Age of onset of cerebral venous thrombosis: the BEAST study
Bibliographic record
Abstract
Abstract Background: Cerebral venous thrombosis (CVT) is an uncommon cause of stroke in young adults. We aimed to determine the impact of age, gender and risk factors (including sex-specific) on CVT onset. Methods: We used data from the BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis), a multicentre multinational prospective observational study on CVT. Composite factors analysis (CFA) was performed to determine the impact on the age of CVT onset in males and females. Results: A total of 1309 CVT patients (75.3% females) aged ⩾18 years were recruited. The overall median (IQR-interquartile range) age for males and females was 46 (35–58) years and 37 (28–47) years (p < 0.001), respectively. However, the presence of antibiotic-requiring sepsis (p = 0.03, 95% CI 27–47 years) among males and gender-specific risk factors like pregnancy (p < 0.001, 95% CI 29–34 years), puerperium (p < 0.001, 95% CI 26–34 years) and oral contraceptive use (p < 0.001, 95% CI 33–36 years) were significantly associated with earlier onset of CVT among females. CFA demonstrated a significantly earlier onset of CVT in females, ~12 years younger, in those with multiple (⩾1) compared to ‘0’ risk factors (p < 0.001, 95% CI 32–35 years). Conclusions: Women suffer CVT 9 years earlier in comparison to men. Female patients with multiple (⩾1) risk factors suffer CVT ~12 years earlier compared to those with no identifiable risk factors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".