Total nasal reconstruction: use of a radial forearm free flap, titanium mesh, and a paramedian forehead flap.
Bibliographic record
Abstract
BACKGROUND: reconstruction of a total nasal defect presents a significant challenge to the reconstructive surgeon. The form, function, and aesthetic appeal of all the nasal subunits must be addressed. Classic teaching emphasizes the importance of restoring the internal lining of the nose, the rigid scaffolding, and the outer skin and soft tissue layer. METHODS: a restrospective review was undertaken in eight patients who had undergone total nasal reconstruction in two Canadian tertiary care centres. All eight patients had their nasal defect reconstructed with a radial forearm free flap for internal lining, titanium mesh for structural support, and a paramedian forehead flap for skin and soft tissue cover. Nasal function, graft survival, patient satisfaction, and complications were recorded. RESULTS: seven of eight patients were satisfied with the cosmetic outcome of their nasal reconstruction. Two patients reported poor nasal breathing owing to nasal stenosis. Two cases of minor titanium extrusion required operative intervention for repair. There were no cases of loss of the radial forearm free flap or paramedian forehead flap in this series. CONCLUSIONS: reconstruction with a radial forearm free flap, titanium mesh, and a paramedian forehead flap is a reliable, cosmetically appealing, and functional method for total nasal reconstruction. Minor surgical revisions should be anticipated to achieve the best cosmetic outcome. This is the first reported series using these three entities together to reconstruct total and subtotal rhinectomy defects.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".