Management of Adult Unruptured Brain Arteriovenous Malformations: An Updated Network Meta-Analysis
Bibliographic record
Abstract
The management of unruptured brain arteriovenous malformations (ubAVMs) is a complex challenge to neurovascular practitioners. This meta-analysis aimed to identify the optimal management of ubAVMs comparing conservative management, embolization, radiosurgery, microsurgical resection, and multimodality. The search strategy was developed a priori according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched the Ovid Medline, Embase, Web of Science, and Cochrane Library databases to identify relevant papers. Using R version 4.1.1., a frequentist network meta-analysis was conducted to compare different management modalities for the ubAVMs. Overall, the conservative group had the lowest risk of rupture (P-score=0.77), and the lowest rate of complications was found in the conservative group (P-score=1). Among different interventions, the multimodality group had the highest rupture risk (P-score=0.34), the lowest overall complications (P-score=0.75), the best functional improvement (P-score=0.65), and the lowest overall mortality (P-score=0.8). However, multimodality treatment showed a significantly higher risk of rupture (odds ratio [OR]=2.13; 95% confidence interval [95% CI]=1.18-3.86) and overall complication rate (OR=5.56; 95% CI=3.37-9.15) compared to conservative management; nevertheless, there were no significant differences in overall mortality or functional independence when considered independently. Conservative management is associated with the lowest rupture risk and complication rate overall. A multimodal approach is the best option when considering mortality rates and functional improvement in the context of existing morbidity/symptoms. Microsurgery, embolization, and radiosurgery alone are similar to the natural history in terms of functional improvement and mortality, but have higher 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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".