The LN Flap for Nasal Defect Reconstruction: An 84-case Experience with a Novel Technique
Bibliographic record
Abstract
BACKGROUND: The nose is a common site for cutaneous malignancy and post-ablative reconstruction. To our knowledge, a myocutaneous island flap based on the levator labii superioris alaeque nasi (LLSAN) and nasalis muscles, with vascularity from the lateral nasal and angular arteries, has not been described for reconstruction of the lower lateral nose. METHODS: A retrospective chart review of patients who underwent LLSAN-nasalis island flap reconstruction between 2015 and 2019 was performed. The surgical technique involved marking of an inverted-V flap on the nasal sidewall. The skin lateral to the flap was then developed in the subcutaneous plane to expose the LLSAN muscle and divide its origin on the maxillary frontal process and its caudal insertions into the alar dermis. The medial incision was down to periosteum and perichondrium, and dissection beneath the flap separated it from the nasal support structure. The resultant flap had a great caudal mobility. The donor site was closed in a V-to-Y pattern. RESULTS IN TOTAL: (71.4%). The average age of patients was 74.9 years. An estimated 27 patients were lost to follow-up. At mean follow-up of 24.3 weeks, there were no cases of flap necrosis, 1 case of hematoma (1.8%), 1 case of infection (1.8%), 3 cases of persistent trapdoor deformity (5.3%), and 3 cases of alar notching (5.3%), 1 of whom required revision surgery. CONCLUSION: The LLSAN-nasalis myocutaneous island flap is a simple, reliable technique for resurfacing lateral lower nasal defects up to 2 × 2 cm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".