Most Promising Approaches to Improve Brain AVM Management: ARISE I Consensus Recommendations
Bibliographic record
Abstract
Brain arteriovenous malformations (bAVMs) are complex, and rare arteriovenous shunts that present with a wide range of signs and symptoms, with intracerebral hemorrhage being the most severe. Despite prior societal position statements, there is no consensus on the management of these lesions. ARISE (Aneurysm/bAVM/cSDH Roundtable Discussion With Industry and Stroke Experts) was convened to discuss evidence-based approaches and enhance our understanding of these complex lesions. ARISE identified the need to develop scales to predict the risk of rupture of bAVMs, and the use of common data elements to perform prospective registries and clinical studies. Additionally, the group underscored the need for comprehensive patient management with specialized centers with expertise in cranial and spinal microsurgery, neurological endovascular surgery, and stereotactic radiosurgery. The collection of prospective multicenter data and gross specimens was deemed essential for improving bAVM characterization, genetic evaluation, and phenotyping. Finally, bAVMs should be managed within a multidisciplinary framework, with clinical studies and research conducted collaboratively across multiple centers, harnessing the collective expertise and centralization of resources.
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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 | 0.033 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".