Full‐Thickness Skin Graft according to Surrounding Relaxed Skin Tension Line Improves Scar Quality in Facial Defect Coverage: A Retrospective Comparative Study
Bibliographic record
Abstract
A full-thickness skin graft (FTSG) is useful for covering small skin and soft tissue defects. In this paper, we suggest FTSG in consideration of the relaxed skin tension line (RSTL) concept for scar quality improvement since FTSG has disadvantages, including contour irregularities and mismatches of color and texture. We conducted a retrospective chart review of twenty-one patients with skin cancer on the face who underwent wide excision and FTSG by a single surgeon from October 2013 to July 2019. Twenty-one patients with skin cancer on the face were divided into RSTL-matched and RSTL-unmatched groups, and FTSG was performed. Each group was subjected to scar assessment three months after surgery. Observer assessment was performed by five independent observers using the observer component of the patient and observer scar assessment scale (POSAS) and Vancouver scar scale (VSS). Our results indicate that there were significant differences between the RSTL-matched and RSTL-unmatched groups in the VSS and POSAS components. In addition, the RSTL-matched group showed a natural appearance with surrounding tissue in the dynamic animation phase compared to the unmatched group. RSTL-matched FTSG can be an attractive option for face skin and soft tissue defect coverage. (An earlier version of this paper has been presented at the International Conference on PRS Korea 2020.).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".