Stepwise, Multi-Incisional, and Single-Stage Approach to Reshape Facial Contour After Large Cutaneous Lesion Resection
Bibliographic record
Abstract
BACKGROUND: Removal of large facial benign cutaneous lesions remains challenging. Serial or complete excisions together with local flaps or expander-based reconstructions are required. However, those techniques are time-consuming and may contribute to poor cosmetic and functional outcomes. OBJECTIVE: The authors describe the resection and reconstruction of large facial benign cutaneous lesions by using Stepwise, Multi-Incisional, and Single-Stage (SMISS) approach. METHODS: The authors performed a retrospective review from all patients with large facial benign cutaneous lesions who underwent "SMISS" approach for reconstruction between September 2013 and December 2014. RESULTS: The authors treated 47 patients (32 female and 15 male; mean age 23.5 years, range 9-50 years). Follow-up was for 12 months or longer. The mean length of major axis was 43.91 mm, minor axis 32.10 mm, and scar 66.91 mm. Good to excellent outcomes were achieved in all patients with a mean Vancouver scar scale score of 3.46 ± 0.39 (Cronbach α = 0.890) and mean visual analog scale score of 8.02 ± 0.69 (Cronbach α = 0.946). LIMITATIONS: This was a nonrandomized, unblinded clinical case series with a limited sample size. CONCLUSION: For the excision and reconstruction of large facial benign cutaneous lesions, "SMISS" technique can be considered as a suitable option, leading to excellent results and a high patient satisfaction.
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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 | 0.001 |
| 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) | 0.002 | 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".