Endoscopic endonasal transpterygoid approaches: Anatomical landmarks for planning the surgical corridor
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Endoscopic endonasal transpterygoid approaches (EETA) use the pneumatization of the sinonasal corridor to control lesions of the middle and posterior skull base. These surgical areas are complex and the required surgical corridors are difficult to predict. AIM: Define anatomical landmarks for the preoperative planning of EETAs. STUDY DESIGN: Anatomical study. METHODS: We reviewed images from high-resolution maxillofacial CT scans with (0.6-mm axial slice acquisition). Cephalometric measurements were obtained using Kodak Carestream Image Software (Rochester, NY). RESULTS: Average distance from midline to the vidian canal was 12.78 mm (range 9.4-15.8 mm). Average horizontal distance from the vidian canal to the foramen rotundum was 5.6 mm (range 2.8-11.5 mm). Average vertical distance from the vidian canal to the foramen rotundum was 6.22 mm (range 4.3-9.3 mm). These landmarks are consequential during the preoperative planning of the surgical corridor. To facilitate communication, we classified EETAs as: A) Partial removal of the pterygoid plates (transposition of temporo-parietal fascia); B) removal of anteromedial aspect of the pterygoid process (lesions involving the lateral recess of the sphenoid sinus); C) involves dissecting the vidian nerve to control the petrous ICA and removing the pterygoid plates base to reach the petrous apex, Meckel's cave, or cavernous sinus; D) variable removal of the pterygoid plates to access the infratemporal fossa; and E) removal of pterygoid process and medial third of the Eustachian tube to expose the nasopharynx. CONCLUSIONS: Our novel classification and landmarks system helps to understand the anatomy of this complex area and to accurately plan the EETA.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".