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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".