Facial Nerve Arterial Arcade Supply in Dural Arteriovenous Fistulas: Anatomy and Treatment Strategies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Endovascular treatment of petrous dural AVFs may carry a risk of iatrogenic facial nerve palsy if the facial nerve arterial arcade, an anastomotic arterial arch that supplies the geniculate ganglion, is not respected or recognized. Our purpose was to demonstrate that the use of a treatment strategy algorithm incorporating detailed angiographic anatomic assessment allows identification of the facial nerve arterial arcade and therefore safe endovascular treatment. MATERIALS AND METHODS: This was a retrospective cohort study of consecutive petrous dural AVF cases managed at Toronto Western Hospital between 2006 and 2018. Our standard of care consists of detailed angiographic assessment followed by multidisciplinary discussion on management. Arterial supply, primary and secondary treatments undertaken, angiographic outcomes, and clinical outcomes were assessed by 2 independent fellowship-trained interventional neuroradiologists. RESULTS: Fifteen patients had 15 fistulas localized over the petrous temporal bone. Fistulas in all 15 patients had direct cortical venous drainage and received at least partial supply from the facial nerve arterial arcade. Following multidisciplinary evaluation, treatment was performed by endovascular embolization in 8 patients (53%) and microsurgical disconnection in 7 patients (47%). All patients had long-term angiographic cure, and none developed iatrogenic facial nerve palsy. CONCLUSIONS: By means of our treatment strategy based on detailed angiographic assessment and multidisciplinary discussion, approximately half of our patients with petrous AVFs were cured by endovascular treatment, half were cured by an operation, and all had preserved facial nerve function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".