Stepwise, Multi-Incisional, and Single-Stage Approach to Reshape Facial Contour After Large Cutaneous Lesion Resection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it