Superiority of occipital donor sites for split‐thickness skin grafting in dermatosurgery: Results of a prospective randomized controlled study
Bibliographic record
Abstract
BACKGROUND: Split-thickness skin grafts are commonly used in dermatosurgery. For occipital donor sites, retrospective studies have shown good results with respect to graft take and healing rates. Nevertheless, the majority of grafts in dermatosurgery are harvested from the thigh. To date, there has been no prospective randomized controlled study comparing occipital versus femoral donor sites. PATIENTS AND METHODS: Following micrographically controlled R0 tumor resection, 108 patients were randomized prior to undergoing split-thickness skin grafting (donor site: occiput vs. thigh). Follow-up examinations were carried out on day 3, 5, 7, and 14, as well as one month and three months after surgery. Documented data included graft take rates, re-epithelialization rates at the donor site, pain, cosmetic outcome, Vancouver Scar Scale (VSS), and complications. RESULTS: Occipital donor sites showed significantly faster reepithelization, less pain, fewer complications, a better cosmetic outcome, and better results on the VSS. With regard to graft take rates, grafts harvested from the occiput were significantly superior on days 3 and 5. CONCLUSIONS: This is the first randomized controlled trial showing a significant superiority of occipital compared to femoral donor sites regarding re-epithelialization, pain, cosmetic outcome and the Vancouver Scar Scale.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".