Dyslipidemia, statins, and venous thromboembolism: a potential risk factor and a potential treatment
Bibliographic record
Abstract
The optimal drug for the prevention of venous thromboembolism is one that is efficacious, associated with minimal bleeding risk, and easy to administer. Statins fulfill the latter two criteria, but their efficacy remains unproved. By examining the association between dyslipidemia and venous thromboembolism, as well as the evidence that statins might prevent venous thromboembolism, there may be a new rationale for the use of this class of drugs. There may be a common link between arterial and venous thrombosis. Dyslipidemia may be one of the many systemic factors associated not only with arterial thrombosis, but with venous thromboembolism as well. This may occur through the effects of circulating lipid molecules on the vascular endothelium, platelet function, and coagulation factors. By impeding these mechanisms, statins may be protective against venous thrombosis, but epidemiologic studies are few in number, and no randomized clinical trials have been conducted. Better epidemiologic evidence is required to establish whether dyslipidemia is a risk factor for venous thromboembolism. If future observational studies can demonstrate that statins are associated with a lower risk of venous thromboembolism, then consideration should be given to conducting a randomized clinical trial comparing statins with placebo for the prevention of venous thromboembolism. Until then, the efficacy of statins for the prevention or treatment of venous thromboembolism remains uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".