Cardiovascular Risk Factors and Venous Thromboembolism
Bibliographic record
Abstract
BACKGROUND: The concept that venous thromboembolism (VTE) and atherosclerosis are 2 completely distinct entities has recently been challenged because patients with VTE have more asymptomatic atherosclerosis and more cardiovascular events than control subjects. We performed a meta-analysis to assess the association between cardiovascular risk factors and VTE. METHODS AND RESULTS: Medline and EMBASE databases were searched to identify studies that evaluated the prevalence of major cardiovascular risk factors in VTE patients and control subjects. Studies were selected using a priori defined criteria, and each study was reviewed by 2 authors who abstracted data on study characteristics, study quality, and outcomes. Odds ratios or weighted means and 95% confidence intervals (CIs) were then calculated and pooled using a random-effects model. Statistical heterogeneity was evaluated through the use of chi2 and I2 statistics. Twenty-one case-control and cohort studies with a total of 63 552 patients met the inclusion criteria. Compared with control subjects, the risk of VTE was 2.33 for obesity (95% CI, 1.68 to 3.24), 1.51 for hypertension (95% CI, 1.23 to 1.85), 1.42 for diabetes mellitus (95% CI, 1.12 to 1.77), 1.18 for smoking (95% CI, 0.95 to 1.46), and 1.16 for hypercholesterolemia (95% CI, 0.67 to 2.02). Weighted mean high-density lipoprotein cholesterol levels were significantly lower in VTE patients, whereas no difference was observed for total and low-density lipoprotein cholesterol levels. Significant heterogeneity among studies was present in all subgroups except for the diabetes mellitus subgroup. Higher-quality studies were more homogeneous, and significant associations remained unchanged. CONCLUSIONS: Cardiovascular risk factors are associated with VTE. This association is clinically relevant with respect to individual screening, risk factor modification, and primary and secondary prevention of VTE. Prospective studies should further investigate the underlying mechanisms of this relationship.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".