Use of the Nasal Sidewall Island Inversion Flap for Single-Stage Ala Nasi Reconstruction
Bibliographic record
Abstract
BACKGROUND: Alar reconstruction can be challenging, and numerous solutions for reconstructing this potentially difficult area have been proposed. The authors' preferred solution is an island inversion flap based on the superior alar artery. METHODS: The authors performed a retrospective review of the medical notes and photographs of 103 consecutive patients who underwent alar reconstruction with the nasal sidewall island inversion flap. Patient demographics and complications were recorded. When they were available, postoperative photographs (>2 months postoperatively) were rated by three plastic surgeons using a Likert scale together with a rating guide to determine the aesthetic outcome. RESULTS: Between 1998 and 2012, the authors performed 103 island inversion flaps. Mean patient age was 59 years (range, 23 to 85 years). Eighteen (17 percent) of 103 patients had defects extending beyond the ala. There were no flap losses and seven cases of superficial epidermolysis. Revision surgery was performed in three cases. In 48 of 103 cases, postoperative photographs (>2 months postoperatively) were available for aesthetic assessment. The aesthetic results were rated as "good" to "excellent" in more than 70 percent of cases (range, 73 to 85 percent) by the three plastic surgeons. CONCLUSIONS: The nasal sidewall island inversion flap is a reliable and versatile flap for alar reconstruction. It can consistently produce good to excellent aesthetic results in a single-stage operation. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 |
| 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) | 0.003 | 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".