The lateral retrocanthal transorbital endoscopic approach to the middle fossa: cadaveric stepwise approach and review of quantitative cadaveric data
Bibliographic record
Abstract
OBJECTIVE: The lateral retrocanthal transorbital endoscopic approach (LRCTEA) facilitates trajectory to the middle fossa, preserving the lateral canthal tendon and thus avoiding postoperative complications such as eyelid malposition. Here, the authors sought to define the surgical anatomy and technique of LRCTEA using a stepwise approach in cadaveric heads and offer an in-depth examination of existing quantitative data from cadaveric studies. METHODS: The authors performed LRCTEA to the middle cranial fossa under neuronavigation in 7 cadaveric head specimens that underwent high-resolution (1-mm) CT scans preceding the dissections. RESULTS: The LRCTEA provided access to middle fossa regions including the cavernous sinus, Meckel's cave, and medial temporal lobe. The trajectories and endpoints of the approach were confirmed using electromagnetic neuronavigation. A stepwise approach was delineated and recorded. CONCLUSIONS: The authors' cadaveric study delineates the surgical anatomy and technique of the LRCTEA, providing a stepwise approach for its implementation. As these approaches continue to evolve, their development and refinement will play an important role in expanding the surgical options available to neurosurgeons, ultimately improving outcomes for patients with complex skull base pathologies. The LRCTEA presents a promising advancement in skull base surgery, particularly for accessing challenging middle fossa regions. However, surgeons must remain vigilant to potential complications, including transient diplopia, orbital hematoma, or damage to the optic apparatus.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".