Towards the genetic basis of cerebral venous thrombosis—the BEAST Consortium: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Cerebral venous thrombosis (CVT) is a rare cerebrovascular condition accounting for <1% of all stroke cases and mainly affects young adults. Its genetic aetiology is not clearly elucidated. METHODS AND ANALYSIS: To better understand the genetic basis of CVT, we have established an international biobank of CVT cases, Biorepository to Establish the Aetiology of Sinovenous Thrombosis (BEAST) which aims to recruit highly phenotyped cases initially of European descent and later from other populations. To date we have recruited 745 CVT cases from 12 research centres. As an initial step, the consortium plans to undertake a genome-wide association analysis of CVT using the Illumina Infinium HumanCoreExome BeadChip to assess the association and impact of common and low-frequency genetic variants on CVT risk by using a case-control study design. Replication will be performed to confirm putative findings. Furthermore, we aim to identify interactions of genetic variants with several environmental and comorbidity factors which will likely contribute to improve the understanding of the biological mechanisms underlying this complex disease. ETHICS AND DISSEMINATION: BEAST meets all ethical standards set by local institutional review boards for each of the participating sites. The research outcomes will be published in international peer-reviewed open-access journals with high impact and visibility. The results will be presented at national and international meetings to highlight the contributions into improving the understanding of the mechanisms underlying this uncommon but important disease. This international DNA repository will become an important resource for investigators in the field of haematological and vascular disorders.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".