The Expanded Forehead Flap for Nasal Reconstruction: A Systematic Review of Postoperative Outcomes in East Asian Patients
Bibliographic record
Abstract
Introduction: The purpose of this systematic review is to assess postoperative outcomes of the expanded forehead flap (EFF) for nasal reconstruction in East Asian patients, focussing on both functional restoration and aesthetic satisfaction in a population with unique anatomical considerations. Methods: A systematic review was performed using PubMed, MEDLINE, Embase, and Scopus databases by 4 independent reviewers. Articles were included if they focussed on the use of an expanded forehead flap for nasal reconstruction in East Asian patients, with outcomes related to complications, aesthetic satisfaction, and functional results. Studies published in English between 1995 and 2025 were considered. Results: The literature search yielded 68 initial articles. After duplicates of articles were removed, 50 articles underwent title review. Thirty-six articles underwent screening, and 32 articles were approved for full-text review. Twelve studies were deemed appropriate for inclusion in the systematic review. Conclusion: The expanded forehead flap is an effective and reliable procedure for nasal reconstruction in East Asian populations, offering both functional and aesthetic improvements. However, complications such as colour mismatch, hypertrophic scarring, and flap vitality issues must be carefully managed. Further studies with standardized outcome measures can continue to refine surgical techniques and minimize complications, ensuring better long-term 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".