Large and Deep Brain Arteriovenous Malformations Are Associated With Risk of Future Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The correlation between features present in brain arteriovenous malformations (AVMs) such as size, location, and angioarchitecture at presentation with subsequent risk of hemorrhage may be valuable in predicting the behavior of AVMs and therefore guiding management. METHODS: We prospectively followed up 390 patients with brain AVMs at the University of Toronto Vascular Malformation Study Group. Location, size, angioarchitecture details, blood supply, and clinical presentation were recorded at baseline. Intracranial hemorrhages during follow-up were recorded. Significant factors from univariate analyses were used to construct a multivariate model relating the above features to the occurrence of hemorrhage. RESULTS: Thirty-eight patients had bleeding caused by the AVM in a follow-up of 1205 patient-years (mean, 3.1 years per patient). In analyses adjusted for multiple AVM characteristics, large AVMs bled more frequently than small lesions (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.41 to 4.35; P<0.0001), and deep-seated AVMs had more bleeding in follow-up than those located at superficial sites (OR, 5.56; 95% CI, 2.63 to 12.5; P<0.0001). CONCLUSIONS: Deep-seated and large AVMs were significantly more prone to hemorrhage during prospective follow-up. The distinction between factors associated with hemorrhagic presentation and the natural history risk of hemorrhage will be emphasized.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".