Immediate regraft of the remnant skin on the donor site in split-thickness skin grafting
Bibliographic record
Abstract
BACKGROUND: Skin defects of head and neck need reconstruction using various local flaps. In some cases, surgeons should consider skin graft for large skin defect. It is important to heal skin graft and donor sites. The authors investigated wound healing mechanisms at the donor sites with split-thick-ness skin graft (STSG). In this study, the authors compared two types of immediate regraft including sheets and islands for the donor site after facial skin graft using remnant skin. METHODS: The author reviewed 10 patients who underwent STSG, from March 2015 to May 2017, for skin defects in the craniofacial area. The donor site was immediately covered with the two types using remnant skin after harvesting skin onto the recipient site. Depending on the size of the remnant skin, we conducted regraft with the single sheet (n= 5) and island types (n= 5). RESULTS: On postoperative day 1 and 3 months, the scar formation was evaluated using the Patient and Observer Scar Assessment Scale (POSAS) and Vancouver Scar Scale (VSS). Total POSAS and VSS scores for the island type were lower than in single sheet group after 3 months postoperatively. There was significant difference in specific categories of POSAS and VSS. CONCLUSION: This study showed a reduction in scar formation following immediate regrafting of the remnant skin at the donor site after STSG surgery. Particularly, the island type is useful for clinical application to facilitate healing of donor sites with STSG.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".