The Natural History and Predictive Features of Hemorrhage From Brain Arteriovenous Malformations
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients harboring brain arteriovenous malformations (bAVMs) are at a lifelong risk for hemorrhagic strokes, but the natural history is poorly understood. We examined the impact of demographic and angiographic features on the likelihood of future hemorrhage. METHODS: A prospectively accrued database of bAVM patients maintained at the Toronto Western Hospital was analyzed; 678 consecutive, prospectively enrolled bAVM patients were followed for 1931.7 patient-years. The rate of hemorrhage over long-term follow-up was recorded. The impact of baseline clinical and radiographic features and partial treatment on time to hemorrhage were analyzed using survival analysis. Neurological outcome after hemorrhage was assessed using the Glasgow Outcome Score. RESULTS: Hemorrhage rates were 4.61% per year for the entire cohort (n=678), 7.48% per year for bAVMs with initial hemorrhagic presentation (n=258), 4.16% per year for initial seizure presentation (n=260), 3.99% per year for patients not harboring aneurysms (n=556), 6.93% per year for patients with associated aneurysms (n=122), and 5.42% per year for bAVMs with deep venous drainage (n=365). Hemorrhagic presentation was a significant independent predictor of future hemorrhage (HR, 2.15; P<0.01), whereas associated aneurysms (HR, 1.59; P=0.07) and deep venous drainage (HR, 1.59; P=0.07) showed a trend toward significance. Hemorrhage risk was unchanged in patients who underwent partial arteriovenous malformation embolization (n=211; HR, 0.875; P=0.32). CONCLUSIONS: Brain arteriovenous malformations presenting with hemorrhage, with deep venous drainage, or associated aneurysms have approximately 2-fold greater likelihood of a future hemorrhage. Partial treatment by embolization does not alter these risks. This natural history should be taken into account in the treatment strategy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".