Prevalence and Characteristics of Intracranial Aneurysms in Hereditary Hemorrhagic Telangiectasia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The association between hereditary hemorrhagic telangiectasia and intracranial aneurysms remains controversial. This study evaluated the prevalence and characteristics of intracranial aneurysms in patients with hereditary hemorrhagic telangiectasia with brain vascular malformations. MATERIALS AND METHODS: Between 2007 and 2021, patients enrolled in the Brain Vascular Malformation Consortium with definite hereditary hemorrhagic telangiectasia, the presence of brain vascular malformations, and available angiographic studies of the brain were retrospectively reviewed. Angiographic features of intracranial aneurysms and their relationship to coexisting brain vascular malformations were analyzed. We also examined the association between baseline clinical features and the presence of intracranial aneurysms. RESULTS: One hundred eighty patients were included. A total of 14 intracranial aneurysms were found in 9 (5%) patients, and 4 intracranial aneurysms were considered flow-related aneurysms. Patients with intracranial aneurysms were significantly older than patients without intracranial aneurysms (mean, 48.1 [SD, 18.2] years versus 33.5 [SD, 21.0] years; P = .042). If we excluded flow-related intracranial aneurysms, the prevalence of intracranial aneurysms was 3.3%. All intracranial aneurysms were in the anterior circulation, were unruptured, and had an average maximal diameter of 3.9 (SD, 1.5) mm. No intracranial aneurysms were found in pediatric patients with hereditary hemorrhagic telangiectasia. No statistically significant correlation was observed among other baseline demographics, hereditary hemorrhagic telangiectasia features, and the presence of intracranial aneurysms. CONCLUSIONS: The prevalence of intracranial aneurysms in this large cohort study is comparable with that in the general population and might be increased slightly due to hemodynamic factors associated with shunting brain vascular malformations.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".