Locoregional flaps versus skin grafts in the nose: aesthetic considerations after cancer ablation
Bibliographic record
Abstract
Background The annual incidence of skin cancer has been increasing, and surgical ablation is presently the treatment of choice for skin cancer. However, it leaves soft tissue defects that require reconstruction. The methods for reconstruction include locoregional flaps (LRFs) and full-thickness skin grafts (FTSGs). We compared these two surgical methods for reconstruction of defects in the nose, which is prominently visible and the most common site of facial skin cancer, and assessed the cosmetic results by evaluating the scars.Methods This retrospective study was conducted between July 2012 and January 2021. Patients were evaluated for scars after at least 6 months of follow-up. Patients were divided into LRF and FTSG groups. The scars were evaluated using the Vancouver Scar Scale.Results In total, 27 patients were included in this study. Their mean age was 66.8 years. Eighteen patients underwent LRF, and nine patients underwent FTSG. The average defect size was 1.55 cm² in the LRF group, and 1.38 cm² in the FTSG group. The average scar score was 1.44 points in the LRF group and 3.67 points in the FTSG group. The LRF group showed significantly lower total scores than the FTSG group.Conclusions Although LRFs and FTSGs are useful reconstructive methods for nasal soft tissue defects, this study showed that LRFs are superior to FTSGs in terms of aesthetic results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".