Quantitative Measurement of Carotid Atherosclerosis in Relation to Levels of Von Willebrand Factor and Fibrinolytic Variables in Plasma -- A 2-year Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: It has been proposed that the mechanism of action of the new risk factors for myocardial infarction and stroke, von Willebrand factor (vWF), tissue plasminogen activator (tPA) and tissue plasminogen activator inhibitor-1 (PAI-1) could possibly be mediated via a primary effect on atherogenesis but there is little data to substantiate this. DESIGN: A prospective single-centre cohort study of progression of atherosclerosis. METHODS: Carotid plaque area was quantitated by two-dimensional (2D) ultrasound in 258 subjects at entry and after 1 and 2 years. Plasma and serum samples were drawn at baseline and serum lipids and plasma levels of haemostatic factors were measured. RESULTS: The traditional risk factors, smoking, total cholesterol, hypertension and male gender explained 51% of the variance in plaque area at baseline and 48% at 1-year follow-up. There were small positive associations of plaque area with vWF, tPA and tPA/PAI-1 complex and a tendency to negative associations with PAI-1 levels, independent from the traditional risk factors. The additional explanatory power of the haemostatic factors did not exceed 3%. CONCLUSION: The data accord with a marginal role in atherogenesis of vWF and tPA, and underline the major impact of smoking, hypertension and cholesterol on carotid plaque area progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".