Neurovascular Sparing Transposition of Pterygopalatine Fossa: Anatomical Principles and Techniques
Bibliographic record
Abstract
OBJECTIVE: Sacrifice of pterygopalatine fossa (PPF) neurovascular structures during endoscopic endonasal transpterygoid approach (EETPA) may impact a patient's comorbidity. We present anatomical and surgical techniques for maximizing PPF transposition while preserving its neurovascular structures through orbito-pterygo-sphenoidal (OPS) ligament release and descending palatine canal (DPC) decompression. METHODS: The EETPA was performed on six specimens. Two measurements were obtained to assess PPF transposition: (1) Inferior transposition (distance between the superior margins of the base of the pterygoid process and PPF); (2) lateral transposition (distance from Eustachian tube lateral margin to PPF medial margin). RESULTS: After incising the OPS ligament, a mean gain of PPF inferior transposition of 7 mm (4-11 mm, p = 0.03) was observed, with a total inferior transposition of 12 mm (6-15 mm). Subsequently, the posterior half of the inferior turbinate was removed and the DPC was decompressed, with a mean gain of PPF lateral transposition of 12 mm (range 8-15 mm, p = 0.01). CONCLUSION: The OPS ligament release offers a significant advantage for the PPF inferior transposition, allowing access to the inferolateral recess of the sphenoid sinus, cavernous sinus, and paramedian middle cranial fossa, while the DPC decompression provides a significant advantage for the PPF lateral transposition, granting access to the Eustachian tube and ITF. LEVEL OF EVIDENCE: N/A.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".