Flow Diverter Performance in Aneurysms Arising From the Posterior Communicating Artery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Flow diverters (FDs) have demonstrated a safe and effective profile. However, the use of FDs for posterior communicating artery (PComA) aneurysms remains controversial. OBJECTIVE: To evaluate the safety and effectiveness of FDs for PComA aneurysms using a systematic review and meta-analysis of the literature. METHODS: We performed a systematic search from inception until June 2022 for flow diversion in PComA aneurysms. Primary effectiveness was the rate of complete aneurysm occlusion defined by Raymond-Roy class 1. Primary safety outcomes were treatment-related ischemic/hemorrhagic (composite) events causing morbidity and mortality. Secondary safety was PComA patency at follow-up. Random-effects meta-analyses were used to calculate proportions, and statistical heterogeneity was assessed. RESULTS: A total of 13 studies with 397 patients harboring 403 aneurysms were included. Mean age was 48 years, and the mean aneurysm size was 5.3 mm. Most aneurysms were unruptured (65%). Complete occlusion at final follow-up was 73% (CI 66%-79%), and adjunctive coils were used in 10% of aneurysms. Retreatment rate was 2% (CI 0%-9%). The primary safety composite outcome was 4% (CI 3%-7%), and mortality was 1%. PComA patency at final follow-up was 76% (CI 57%-89%). Subgroup analysis, patients with fetal PComAs had a lower complete occlusion rate (42% fetal PComA vs 77%, psubgroupdifference = <.01). CONCLUSION: The performance of FDs in PComA aneurysms is comparable with outcomes found in other subtypes of supraclinoid aneurysms. Effectiveness was acceptable and safety favorable. However, effectiveness was suboptimal in patients with fetal-type PComAs; alternative treatments should be considered in these cases.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".