Autologous Scar-Related Tissue Combined with Skin Grafting for Reconstructing Large Area Burn Scar
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: This study introduced a novel method to reconstruct large areas of scarring caused by burns via combining autologous scar-related tissue with spit-thickness skin grafting (ASTCS). METHODS: 25 patients underwent reconstruction after scar resection surgeries around the joints were analyzed between Jan 2012 and Jan 2018. Patient demographics and clinical parameters were collected, autologous scar-related tissue was modified to meshed structure, and the split-thickness skin was acquired from the scalp. The scar was resected and punched by a meshing machine with a thickness of 0.3-0.5 mm at a ratio of 1:1. The secondary wounds were covered by the epidermis from a donor site. The surgical areas were bandaged for 7-10 days before the first dressing change. RESULTS: 25 patients (mean [SD] age, 26.4 [18.8] years; 16 [64%] men) underwent wounds reconstructive operations due to scar resection were reviewed. Wound location of 9 (22%), 8 (19.5%), 9 (22%), 7 (17.1%) and 8 (19.5%) cases were reconstructed in axillary, hand and wrist, popliteal fossa, elbow and neck, respectively. 39 sites of transplanted tissues survived well, and 2 sites were cured after two weeks of dressing changes. Except the analysis of injury causes, nutritional status, wound area and hospital days, patients with scar deformities in joint areas achieved satisfactory function by assessing the Vancouver Burn Skin Score and the Barthel Index Scale Scores after 12-month follow-up. CONCLUSIONS: Combining autologous scar-related tissue with skin grafting provided a novel method for treating large areas of burn scars with better functional outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it