Total nasal reconstruction: use of a radial forearm free flap, titanium mesh, and a paramedian forehead flap.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it