<i>In Situ</i> Forming Nutritional and Temperature Sensitive Scaffold Improves the Esthetic Outcomes of Meshed Split-Thickness Skin Grafts in a Porcine Model
Bibliographic record
Abstract
Objective: Full-thickness burn wounds require immediate coverage, and the primary clinical approaches comprise of skin allografts and autografts. The use of allografts is often temporary due to the antigenicity of allografts. In contrast, the availability of skin autografts may be limited in large burn injuries. In such cases, skin autografts can be expanded through the use of a skin mesher, creating meshed split-thickness skin grafts (MSTSGs). MSTSGs have revolutionized the treatment of large full-thickness burn injuries since the 1960s. However, contractures and poor esthetic outcomes remain a problem. We previously formulated and prepared an in situ forming skin substitute, called MeshFill (MF), which can conform to complex shapes and contours of wounds. The objective of this study was to assess the esthetic and wound healing outcomes in full-thickness wounds treated with a combination of MF and MSTSG in a porcine model. Approach: Either MSTSGs or MSTSG+MF was applied to full-thickness excisional wounds in Yorkshire pigs. Wound healing outcomes were assessed using histology, immunohistochemistry, and wound surface area analysis from day 10 to 60. Clinical evaluation of wounds were utilized to assess esthetic outcomes. Results: The results demonstrated that the combination of MSTSGs and MF improved wound healing and esthetic outcomes. Innovation: Effects of MSTSGs and reconstitutable liquid MF in a full-thickness porcine model were investigated for the first time. Conclusion: MF provides promise as a combination therapeutic regimen to improve wound healing and esthetic outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".