Endoscopically-assisted transmastoid approach to the geniculate ganglion and labyrinthine facial nerve
Bibliographic record
Abstract
BACKGROUND: Endoscopic transcanal approaches to the facial nerve allow excellent exposure of the tympanic facial nerve. This approach becomes limited when access is required to the more proximal geniculate ganglion and labyrinthine portion of the facial nerve. The aim of this report was to determine the feasibility of a transmastoid endoscopically assisted approach to the geniculate ganglion and labyrinthine facial nerve. This is an endoscopic cadaveric dissection and video review at a university anatomical laboratory. METHODS: A total of 12 endoscopic cadaveric dissections were performed. A cortical mastoidectomy and perilabyrinthine air cell removal was performed using an operating microscope. Beyond this, dissection was performed with an endoscope. RESULTS: In all dissections, an endoscopically assisted transmastoid approach allowed complete access to the geniculate ganglion, and at least 1.5 mm of the distal labyrinthine facial nerve. Further transcrusal drilling through the anterior crus of the superior semicircular canal allowed access to the entire labyrinthine facial nerve. CONCLUSIONS: The entire geniculate ganglion and labyrinthine facial nerve is difficult to access with microscopic techniques. Adding endoscopic visualization allows for complete visualization of the geniculate ganglion. Clinical reports will further strengthen these preliminary cadaveric results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".