Comparative study on the donor site aesthetic outcome between epidermal graft and split‐thickness skin graft
Bibliographic record
Abstract
Donor site aesthetic outcomes of epidermal graft (EG) vs split-thickness skin graft (SSG) have yet to be objectively compared. Here, we evaluate donor site healing using a validated scar assessment tool and digital colorimetric technique, which compares colour in a consistent and objective manner. Ten patients (SSG (n = 5) and EG (n = 5)) were included. Donor site scarring was evaluated using the Vancouver Scar Scale (VSS) at Week 6 and Month 3. Colorimetric measurement was performed at Weeks 3 and 6 and Month 3. The mean donor site healing time for EG was significantly shorter (EG: 4.6 days (95% c.i. 3.8-5.3), SSG: 16.8 days (95% c.i. 13.3-20.1) (P = 0.003)). The VSS scores of the EG donor site were lower at Week 6 and Month 3(P < 0.001). The colour match between the donor site and surrounding skin for EG was better compared with SSG at all time points and was almost identical to their surrounding healthy skin at Month 3. This study is the first to objectively measure the clinical appearance of the EG donor site against SSG. EG donor site has faster healing with excellent scarring and good colour match with its surrounding normal skin at all time points compared with SSG.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".