E.4 Safety and outcomes of middle meningeal artery embolization for pseudoaneurysms and aneurysms: a systematic review
Bibliographic record
Abstract
Background: Middle meningeal artery embolization (MMAE) is increasingly used to treat chronic subdural hematomas, arteriovenous fistulas and meningiomas. Less commonly, MMAE is performed for pseudoaneurysms and aneurysms. While procedural safety and efficacy in the context of the former diseases is well-documented, data for MMA aneurysm/pseudoaneurysm treatment are scarce. Methods: We conducted a systematic review using PubMed/Medline and GoogleScholar, targeting studies published in English since 1994. Original research studies and case reports involving adult patients (≥18 years) with aneurysms or pseudo-aneurysms treated with MMAE were included. Data on complications, outcomes, procedural techniques, and embolization materials were analyzed using descriptive statistics. Results: Of 1,690 identified studies, 600 underwent full-text review, and 27 studies/case reports focusing on MMAE for pseudoaneurysms and aneurysms were included in the final analysis. In most cases, the treatment was successful, with complete (pseudo-)aneurysm occlusion in all patients and symptom improvement in 24 of 28 patients (85.7%). Complications were rare, occurring in <5%, and mild, such as transient headaches (n=1) which resolved spontaneously. Conclusions: MMAE appears to be a safe and effective treatment for pseudoaneurysms and aneurysms, with minimal complications and high success rates. However, available data are scarce and from case reports only, limiting generalizability. Confirmation in larger, multi-center studies is needed.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.006 | 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".