One-stage limb Pelnac® reconstruction after removal of skin cancer: safety, efficacy, and aesthetic outcomes
Bibliographic record
Abstract
Abstract Objective To assesse the outcomes of one-stage limb reconstruction after removal of skin cancers defect. Methods This prospective study was conducted from September 2017 to January 2020 and included 15 patients. All patients underwent extensive tumor resection and one-stage Pelnac® reconstruction of large skin defects, and regular postoperative follow-up was scheduled. At the 6-month follow-up, tumor recurrence and scar quality was assessed using the Vancouver Scar Scale (VSS). None of the patients exhibited infection, wound necrosis, hematoma, seroma, or recurrence. Results All the skin grafts were well accepted by the patients. Nine patients reported normal or near-normal sensory function, while six reported slight sensory loss. No cases of significant functional loss were observed. We enrolled 10 men and 5 women with a mean age of 63.9 years (range: 46-78 years). The mean follow-up duration was 20.6 months (range: 12-36 months). The skin tumors were located on the feet (n = 4), forearms (n = 3), and legs (n = 8). The malignant tumors included malignant melanomas (13.3%), basal cell carcinomas (33.3%), and squamous cell carcinomas (53.3%). The mean operative time was 40.7 min. Two patients underwent radiotherapy. The average length of hospital stay was 2.6 days. The mean skin defect area was 33.2 cm2 (range: 16.6-51.6 cm2). The patient satisfaction score (regarding the aesthetic appearance of the grafted area) was 79.7/100, and the VSS score was 3.8. Conclusion Pelnac® dermal templates facilitate efficient and reliable reconstruction of skin defects after skin cancer resection.
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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 | 0.001 |
| 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".