A comparative experimental trial between two artificial skin substitutes: Integra bilayer versus Matriderm 2mm to cover a full-thickness skin wound
Bibliographic record
Abstract
Background: Artificial Skin Substitutes (ASS) are bioengineered materials designed as acellular scaffolds that act as substrate for cellular growth, proliferation and support for new tissue formation. ASS are indicated as alternatives to wound healing in circumstances when standard therapies are not feasible. ASS are a reality since 1981 and there currently are a huge range of matrix to choose. Integra® has been the first one and the most used worldwide. Nevertheless, there are other ASS with different characteristics than can improve it. Matriderm® 2mm is a new acellular scaffold similar to Integra®, but with a different material composition that allows an earlier cover of the skin loss with a skin autograft.\nObjective: To compare the clinical outcomes between Matriderm® 2 mm versus Integra® bilayer, as a treatment to cover a full-thickness skin wound, using the clinical scales Vancouver Scar Scale (VSS) and Patient and Observes Scar Assessment Scale (POSAS).\nIntervention: Both ASS will be performed in two-step grafting procedure. The wound will be covered at first with an ASS and there will be a second stage surgery to graft a split-thickness skin autograft.\nMethods/Design: It is designed a multicenter, controlled, randomized and single blind trial. 154 patients with full-thickness skin wound that fulfill the inclusion and exclusion criteria will be included
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".