Association of European population levels of thrombotic and inflammatory factors with risk of coronary heart disease: the MONICA Optional Haemostasis Study†
Bibliographic record
Abstract
AIMS: Classical risk factors do not fully explain international differences in risk of coronary heart disease (CHD). We therefore measured thrombotic and inflammatory markers in a substudy of the WHO MONICA project and correlated these with CHD event rates. METHODS AND RESULTS: We measured levels of fibrinogen (clottable and nephelometric), von Willebrand factor (vWf), tissue plasminogen activator antigen, plasminogen activator inhibitor activity, fibrin D-dimer, plasma viscosity, C-reactive protein, and total cholesterol in 12 MONICA populations (listed at the end of this paper), all but one European. Men and women aged 45-64 years were studied from 10 countries. All samples were collected using a carefully standardized protocol, and analysed centrally. Results were available for 3996 subjects (nephelometric fibrinogen and viscosity), 2378 subjects (other thrombotic assays), and 1757 subjects (C-reactive protein and total cholesterol). Significant differences in levels of thrombotic and inflammatory factors exist in MONICA populations mainly from European countries. These differences persist after adjustment for age, smoking habit, and body mass index. Cross-sectional correlations between coronary event rates and these thrombotic/inflammatory markers were significant for vWF antigen in both sexes, nephelometric fibrinogen in men, and D-dimer in women. CONCLUSION: In particular, vWF, nephelometric fibrinogen, and D-dimer should be examined in further research as potential risk factors which may help explain differences in coronary risk between European populations.
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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 | 0.000 |
| 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 | 0.001 |
| 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".