Endovascular Reconstruction Utilizing Flow Diversion Stenting in a Patient With Bilateral Giant Cavernous Internal Carotid Artery Aneurysms
Bibliographic record
Abstract
Bilateral giant cerebral aneurysms are exceedingly rare. Giant aneurysms of the internal carotid artery (ICA) carry a poor prognosis if untreated. Flow diversion is an endovascular technique whereby a device is placed in the parent blood vessel to divert blood flow away from the aneurysm and is an available treatment for giant aneurysms. A 69-year-old woman presented with progressive diplopia and was found to have bilateral ICA aneurysms. She had stenting of the left ICA aneurysm with improvement of her symptoms and no complications. Five years post procedure, she presented with recurrent diplopia and was found to have enlargement of the previously seen right-sided cavernous ICA aneurysm, which was treated with another flow diversion stent with no complications. Endoluminal reconstruction/flow diversion with Pipeline™ Embolization Device (PED) has emerged as an alternative to traditional endosaccular coiling and parent artery occlusion. We report a case of bilateral cavernous carotid giant aneurysms treated with flow diversion and demonstrate that flow diversion stenting using the PED is a safe and reliable treatment for bilateral giant ICA aneurysms. We encourage interventionists to consider this technique in patients with giant intracranial aneurysms. J Neurol Res. 2020;10(4):136-139 doi: https://doi.org/10.14740/jnr593
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".