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Record W7117729558 · doi:10.1177/27325016251403169

The Expanded Forehead Flap for Nasal Reconstruction: A Systematic Review of Postoperative Outcomes in East Asian Patients

2025· article· en· W7117729558 on OpenAlexaff
Ramez Michail, Yousef Sefau, Nii T. Obodai, Justin Paletz

Bibliographic record

VenueFACE · 2025
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsForeheadSystematic reviewRhinoplastyNoseInclusion and exclusion criteriaMEDLINE

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.306
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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