Abstract P249: Hemostatic Factors and Long-Term Risk of Peripheral Arterial Disease: The Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Background: A few cross-sectional studies have reported associations between hemostatic factors and peripheral arterial disease (PAD), but prospective data are largely lacking. Hypothesis: Plasma hemostatic factors are associated with incident PAD, independently of traditional atherosclerotic risk factors. Methods: In 14,071 men and women (age 45-64 years and 25.4% blacks) at visit 1 (1987-1989) of the ARIC Study, we investigated the associations of fibrinogen, Von Willebrand factor (VWF), factor VIII, factor VII, Antithrombin III (ATIII) with incidence of PAD (defined as hospitalizations with PAD diagnosis [ICD-9: 440.2x, 440.3, and 440.4] or leg revascularization [38.18, 39.25, 39.29 and 39.50]). We also explored associations of d-dimer measured at visit 3 (1993-1995) in 11,619 participants. Results: We identified 540 incident PAD during a median follow-up of 24.4 years. Fibrinogen, VWF, factor VIII, and d-dimer demonstrated positive dose-response relationships to incident PAD, independent of other risk factors (Table). In comparison with respective referent categories, significantly higher PAD risk was observed in the top two quintiles of fibrinogen, VWF, and d-dimer and the highest quintile of factor VIII. When fibrinogen, VWF, and factor VIII were modeled simultaneously (d-dimer was measured at a different visit), only fibrinogen and VWF remained significantly associated with PAD. Conclusion: Hemostatic factors, particularly fibrinogen and VWF (as well as d-dimer), were independently associated with future risk of PAD. Our findings suggest the pathophysiological involvement of hemostasis in the development of PAD and potential usefulness of those factors for classifying long-term risk of PAD.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".