Endoscopic transpterygoid nasopharyngectomy: Correlation of surgical anatomy with multiplanar CT
Bibliographic record
Abstract
BACKGROUND: Oncologic resection of the nasopharynx is challenging due to its complex and deep-seated nature. We aimed to illustrate the anatomic landmarks of endoscopic nasopharyngectomy and design a surgical training model that could facilitate learning of this technique. METHODS: An endoscopic endonasal dissection of the nasopharynx was completed in fresh cadaveric specimens under conditions similar to those of our operating suite. Digital data from a high-resolution CT scan were imported to an image guidance system to be used during the dissections. RESULTS: We expanded the sinonasal corridor, harvested a contralateral nasoseptal flap, and exposed the pterygopalatine and infratemporal fossae. A detailed anatomic dissection of the nasopharynx was correlated to multiplanar images provided by the image guidance system, highlighting appropriate bony, neural, and vascular landmarks. CONCLUSIONS: Understanding the anatomy-based endoscopic modular approaches facilitates planning and safe execution of an oncologic nasopharyngectomy. Clinical experience remains mandatory because anatomic models fall short of clinical scenarios.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".