Flow diversion for distal cerebral aneurysms: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Flow diverters (FDs) are increasingly used off-label for distal cerebral aneurysms (DCAs), those located at or beyond the anterior cerebral artery (ACA) A2, middle cerebral artery M2, and posterior cerebral artery P2 segments. However, data on their safety and efficacy remain limited. This systematic review and meta-analysis synthesizes current evidence on FD treatment for DCAs. METHODS: A comprehensive search was performed across PubMed, Scopus, and Web of Science, including studies with ≥5 patients reporting FD treatment of DCAs. Efficacy outcomes included adequate and complete aneurysm occlusion and retreatment rates. Safety outcomes included good functional outcome (modified Rankin Scale (mRS) score 0-2), procedure-related morbidity, mortality, complications, in-stent stenosis, and covered branch occlusion at follow-up. Pooled analyses with 95% confidence intervals (CI) were conducted using a random-effects model. RESULTS: Eighteen studies (441 patients, 70% women; mean age: 57.2±13.6 years) with 445 DCAs were included. Most aneurysms were unruptured (93.7%) and located in the ACA territory (78.4%). Adequate and complete occlusion at last follow-up were 90% (95% CI, 86 to 95) and 79% (95% CI, 74 to 85), respectively. The retreatment rate was 1.6% (95% CI, 0.2 to 3). Good functional outcome was achieved in 97% (95% CI, 95 to 99). Procedure-related complications occurred in 9% (95% CI, 5 to 13), with morbidity and mortality of 1.5% and 0.6%, respectively. In-stent stenosis and covered branch occlusion rates were 3% (95% CI, 0 to 8) and 5.5% (95% CI, 2 to 9), respectively. CONCLUSION: FDs appear to be safe and effective for DCAs, with high occlusion rates and low retreatment and complication rates.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".