Dermal Regeneration Template in the Management and Reconstruction of Burn Injuries and Complex Wounds: A Review
Bibliographic record
Abstract
Background: Dermal scaffolds have created a paradigm shift for burn and wound management by providing improved healing and less scarring, while improving cosmesis and functionality. Dermal regeneration template (DRT) is a bilayer membrane for dermal regeneration developed by Yannas and Burke in the 1980s. The aim of this review is to summarize clinical evidence for dermal scaffolds focusing on DRT for the management and reconstruction of burn injuries and complex wounds. Methods: A comprehensive search of PubMed was performed from the start of indexing through November 2022. Articles reporting on DRT use in patients with burns, limb salvage, and wound reconstruction were included with focus on high-level clinical evidence. Results: DRT has become an established alternative option for the treatment of full-thickness and deep partial-thickness burns, with improved outcomes in areas where cosmesis and functionality are important. In the management of diabetic foot ulcers, use of DRT is associated with high rates of complete wound healing with a low risk of adverse outcomes. DRT has been successfully used in traumatic and surgical wounds, showing particular benefit in deep wounds and in the reconstruction of numerous anatomical sites. Conclusions: Considerable clinical experience has accrued with the use of DRT beyond its original application for thermal injury. A growing body of evidence from clinical studies reports the successful use of DRT to improve clinical outcomes and quality of life across clinical indications at a number of anatomical sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".