Use of a flow-diverting stent for ruptured dissecting aneurysm treatment in a patient with sickle cell disease
Bibliographic record
Abstract
INTRODUCTION: Sickle cell disease (SCD) is a clinical phenotype that presents a unique challenge to the interventionalist, particularly when stent therapy is contemplated. Homozygous individuals are likely at increased risk for thromboembolic complications. There are no formal guidelines regarding antiplatelet therapy in the short or long term for intracranial stent use in SCD. The authors describe the novel use of a pipeline embolization device (PED) to treat a ruptured dissecting bilobed/fusiform vertebral artery V4 aneurysm in an SCD patient complicated by tortuous proximal anatomy and the anterior spinal artery arising from the diseased segment. Considerations regarding antiplatelet therapy in this scenario are discussed. CASE REPORT: A 50-year-old woman with homozygous recessive SCD was transported to the emergency department and presented with diffuse subarachnoid hemorrhage. CT angiography demonstrated a left-sided 3 × 5 mm fusiform bi-lobulated presumed dissecting vertebral artery aneurysm, immediately distal to the origin of the posterior inferior cerebellar artery (PICA). A PED was deployed within the V4 segment across the aneurysm. Post-treatment angiography showed patency of the parent artery, and patency of the "jailed" anterior spinal artery and of the PICA. DISCUSSION: Selecting a treatment method in SCD patients with a ruptured intracranial aneurysm is challenging and there are no clinical trials comparing treatment methods in this population. The authors demonstrate that flow diversion is feasible in SCD, which has not been described in the literature. Additionally, the case stresses the peri- and post-procedural management of SCD, as well as long-term considerations with a flow-diverting stent in place.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".