Full Thickness Nasal Reconstruction With Paired Pericranial and Paramedian Forehead Flaps
Bibliographic record
Abstract
PURPOSE: The reconstruction of full-thickness nasal defects poses a significant challenge following oncologic resection. This study aims to share a technique using paired pericranial forehead flap (PCF) with contralateral paramedian flap (PMF) for such defects. Patient outcomes were reviewed, and the advantages and disadvantages of the reconstructive technique are discussed. METHODS: A retrospective review of a single surgeon practice was done between 2019 and 2024. Cases of nasal reconstruction with a paired PCF and PMF following oncologic resection were reviewed. Defect characteristics, reconstructive technique, and postoperative complications were evaluated. A literature review summarizing the evolution of this technique from inception to April 2024 was conducted using PubMed. RESULTS: The literature review identified 7 reports describing the use of a paired PCF and PMF for nasal reconstruction. The modifications and enhancements described in each study are summarized. The case series included 13 patients requiring oncologic resection for squamous cell carcinoma (8 patients) or basal cell carcinoma (5 patients). Every case required reconstruction of at least 2 nasal subunits, primarily involving the nasal tip, alae, and columella. Reconstruction was performed with the ipsilateral PCF, contralateral PMF, and structural grafts. Auricular cartilage grafts were universally used for structural support, with additional costal cartilage grafts and a split calvaria bone graft in select cases. The technique showed good functional and esthetic outcomes without any notable graft failures or donor site complications. CONCLUSIONS: The combination of an ipsilateral PCF and contralateral PMF is an effective strategy for reconstructing full-thickness nasal defects involving multiple nasal subunits.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".