Rosuvastatin for the prevention of venous thromboembolism: a pooled analysis of the HOPE-3 and JUPITER randomized controlled trials
Bibliographic record
Abstract
AIMS: To examine the association between rosuvastatin and VTE risk, and whether effects vary in different subpopulations stratified by key demographic, cardiovascular disease (CVD) risk factors, and other risk factors associated with VTE. METHODS AND RESULTS: An individual participant data meta-analysis was conducted across two randomized controlled trials in 30 507 participants over a mean follow-up of 3.62 years, individuals had no prior history of vascular disease but were at intermediate CV risk. In both trials, participants were randomized to receive rosuvastatin or matching placebo. The primary outcome was VTE during follow-up, defined as either deep vein thrombosis or pulmonary embolism. Associations between rosuvastatin and VTE were examined in the overall pooled cohort, and subpopulations stratified by demographic risk factors (i.e. age and sex), CVD risk factors (i.e. obesity, smoking, lipid levels, blood pressure levels, and C-reactive protein level), and a history of cancer. Mean age was 65.96 (SD 7.19) years of age, and 17 832 (58.45%) were male and 5434 (17.82%) were smokers, median BMI was 27.6 [interquartile range (IQR) 24.7-31.1] kg/m2, and median CRP level was 3.4 (IQR 2.1-6.0) mg/L. There were 139 VTE events. In the pooled cohort, rosuvastatin was associated with a large proportional reduction in the risk of VTE (hazard ratio 0.53, 95% CI 0.37-0.75). No significant interactions were observed between treatment with rosuvastatin and the risk of VTE across subpopulations stratified by demographic, CVD risk factors, or a history of cancer (P-values for interactions >0.05 for all subgroups). CONCLUSION: Rosuvastatin is associated with a 47% proportional reduction in the risk of VTE, and its effect is consistent both in the presence or absence of VTE-related clinical risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.119 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.037 | 0.061 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".