Idiopathic Venous Thromboembolism and Metabolic Syndrome: A Meta-analysis
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a recognised risk factor for arterial thromboembolism. However, whether MetS is also a risk factor for venous thromboembolism (VTE) is uncertain. PubMed, Embase, Web of science, and Cochrane databases were searched for case-control and cohort studies as well as conference proceedings of the International society on Thrombosis and Haemostasis (ISTH), and the Women's Health International Symposium Thrombosis and Hemostasis Branch (WHITH) published on or before March 1, 2021, to identify eligible studies. All included articles were assessed by two investigators using the Newcastle-Ottawa scale (NOS). We calculated odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the association between VTE and MetS by using random or fixed-effects models. There were 31 case-control and 5 cohort studies with a total of 78,529 participants that fulfilled the inclusion criteria, MetS (OR 1.49; 95% CI 1.29-1.73) and its critical component obesity (OR 2.03; 95% CI 1.74-2.37), hypertension (OR 1.40; 95% CI 1.19-1.64) and diabetes mellitus (OR 1.22; 95% CI 1.01-1.48) were significant risk factors for VTE. MetS and its critical component obesity may contribute to the multifactorial pathogenesis of VTE. Key Words: Venous thromboembolism, Metabolic syndrome, Obesity.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".