Safety and Efficacy of Flow Diverters for Treatment of Unruptured Anterior Communicating Artery Aneurysms: Retrospective Multicenter Study
Bibliographic record
Abstract
BACKGROUND: The anterior communicating artery (AComm) region is the most common site of intracranial aneurysms, with increased rupture risk compared with other locations. Overall, flow diverters present as a safe and efficacious treatment for intracranial aneurysms, but there is paucity of data for their use in the treatment of unruptured AComm aneurysms. We present the largest multicentric analysis evaluating the outcomes of flow diverters in AComm aneurysm treatment. METHODS: Databases from 10 centers were retrospectively reviewed for unruptured AComm aneurysms treated with flow diverters. Demographics, clinical presentation, radiographic characteristics, procedural complications, and outcomes were assessed. RESULTS: A total of 144 patients harboring 147 AComm aneurysms were treated between January 2012 and December 2021. Seventy-four were women (51.4%) and median age was 60 (IQR 50-67) years. All were unruptured AComm aneurysms. Half of the cohort had similar anterior cerebral artery sizes (51.4%). The most common morphology was saccular (94.6%), with a branch involvement in 32.7% of cases. Median vessel diameter was 2.4 mm, and the Pipeline Flex was the most prevalent device (32.7%). Median follow-up time was 17 months, with complete occlusion in 86.4% at the last follow-up. Functional independence (modified Rankin Scale score 0-2) was reported in 95.1%. Intraprocedural complications occurred in 5.6%, and postoperative complications were noted in 9.7% of cases. Combined major complication and mortality rate was 2.1%. CONCLUSIONS: Our study suggests that flow diverters are a useful treatment for AComm aneurysms. Mid-term results indicated favorable aneurysm occlusion with a good safety profile. Additional prospective studies with longer follow-up periods and independent adjudication are warranted to better assess these results.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".