Safety and Efficacy of Flow Diversion for Intracranial Aneurysms in Small Parent Vessels: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Intracranial aneurysms arising from small parent vessels (<2 mm) are challenging due to their fragile anatomy and distal locations. Flow diversion (FD) offers a promising treatment, but its safety and efficacy in small vessels remain underexplored. Objective: To evaluate angiographic occlusion rates and procedure-related morbidity associated with flow diversion for intracranial aneurysms arising from parent vessels <2 mm in diameter. Methods: A retrospective cohort study was conducted at Bicêtre Hospital, Paris, France, from January 2018 to December 2023, analyzing 50 patients undergoing 56 procedures for 55 aneurysms. FD devices (e.g., Silk Vista Baby, Pipeline Flex) were used. Follow-up at 6, 18, and 42 months assessed occlusion via digital subtraction angiography (DSA) using Raymond-Roy and O’Kelly-Marotta scales. Results: Complete occlusion was achieved in 70.9% (39/55) of aneurysms at a mean follow-up of 17.86 months. At 6, 18, and 42 months, occlusion rates were 56.4%, 55.6%, and 76.5%, respectively. Intraprocedural complications occurred in 28.6% (16/56) of procedures, with 19.6% (11/56) due to in-stent thrombosis. Symptomatic major complications were observed in 17.9% (10/56), including ischemic events and hemorrhage, and permanent FD-related morbidity was 8.0% (4/50). Conclusion: FD is effective for small vessel aneurysms, with high occlusion rates, but significant complications highlight the need for careful patient selection and refined techniques. Larger studies are needed to optimize outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".