Application of dermal regenerative template in reconstructing skin defects after plantar malignant melanoma excision.
Bibliographic record
Abstract
PURPOSE: The excision of plantar malignant melanoma frequently leads to wide skin defects on the plantar surface. This study aimed to investigate the advantages and feasibility of dermal regenerative template reconstructing plantar blemishes caused by malignant melanoma. METHODS: 28 patients identified with plantar malignant melanoma were included in this retrospective article. Eighteen patients received immediate skin grafts after wide excision skin graft (SG) group), whereas the remaining 10 patients were treated with dermal regenerative template (DRT) (Lando ®, Shenzhen TsingCare Medical Co. Ltd) 14 days before skin grafts (DRT group) and the postoperative survival rate in the two groups was analyzed. During the 6-month follow-up, we compared the scar index, plantar pain, and recurrent skin graft ulcer incidence on the skin grafts area. RESULTS: Postoperative survival rate in the DRT group (91.75% ± 7.64%) was higher than in the SG group (80.51% ± 7.17%). The DRT group showed less scar formation on Vancouver scar scale (VSS index): 3.40 ± 1.07 than the SG group (VSS index: 6.33 ± 0.68). The dermal regenerative template alleviated plantar pain and decreased the incidence of ulcer on the skin grafts area. CONCLUSIONS: The dermal regenerative template not only improves the survival rate of skin grafts but also alleviates scar condition, plantar pain and recurrent skin graft ulcer. This study provides a new reconstructive strategy in plantar skin defects after the excision of malignant melanoma.
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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".