Evaluation of Artificial Dermis for the Treatment of Leg Ulcers: Clinical Outcomes From an Exploratory Study
Bibliographic record
Abstract
Chronic leg ulcers present a significant clinical challenge due to their prolonged healing time and high recurrence rates. This prospective, multi-centre, non-randomised, observational study investigated the efficacy of a dermal regeneration template in improving skin graft integration for chronic leg ulcer treatment. Thirty patients were enrolled, with a control group receiving only skin grafts to evaluate the additional benefits of the template. Patients were assessed for pain levels, healing rates, wound retraction, pruritus, dressing type, analgesic use, complications, surgeon-evaluated wound recovery using the Vancouver scale, quality of life through the EuroQol questionnaire and photographic wound documentation. At 18 months, 70.0% of patients achieved at least a 50% reduction in wound surface area and 56.7% experienced complete wound closure. Significant improvements were observed in pain and discomfort (p = 0.0125), mobility (p = 0.0267), pain levels (p = 0.0340), vascularity (p = 0.0275) and overall wound reduction (p = 0.0368). The control group demonstrated lower wound reduction and complete healing rates, reinforcing the superior effectiveness of the dermal regeneration template in combination with skin grafting. This study highlights the potential of this approach to accelerate wound healing, reduce patient discomfort and enhance quality of life compared to traditional skin grafting alone.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".