C.07 Trigeminal neuralgia due to dolichoectatic vertebrobasilar artery compression: a 20 year experience
Bibliographic record
Abstract
Background: Trigeminal Neuralgia (TN) is rarely caused by a dolichoectatic vertebrobasilar artery (eVB) compression of the trigeminal nerve. These patients present a surgical challenge and are often not considered for microvascular decompression (MVD) due to assumed risk. We present our experience demonstrating the technique and outcomes of MVD in these patients. Methods: A retrospective chart review of patients who were surgically treated by the senior author between 1997 and 2016 with an admitting diagnosis of TN was performed. Patients with pre-operative neuroimaging demonstrating eVB compression of their trigeminal nerve root were included. Results: During the 20-year review, 552 patients underwent microvascular decompression for TN and 13 (2.4%) had dolichoectactic vertebrobasilar compressions (10 male, 3 female). The average hospital length of stay was 2.8 days (Range 2-7) with no major complications. At final follow-up (>2 years): 7 had no pain with no medications (78%), 2 had persistent pain (22%) – one of which underwent a successful glycerol rhizotomy at 8 months, 2 were lost to follow-up, and 2 had surgery within 2 years. Conclusions: Microvascular Decompression for Trigeminal Neuralgia caused by a dolichoectatic vertebrobasilar artery can be performed with a high rate of safety and success in the setting of a high case volume centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".