Safety and Efficacy of Hairy Scalp Donors in Thick Split-Thickness Skin Grafting: Complications and Outcomes of Grafts
Bibliographic record
Abstract
BACKGROUND: Several studies have evaluated thin and thick split-thickness scalp skin grafts (STSG); however, detailed outcomes of thick grafts have not been well defined. This study aimed to evaluate the safety, complications, and outcomes associated with thick STSGs harvested from the hairy scalp. METHODS: This retrospective study included 102 Korean patients who underwent thick STSGs from the hairy scalp using a dermatome depth setting of 0.4 mm (16 mils=0.016 inches). Preoperative scalp thickness was measured using ultrasonography. Graft thickness was evaluated histometrically at its thickest portion and compared with the dermatome depth setting. Outcomes and complications were assessed for both donor and recipient sites. RESULTS: The cohort included 71 males and 31 females [mean age: 33.3 y (±23.8; range 0.8-78)]. The mean ultrasound-measured scalp thickness was 67.4 mils (±16.8; range: 32-106). The mean dermatome setting was 21.5 mils (±4.5; range: 16-32), and the mean histometric graft thickness was 26.7 mils (±6.9; range: 11-50). Donor site healing averaged 9.7 days (±2.0; range: 6-15). Complications at the recipient site included hypertrophic scarring (n=9), partial graft loss (n=8), contracture (n=7), dyschromia (n=6), and hair transfer (n=6). Vancouver Scar Scale (VSS) scores improved substantially in representative cases, demonstrating favorable aesthetic and functional improvements. CONCLUSIONS: All donor sites achieved successful regenerative healing. Thick scalp grafts demonstrated reduced contracture and superior color matching. These findings support the use of the hairy scalp as a safe and effective donor site for thick STSGs.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".