Medial Brow Restoration Following Paramedian Forehead Flap Reconstruction: A Surgical Technique
Bibliographic record
Abstract
OBJECTIVE: The paramedian forehead flap is a versatile and widely used reconstruction technique for nasal defects. Although it can provide excellent aesthetic outcomes, the medial brow can be disrupted, leading to facial asymmetry and patient dissatisfaction. We describe a simple and reliable surgical technique for medial eyebrow reintegration performed at the time of forehead flap pedicle division. METHODS: Between 2015 and 2024, 146 patients underwent medial eyebrow reconstruction using this technique at the time of their pedicle division. During the second stage of the forehead flap during pedicle division, a standardized technique using a semicircular incision and flap inset was employed to reposition the medial eyebrow into continuity with the remaining brow. Patient outcomes were assessed through follow-up visits, and aesthetic outcomes were reviewed descriptively. RESULTS: One hundred forty-six patients underwent eyebrow reconstruction using this method at the time of the division of their forehead flap. All patients achieved natural-appearing medial-to-lateral brow continuity with high levels of satisfaction. Minor revisions were required in a limited number of cases including sensitive neuroma, and contour deformities. CONCLUSIONS: This reconstruction technique allows for reliable aesthetic restoration of the medial eyebrow after forehead flap reconstruction. It can be easily incorporated into standard practice during pedicle division and improves symmetry and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".