Radiologic Assessment of Brain Arteriovenous Malformations: What Clinicians Need to Know
Bibliographic record
Abstract
Brain arteriovenous malformations (AVMs) are abnormal vascular connections within the brain that are presumably congenital in nature. There are several subgroups, the most common being glomerular type brain AVMs, with fistulous type AVMs being less common. A brain AVM may also be a part of more extensive disease (eg, cerebrofacial arteriovenous metameric syndrome). When intracranial pathologic vessels are encountered at cross-sectional imaging, other diagnoses must also be considered, including large developmental venous anomalies, malignant dural arteriovenous fistulas, and moyamoya disease, since these entities are known to have different natural histories and require different treatment options. Several imaging findings in brain AVMs have an impact on decision making with respect to clinical management; the most important are those known to be associated with risk of future hemorrhage, including evidence of previous hemorrhage, intranidal aneurysms, venous stenosis, deep venous drainage, and deep location of the nidus. Other imaging findings that should be included in the radiology report are secondary effects caused by brain AVMs that may lead to nonhemorrhagic neurologic deficits, such as venous congestion, gliosis, hydrocephalus, or arterial steal.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".