Pilot Clinical Evaluation of PoreSkin: A Human Acellular Dermal Matrix in Burn Scars.
Bibliographic record
Abstract
Background: An extensive full-thickness wound need a graft, sometime very large. However, donor sites are often limited. Dermal substitutes are among the tissue-engineered products applied to clinical use. PoreSkin, a human acellular dermal matrix (hADM) manufactured by the Faculty of Medicine, Chulalongkorn University, is the first human dermal substitute developed in Thailand. Objective: Assess the safety and ability in achieving durable and definitively cosmetic coverage using PoreSkin. Material and Method: Eleven hypertrophic burn scars were enrolled in the present study. After scar excision, PoreSkin was placed followed by delayed split-thickness skin graft, three weeks later. The primary outcomes were the engraftment rate of the Poreskin and the skin graft. The secondary outcomes included complications and the final cosmetic appearance. Results: The engraftment rate of PoreSkin was 97.7% at day 21. The engraftment rate of autologous sheet skin graft placed over PoreSkin was 91.8%. Regarding the quality of the scar, using the Vancouver scar scale, it shows a statistically significant improvement (p<0.05). No major complications or rejection were observed. Conclusion: The performance of PoreSkin as a human acellular dermal matrix (hADM) is comparable to other commercial dermal substitutes in term of engraftment rate, complications, and rejection.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".