Flow diversion in vasculitic intracranial aneurysms? Repair of giant complex cavernous carotid aneurysm in polyarteritis nodosa using Pipeline embolization devices: first reported case
Bibliographic record
Abstract
Intracranial aneurysms in polyarteritis nodosa (PAN) are exceedingly rare lesions with unpredictable behavior that pose real challenges to microsurgical and endovascular interventions owing to their inflammatory nature. We introduce a safe and effective alternative for treating these aneurysms using Pipeline embolization devices (PEDs). A 20-year-old man presented with diplopia, headaches, chronic abdominal pain, and weight loss. Diagnostic evaluations confirmed PAN, including bilateral giant cavernous carotid aneurysms. Cyclophosphamide and steroids achieved significant and sustained clinical improvement, with a decision to follow the aneurysms serially. Seven years later the left unruptured aneurysm enlarged, causing a sudden severe headache and a cavernous sinus syndrome. Treatment of the symptomatic aneurysm was pursued using flow diversion (PED) and the internal carotid artery was successfully reconstructed with a total of four overlapping PEDs. At 6 months follow-up, complete exclusion of the aneurysm was demonstrated, with symptomatic recovery. This is the first description of using a flow-diverting technique in an inflammatory vasculitis. In this case, PEDs not only attained a definitive closure of the aneurysm but also reconstructed the damaged and fragile arterial segment affected with vasculitis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".