The “Swing-Door” Regrafting of Donor Site: An Alternative Method for Split-Thickness Skin Graft in the Hand
Bibliographic record
Abstract
Abstract Background Skin defects in the hands are common injuries, and autologous skin grafting is the ideal treatment. However, complications can occur at the donor and recipient sites. This study compares the “Swing-door” technique with conventional skin grafting. Methods From August 2019 to February 2023, 19 patients with skin defects of hand underwent the “Swing-door” split-thickness skin graft (STSG) technique. The thin epithelial layer was elevated with proximal part attached. Skin graft was harvested beneath. Donor site was then closed with epithelial flap like a “Swing-door”. The outcomes were evaluated in terms of healing time, scar formation, and pain at the donor and recipient sites. The data were compared with the conventional STSG. Results The “Swing-door” group had lower graft take percentages, but complications did not significantly differ between the two groups. The “Swing-door” technique resulted in better cosmetic outcomes, as evidenced by lower Vancouver Scar Scale scores, faster donor site epithelialization, and reduced pain and discomfort during the early postoperative period, as measured by Visual Analog Scale. Conclusion The “Swing-door” STSG is a useful alternative for treating hand skin defects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".