Utility of RECELL for Traumatic Skin Defects: Protocol for a Prospective, Single-Arm Pilot Study
Bibliographic record
Abstract
Background: Skin grafting is currently the gold standard for managing traumatic skin defects. However, split-thickness skin grafting, the most common technique, is challenging because it yields a limited amount of skin and creates a new wound at the healthy donor site. RECELL (Avita Medical), a noncultured skin cell suspension technique, yields healing outcomes with comparable duration and quality to skin grafting for burn trauma. Although RECELL is widely used for burn trauma, its use for severe traumatic skin defects is not yet prevalent in Japan. Objective: This study aims to evaluate the utility of the RECELL skin reconstruction technique in trauma cases involving significant skin defects. Specifically, the primary aim is to validate the efficacy of the RECELL skin reconstruction technique when combined with split-thickness mesh grafting in patients with significant posttraumatic skin defects, with a particular focus on wound closure and scar quality. Methods: This prospective, single-center, open-label pilot study will enroll patients aged 16 years and older with traumatic limb skin defects ≥160 cm² and sufficient dermal-like tissue. Participants will undergo RECELL-assisted autologous skin grafting with mesh grafts under general anesthesia. Wound healing, scarring (Vancouver Scar Scale), and pain (visual analog scale) will be assessed up to 24 weeks postoperatively. The primary end point is ≥95% reepithelialization at postoperative day 14. Descriptive statistical analyses will be performed to evaluate outcomes and inform future randomized trials. Results: Patient recruitment began in April 2024 and is currently ongoing in our hospital. As of April 10, 2025, 10 patients had been screened, of whom 4 were enrolled. No adverse events or protocol deviations have been noted. Conclusions: This study aims to validate the utility of RECELL for skin defects resulting from trauma by assessing wound closure and scar formation, including adverse events, delayed healing, infection, and durability.
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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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".