Evaluation of Clinical and Biomechanical Features of Scars Resulting from the Treatment of Burn Contractures Comparing Acellular Dermal Matrices
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare clinical and biomechanical features of scars resulting from the treatment of burn contractures using different acellular dermal matrices (ADM). SUMMARY BACKGROUND DATA: Extensive burns often lead to severe sequelae, such as skin contractures, that can be treated using ADM to improve the quality of these scars. METHODS: A prospective, randomized and controlled clinical trial was performed including patients with burn contractures at least 1-year post-burn, treated using split-thickness skin graft (STSG). These patients were randomized into 4 groups: control (received only STSG without ADM), Integra (ADM + STSG), Matriderm (ADM + STSG), and Pelnac (ADM + STSG). Exclusion criteria were loss of follow-up and graft integration failure. The evaluation was performed using the Vancouver Scar Scale (VSS), the durom-eter, and the cutometer in areas of normal skin, hypertrophic scar, and surgical scar, at least 1 year after the surgery. RESULTS: In the VSS, durometer, and cutometer evaluation, there was no difference in the comparison of surgical scars among groups. Analyzing each group, with an intraindividual evaluation comparing areas of normal skin with surgical scars, the results suggested a possibility of a surgical scar hardness closer to normal skin for Integra and Matriderm groups measured with the durometer. In the cutometer evaluation, surgical scars were not comparable to areas of normal skin. CONCLUSIONS: This study suggests that there is no difference in the quality of scar assessed through the analysis of clinical and biomechanical features comparing acellular dermal matrices (Integra, Matriderm, and Pelnac) and only split-thickness skin graft without a dermal matrix.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".