Effectiveness of double-layer artificial dermis repair material combined with autologous skin patch in repairing deep skin and soft tissue defects: a retrospective study
Bibliographic record
Abstract
Objectives With the development of biotechnology, double-layer artificial dermis repair material has been increasingly employed to repair deep skin and soft tissue defects. This study aims to investigate the effectiveness of double-layer artificial dermis repair material combined with autologous skin patch in repairing deep skin and soft tissue defects.Methods This study conducted a retrospective analysis of 18 patients with deep skin and soft tissue defects who were treated with a combination of double-layer artificial dermal repair materials and autologous skin grafting at our hospital between January 2022 and January 2024.Results A total of 18 patients were treated, including 7 males and 11 females, with an average age of 41.7 ± 19.12 years. All patients exhibited good vascularization of the double-layer artificial dermal material postoperatively. In one patient, only a small part of the autologous skin patch survived after the first grafting, and the skin healed well after the second grafting. The remaining patients healed well after the operation. Two months after the operation, the results showed that no obvious hyperplastic contracture scar was found in the wound grafts of 18 patients, and the total Vancouver Scar Scale (VSS) score was not significantly different between 1 month and 2 months after the operation (p > 0.05).Conclusion Double-layer artificial dermis repair material combined with autologous skin patch provides a reliable, less invasive and simple treatment for deep skin and soft tissue defects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".