Hemorrhage Rates From Brain Arteriovenous Malformation in Patients With Hereditary Hemorrhagic Telangiectasia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Hereditary hemorrhagic telangiectasia (HHT) is a systemic disease characterized by mucocutaneous telangiectasias, epistaxis, and arteriovenous malformations (AVMs). Intracranial hemorrhage (ICH) rates in this population are not well described. We report ICH rates and characteristics in HHT patients with brain AVMs (HHT-BAVMs). METHODS: We studied the first 153 HHT-BAVM patients with follow-up data enrolled in the Brain Vascular Malformation Consortium HHT Project. We estimated ICH rates after BAVM diagnosis. RESULTS: The majority of patients were women (58%) and white (98%). The mean age at BAVM diagnosis was 31±19 years (range, 0-70), with 61% of cases diagnosed on asymptomatic screening. Overall, 14% presented with ICH; among symptomatic cases, 37% presented ruptured. During 493 patient-years of follow-up, 5 ICH events occurred yielding a rate of 1.02% per year (95% confidence interval, 0.42-2.44%). ICH-free survival differed significantly by ICH presentation (P=0.003); ruptured cases had a higher ICH rate (10.07%; 95% confidence interval, 3.25-31.21%) than unruptured cases (0.43%; 95% confidence interval, 0.11-1.73%). CONCLUSIONS: Patients with HHT-BAVM who present with hemorrhage are at a higher risk for rehemorrhage compared with patients with BAVM detected presymptomatically.
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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 | 0.003 |
| 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".