Abstract WP216: "BEAST" at the University of Virginia: Demographic and Phenotypic Data of Patients Contributing to a Biorepository to Establish the Etiology of Sinovenous Thrombosis
Bibliographic record
Abstract
Introduction: Cerebral venous sinus thrombosis (CVST) occurs in 3-4 per 1 million individuals per year, accounting for approximately 1% of all strokes. Multiple etiologic risk factors for CVST have been identified, with known inherited thrombophilias accounting for 22% of cases. In 15% of cases, no known risk factor is identified. Studying the genetics of CVST holds the potential to identify at-risk groups, determine disease severity and prognosis, and consider novel therapies. BEAST is an international effort to identify genetic determinants of CVST, from which a recent discovery genome-wide association study (GWAS) identified 2 new associated loci. Methods: We are enrolling patients at least 18 years of age with a diagnosis of CVST who are willing to provide informed consent and a biospecimen to a central DNA repository. Patients are identified prospectively in clinical practice, and retrospectively using the Univ of Virginia clinical data repository. Results: Since 2012, twenty-eight patients have been enrolled. Demographic and phenotypic data are presented in the Table. Patients range in age from 19 to 65 years, with a mean of 40.5 (SD 14). Headache was the most common presenting symptom, occurring in 60%, followed by seizure (25%), mental status disturbance (21%), aphasia (11%) and mono- or hemiparesis (7%). Imaging revealed focal cerebral edema and/or venous infarction in 7 patients (25%) and hemorrhage in 5 patients (18%); no arteriovenous fistulae were identified. Acute treatments included intravenous or low molecular weight heparin (25/28, 89%) and anti-epileptic medications (7/28, 25%); three patients underwent local thrombolysis or endovascular intervention (11%). Conclusion: Understanding the association between phenotype and genetic determinants of CVST has the potential to advance the diagnosis and management of this challenging entity. Enrollment in BEAST continues and a replication cohort GWAS is anticipated in the near future.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".