A case-control study evaluating single and dual layer dermal matrix in treating scar contractures using the Vancouver and POSAS scar scales, and correlations with retraction and function in treating scar contractures
Bibliographic record
Abstract
The dermal matrix appeared in the late 1990s and became an important tool in burn treatment. Following its success, new matrices soon emerged and were employed to treat burn contracture, but few studies have compared the different types available. This study evaluated two different dermal regeneration matrices comprised of one (Matriderm) or two layers (Integra) according to the Vancouver and the Patient and Observer Scar Assessment (POSAS) evaluation scales with regard to treatment of burn scar contracture. In this prospective, case-control study, 24 patients with impaired mobility secondary to burns were divided into two groups and treated with dual or single layer matrices. Data were collected for intraoperative and postoperative complications, percentage take for matrix and graft areas, Vancouver scar scale score, POSAS score for skin quality assessment, area of matrix take, and skin graft take, and degree of mobility. Both matrices produced significant improvement according to the Vancouver and POSAS assessments over 12 months, but the dual layer matrix yielded better results than the single layer matrix in terms of retraction of the grafted area at 12 months, functional improvement, and skin quality as measured by the Vancouver and POSAS scales. According to statistical analysis, with p<0.05. • The first study to directly compare two dermal regeneration templates using the Vancouver Scar scale and POSAS. • The areas treated with integral matrix had better results, but the two matrices showed significant improvement in function and quality of skin. • We present the correlation between scales and skin quality, retraction and function.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".