Treatment of anterior cerebral artery and anterior communicating artery aneurysms with flow-diversion devices: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: To assess the early safety and efficacy of anterior cerebral artery (ACA) and anterior communicating artery (ACoA) aneurysm treatment with flow-diversion devices (FDDs), we performed a systematic review and meta-analysis for these aneurysms. EVIDENCE ACQUISITION: A literature search was performed by a reference librarian, and, after screening, eight case series were included for meta-analysis. We estimated from each study the cumulative incidence (event rate) and 95% confidence interval (CI) for each outcome. Event rates were pooled in a meta-analysis across studies using the random-effects model; descriptive statistics were reported when relevant. EVIDENCE SYNTHESIS: 129 ACA and ACoA aneurysms from 8 series were included. Technical success rate of 96% (95% CI: 0.93 to 1.00) and a technical complication rate was 3% (95% CI: 0.00 to 0.06). Perioperative rates of ischemic stroke, hemorrhagic stroke, morbidity, and mortality were 3% (95% CI: 0.00 to 0.06), 5% (95% CI: 0.01 to 0.08), 3% (95% CI: 0.00 to 0.06 and 2% (95% CI: 0.00 to 0.05), respectively. The rate of treatment-related, long-term neurological deficit was 4% (95% CI: 0.01 to 0.07). Complete occlusion rate at last radiological follow-up was 79% (95% CI: 0.68 to 0.91). CONCLUSIONS: FDDs are an acceptable tool for the treatment of ACA and ACoA aneurysms with high rates of technical success and low rates of periprocedural morbidity and mortality. Comparative studies with longer-term follow-up are needed to clarify the role of these devices in the management of ACA and ACoA aneurysms in patients with challenging comorbidities.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".