Flow Diverter Treatment Using a Flow Re-Direction Endoluminal Device for Unruptured Intracranial Vertebral Artery Dissecting Aneurysm: Single-Center Case Series and Technical Considerations
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the effectiveness, safety, and technical considerations of flow diverter (FD) treatment using a Flow Re-direction Endoluminal Device (FRED) for unruptured intracranial vertebral artery dissecting aneurysms (VADAs). MATERIALS AND METHODS: We conducted a retrospective study of 23 patients with unruptured intracranial VADAs who underwent FD treatment using a FRED between June 2017 and August 2021. Symptoms, imaging findings, treatment strategies, and angiographic and clinical outcomes were evaluated. Dissections were categorized according to the dominance of the VA in which they occurred: dominant VA, co-dominant VA, and non-dominant VA. RESULTS: All patients successfully underwent FD treatment with either a FRED (n=11) or FRED Jr. (n=12). Complete occlusion rates were 78.3% at 6-month follow-up magnetic resonance angiography and 91.3% at 12-month. There were no instances of complications, recurrence, or retreatment during a median follow-up of 20 months. Dissections occurred in the dominant VA in 3 cases (13.0%), the co-dominant VA in 13 cases (56.5%), and the non-dominant VA in 7 cases (30.4%). Intimal flap and true lumen stenosis were observed in 39.1% and 30.4% of cases, respectively. Four cases required a bilateral VA approach due to technical difficulties, all in the non-dominant VA. CONCLUSION: Flow diversion treatment using a FRED for unruptured intracranial VADAs proved feasible and safe, yielding satisfactory occlusion rates. Technical challenges were more likely in lesions involving non-dominant VAs in the acute or subacute stage, mainly due to associated intraluminal lesions compromising the arterial lumen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".