrafted scars, as well as healthy skin in the same body area, to control for intraindividual variability. All scars were at least 9 months old and at a minimum 2 × 2 cm2 in size. On each individual, at least one nongrafted scar or one grafted scar and healthy skin was imaged with a high-definition ultrasound device (Longport, Inc., Glen Mills, PA, 35MHz probe, 1500 m/s). Vancouver scar scale was assessed. Although scarred skin had significantly fewer follicles than healthy skin in both grafted (P < .0001) and un-grafted sites (P = .0090), there were even significantly fewer follicles in grafted scars than un-grafted scars (P = .0095). In thickness of the sub-epidermal layer, there was no difference between grafted and un-grafted scars (P = .1900). Both kinds of scars had a significantly thicker sub-epidermal layer than healthy skin (P = .0010). Vancouver scar scale was 7.4 for grafted and 4.6 for nongrafted scars with grafted flame burn scars ranging higher than all others (5-11). There was no discomfort during the imaging, and no adverse events occurred during the study period. Our study demonstrates two clear morphologic differences between scars and healthy skin: thickness of the sub-epidermal layer and hair follicle density. Grafted burn scars were shown to contain fewer hair follicles than un-grafted 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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".