Artificial dermal substitutes for tissue regeneration: comparison of the clinical outcomes and histological findings of two templates
Bibliographic record
Abstract
OBJECTIVE: Artificial dermal substitutes (DSs) are fundamental in physiological wound healing to ensure consistent and enduring wound closure and provide a suitable scaffold to repair tissue. We compared the clinical and histological features of two DSs, Pelnac and Integra, in the treatment of traumatic and iatrogenic skin defects. METHODS: This prospective observational study involved 71 randomly selected patients from our hospital. Wound healing was analyzed using the Wound Surface Area Assessment, the Vancouver Scar Scale, and a visual analog scale. Histological and immunohistochemical evaluations were also performed. RESULTS: At 2 weeks, greater regeneration with respect to proliferation of the epidermis and renewal of the dermis was observed with Pelnac than with Integra. At 4 weeks, the dermis had regenerated with both DSs. Both templates induced renewed collagen and revascularization. Differences in the Vancouver Scar Scale score were statistically significant at 4 weeks and 1 year. Pelnac produced a significant increase in contraction at 2 weeks with increasing effectiveness at 4 weeks. Integra produced a higher percentage reduction in the wound surface area and a shorter healing time than Pelnac for wounds >1.5 cm deep. CONCLUSION: Our observational data indicate that both DSs are effective and applicable in different clinical contexts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".