Angioarchitectural Factors Present in Brain Arteriovenous Malformations Associated With Hemorrhagic Presentation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Associations between clinical presentation of brain arteriovenous malformations (AVMs) and their angioarchitecture have been described. This study aims to identify significant factors related to the initial hemorrhagic event through multivariate statistical methodology. METHODS: The authors studied the initial clinical presentation of 390 consecutive patients with brain AVMs at the University of Toronto Vascular Malformation Study Group. Angiographic features present at that time, such as location, size, and blood supply, were recorded following a standard protocol and associated, through multivariate analysis techniques, with type of presentation. RESULTS: Patients had hemorrhagic presentation in 146 cases (37.4%). Hemorrhage was the initial presentation in 59.5% of the deep-seated AVMs (odds ratio [OR]=3.26; 95% CI=1.15 to 9.2; P=0.03). A single draining vein was associated with bleeding at presentation in 57.6% AVMs (OR=1.78; 95% CI=1.12 to 2.82; P=0.01), and 72.8% of the patients with venous ectasia had bleeding as initial evidence (OR=3.9; 95% CI=1.63 to 9.28; P=0.002). Hemorrhage was the initial presentation in 47.6% (111/233) of AVMs <3 cm, 22.5% (32/142) in sizes between 3 and 6 cm, and 20% in malformations >6 cm (3/15), but these differences were not significant in multivariate analyses. CONCLUSIONS: For initial hemorrhagic presentation, a small number of draining veins, deep location, and the presence of venous ectasias were significant associated factors. In contrast with many previous reports, AVM size was not associated with hemorrhage at presentation in adjusted analyses.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".