Individualized surgical treatment using decellularized fish skin transplantation after enzymatic debridement: A two years retrospective analysis
Bibliographic record
Abstract
Over the past few years, treatment of burn injuries has evolved beyond primary surgical therapy with the development of enzymatic debridement and new types of skin replacement materials by providing complex personalized therapy concepts aimed at preserving and replacing the dermal layer of the skin. The aim of our study was to develop an individualized treatment algorithm for mixed depth burn wound and evaluate the outcomes of individualized combined treatment of mixed depth burn wounds with enzymatic debridement and decellularized fish skin. A total of 18 patients with a mean age of 34.8 years and mean follow-up of 447.6 days were included. The mean total burn surface area was 12.3%. All patients received enzymatic debridement and an average area of 247.2 cm 2 of decellularized fish skin. Days until complete epithelization were 49.4 ± 25.79 days. No patient developed scar contracture or keloid. The Patient and Observer Scar Assessment Scale (POSAS) observer scale showed an overall impression average of 2.2 ± 0.83. The POSAS patient scale showed an overall impression average 2 ± 0.7. The Vancouver Scar Scale showed an average score of 1.89 ± 1.45. In conclusion, combined treatment using enzymatic debridement and decellularized fish skin, polylactide membrane, or split skin grafts allows for a more individualized therapy for mixed depth burn wounds. Fish skin was found to provide a satisfying result in terms of the overall outcome of the developed scar tissue and could lead to a reduction in the area that requires autologous transplantation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".