Evaluation and comparison of composite and split-thickness skin grafts using cutometer mpa 580.
Bibliographic record
Abstract
BACKGROUND: In our preliminary experiments we found that composite skin grafts consisting of allogeneic acellular dermis and thin epidermal autologous grafts applied to the excised burn wound in one stage led to better results in terms of viscoelastic properties than autologous split-thickness skin grafts. PATIENTS AND METHODS: In ten burn patients we applied composite skin grafts consisting of allogeneic acellular dermis and thin epidermal autologous grafts and followed the quality of the reconstructed skin cover with a special device, Cutometer MPA 580, over a period of four years. RESULTS: The cutometric curves demonstrated better viscoelastic properties in composite skin grafts than in conventional split-thickness skin grafts after four years. We found Cutometer MPA 580 to be an advantageous device for the objectification of improved quality of reconstructed skin cover. DISCUSSION: Among the various methods the cutometer showed the advantage of being a non-invasive, precise, and objective method of measuring skin's viscoelastic properties. The Vancouver Scar Score is a subjective evaluation of skin viscoelasticity. CONCLUSION: Our prospective clinical study clearly demonstrated that cutometric measurement produced objective results in contrast to clinical evaluation, the Vancouver Scar Score, and other non-quantitative methods. Our hypothesis that composite skin grafts consisting of allogeneic acellular dermis and thin epidermal autologous grafts applied onto the excised burn wound in one stage led to better results in terms of viscoelastic properties than autologous split-thickness skin grafts was fully confirmed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".