Role of von Willebrand factor in venous thromboembolic disease
Bibliographic record
Abstract
OBJECTIVE: Evolving evidence of the shared risk factors and pathogenic mechanisms in arterial and venous thrombosis questions of the strict vascular dichotomy of arterial vs venous. The connection between arterial and venous thrombosis has been highlighted by common underlying inflammatory processes, a concept known as thromboinflammatory disease. Using this relationship, we can apply knowledge from arterial disease to better understand and potentially mitigate venous disease. A protein that has been extensively studied in atherothrombotic disease and inflammation is von Willebrand factor (VWF). Because many predisposing and provoking factors of venous thromboembolism (VTE) have been shown to directly modulate VWF levels, it is, perhaps, not surprising that VWF has been highlighted by several recent association studies of patients with VTE. METHODS: In the present narrative review, we investigated more deeply the effects of VWF in venous disease by synthesizing the data from clinical studies of deep vein thrombosis of the limbs, pulmonary embolism, portal and cerebral vein thrombosis, and the complications of thrombosis, including post-thrombotic syndrome, venous insufficiency, and chronic thromboembolic pulmonary hypertension. We have also discussed the findings from preclinical studies to highlight novel VWF biochemistry in thrombosis and therapeutics. RESULTS: Across the spectrum of venous thromboembolic disease, we consistently observed that elevated VWF levels conferred an increased risk of VTE and long-term venous complications. We have highlighted important findings from VWF molecular research and have proposed mechanisms by which VWF participates in venous disease. Emerging evidence from preclinical studies might reveal novel targets for thromboinflammatory disease, including specific VWF pathophysiology. Furthermore, we have highlighted the utility of measuring VWF to prognosticate and risk stratify for VTE and its complications. CONCLUSIONS: As the prevalence of inflammatory processes, such as aging, obesity, and diabetes increases in our population, it is critical to understand the evolving role of VWF in venous disease to guide clinical decisions and therapeutics.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".