Dermal Regeneration Matrix in the Treatment of Acute Complex Wounds
Bibliographic record
Abstract
INTRODUCTION: Dermal regeneration matrices (DRMs) represent a significant advance in wound treatment, but their use remains limited because of high associated costs. Used correctly, DRMs help improve aesthetic and functional results of skin-grafted areas. OBJECTIVE: This case series reports the use of a DRM of 1-mm and 2-mm thickness in the management of acute complex wounds. MATERIALS AND METHODS: This is a retrospective analysis of a cohort of patients treated between 2015 and 2018. Complex wounds were defined as those with extensive loss of skin and subcutaneous tissue, or as those in critical areas, that required sequential and specialized treatment. Management of acute wounds involved debridement of devitalized tissue, wound bed preparation, DRM implantation, and split-thickness skin grafting (STSG). Negative pressure wound therapy (NPWT) was used in all cases preoperatively, after DRM implantation, and after STSG. Results of integration of DRM and skin grafts were subjectively evaluated. The Vancouver Scar Scale was used to evaluate results 12 months postoperatively. RESULTS: Traumatic injuries were the most common etiology, and the extension of the treated wounds varied between 4 cm × 5 cm to 42 cm × 28 cm, in the greatest dimensions. A 2-mm-thick matrix was used in 14 cases, with skin grafting after 7 to 9 days. In 6 cases, a 1-mm-thick matrix was used, immediately followed by skin grafting. Negative pressure wound therapy was used in all cases. Dermal regeneration matrices and skin graft integration rates of almost 100% were achieved in all cases. No complications occurred. CONCLUSIONS: The results showed use of DRM and NPWT was a good reconstructive option in the management of acute complex wounds that required STSG. With proper patient selection, such treatment is an important tool in the armamentarium of reconstructive procedures.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".