Functional and Aesthetic Comparison between Grafts and Local Flaps in Non-Melanoma Skin Cancer Surgery of the Face: A Cohort Study
Bibliographic record
Abstract
Background: Non-melanoma skin cancers represent more than 90 % of malignant skin tumors, with an incidence of 19.46 cases/100,000 people per year in Italy; however, their real incidence is underestimated. Although there are several therapeutic strategies, the only one that can guarantee a 95 % healing rate and the possibility of performing histological examination is surgical excision with subsequent reconstruction of the injured area with direct closure and with skin graft, local, regional, or free flaps in cases involving greater damage. Material and Methods: Fifty-four patients underwent post-oncological head/face reconstructive surgery with skin graft or local flap between November 2021 and February 2023. The aesthetic outcomes (and the subsequent impact on the patients' lives) were assessed using the Vancouver Scar Scale, Manchester Scar Scale, and Visual Analog Scale with scars ranked by three independent surgeon observers. Results: Patients who received reconstruction with local flaps demonstrated improved aesthetic and functional satisfaction, as well as improved aesthetic evaluation by independent surgeons. Conclusions: The use of local flaps permits a more pleasing reconstruction (functionally and aesthetically) of post-oncological tissue defects of the face.
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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 | 0.001 | 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".