Resurfacing of the nasopharynx after nasopharyngectomy using a free radial forearm flap
Bibliographic record
Abstract
BACKGROUND: Nasopharyngectomy is emerging as an important treatment option for salvaging locally recurrent nasopharyngeal carcinoma (NPC). After nasopharyngectomy, resurfacing the nasopharynx and covering the internal carotid artery is important to minimize the risk of infection, osteoradionecrosis, and carotid rupture. Previous authors have advocated the use of free grafts of skin and mucosa for this purpose but have also described significant rates of partial and total graft failure. METHODS: We believe that the best and most reliable way to resurface the nasopharynx is with vascularized tissue, and our preference is for the use of a free radial forearm flap. To illustrate our approach, we present two patients who underwent nasopharyngectomy by means of a maxillary swing approach and who had resurfacing of the surgical defect with a free radial forearm flap. RESULTS: Both patients had complete en bloc resection of tumor followed by the insetting of a free radial forearm flap to reline the surgical defect. Both flaps remained completely viable, and both patients achieved successful resurfacing of the entire nasopharynx. The morbidity of surgery was minimal, and there were no perioperative complications. On assessment 1 year later, the free radial forearm flap continues to reline the entire neonasopharynx, and the long-term functional recovery after surgery is excellent. CONCLUSION: Resurfacing the nasopharynx after nasopharyngectomy with a free radial forearm flap aids healing and minimizes the risk of complications. The morbidity of surgery is minimal and the functional recovery is excellent.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".