Flow diverter stent for the treatment of ruptured distal anterior cerebral artery: A retrospective multicenter analysis from CRETA registry
Bibliographic record
Abstract
BackgroundData on off-label use of flow diverter for ruptured distal anterior cerebral artery aneurysms (rDACAAs) are limited. The purpose of the present study is to evaluate the efficacy and safety of flow diversion for rDACAAs in a large multicenter cohort.MethodsA retrospective observational study on consecutive patients who were treated with flow diversion for rDACAAs at 8 centers in 4 countries was performed. Primary outcome was the occlusion rate of the target aneurysm at the last radiological follow-up. Secondary outcomes included good clinical outcome, retreatment, technical success, procedure-related complications, radiological outcome of the covered branches and mortality rate.ResultsA total of 21 patients with 21 rDACAAs were treated between January 2017 and December 2024. Thirteen patients were women (61.9%) and the median age was 54 years (IQR 46-66). The most common etiology was saccular (71.4%), followed by dissecting (23.8%) and mycotic (4.8%). In all patients a single stent was successful deployed. Median imaging follow-up was 9 (7-12) months. At last follow-up adequate occlusion was 95.2%. Symptomatic thromboembolic or hemorrhagic complications occurred in 9.5% of patients. Seventeen patients (81%) had good clinical outcome (mRS 0-2) with mortality rate of 9.5%. In-stent stenosis occurred in one case that was conservatively managed without major concerns.ConclusionsFlow diversion is feasible as a potential treatment strategy for acutely ruptured aneurysms arising from distal anterior cerebral artery. Flow diverter may represent a valid option whenever other treatments are considered challenging or high risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".