Assessment of Donor Site Scar Outcomes, Healing Time, and Postoperative Complications Associated With Split Thickness Skin Grafts Harvested From the Hair Bearing Scalp
Bibliographic record
Abstract
BACKGROUND: The hair-bearing scalp is an underused donor site for split-thickness skin grafts (STSG). OBJECTIVE: Evaluating the donor site scar outcomes, healing times, and complications associated with STSG harvested from the hair-bearing scalp. MATERIALS AND METHODS: During this prospective observational study, donor site healing was assessed on postoperative Days 8 and 30. Donor site scar outcomes were quantified at 1 month using the Vancouver Scar Scale. All postoperative complications were collected during the 30-day follow-up window. RESULTS: 80% of donor sites was fully healed at 1-week follow-up. Vancouver Scar Scale score at the donor site was 0.26 at 1-month follow-up. All patients experienced full hair regrowth. Maximum pain scores were reported on the night of surgery (Vancouver Scar Scale 1.8), with quick resolution in days to follow. No major complications were reported. All STSG obtained from the scalp had full take and good texture and color match with the recipient site. CONCLUSION: The hair-bearing scalp is an excellent donor site for split-thickness skin graft harvesting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".