Quantitative Anatomic Comparison of Microsurgical Transcranial, Endoscopic Endonasal, and Transorbital Approaches to the Spheno-Orbital Region
Bibliographic record
Abstract
BACKGROUND: The spheno-orbital region (SOR) is a complex anatomic area that can be accessed with different surgical approaches. OBJECTIVE: To quantitatively compare, in a preclinical setting, microsurgical transcranial approaches (MTAs), endoscopic endonasal transpterygoid approach (EEA), and endoscopic transorbital approaches (ETOAs) to the SOR. METHODS: These approaches were performed in 5 specimens: EEA, ETOAs (superior eyelid and inferolateral), anterolateral MTAs (supraorbital, minipterional, pterional, pterional-transzygomatic, and frontotemporal-orbitozygomatic), and lateral MTAs (subtemporal and subtemporal transzygomatic). All specimens underwent high-resolution computed tomography; an optic neuronavigation system with dedicated software was used to quantify working volume and exposed area for each approach. Mixed linear models with random intercepts were used for statistical analyses. RESULTS: Anterolateral MTAs offer a direct route to the greater wings (GWs) and lesser wings (LWs); only they guarantee exposure of the anterior clinoid. Lateral MTAs provide access to a large area corresponding to the GW, up to the superior orbital fissure (SOF) anteriorly and the foramen rotundum medially. ETOAs also access the GW, close to the lateral portion of SOF, but with a different angle of view as compared to lateral MTAs. Access to deep and medial structures, such as the lamina papyracea and the medial SOF, is offered only by EEA, which exposes the LW and GW only to a limited extent. CONCLUSION: This is the first study that offers a quantitative comparison of the most used approaches to SOR. A detailed knowledge of their advantages and limitations is paramount to choose the ideal one, or their combination, in the clinical setting.
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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 | 0.001 |
| 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".