Use of a novel autologous cell-harvesting device to promote epithelialization and enhance appropriate pigmentation in scar reconstruction
Bibliographic record
Abstract
BACKGROUND: Epidermal replacement is an important step in the management of patients with post-traumatic and iatrogenic scars. Skin-colour variation from disease or trauma causes significant changes in self-image and appearance. AIM: The aim of our study was to analyse the results obtained with a novel autologous cell-harvesting system (ReCell) for epidermal replacement in patients with post-traumatic scars that had not improved with any other surgical procedure. METHODS: We recruited 30 patients with post-traumatic or iatrogenic scars admitted to our department over 2 years. The primary endpoints of the study were: (i) time for complete epithelialization (both treated area and biopsy site) and (ii) aesthetic and functional quality of the epitheliaization (colour, joint contractures). Infections, inflammations or any adverse effects of the procedure were also reported. RESULTS: In total, 30 patients were analysed. The aesthetic and functional outcomes were rated by both patient and surgeon. Pigmentation was rated by the Vancouver Scar Scale. Most (80%) of the patients had an excellent or good outcome, with pigmentation rated as normal in 60% of the group. CONCLUSIONS: The procedure is a feasible, simple and safe technique. It gives similar results to skin grafting but because it harvests from much smaller areas, can open possible future applications in the management of patients with large scars.
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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 | 0.000 |
| 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) | 0.002 | 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".