Antiphospholipid antibodies predict imminent vascular events independently from other risk factors in a prospective cohort
Bibliographic record
Abstract
Summary Antiphospholipid antibodies (aPL) are associated with vascular events, but the magnitude of this risk, alone, or in combination with other atherogenic and thrombophilic risk factors, remains unclear. A prospective cohort of 415 persons was studied for arterial and venous events (AE and VE) over a median time of 7.4 years. aPL and coagulation abnormalities were measured upon beginning of the study and annually for the first four years. Within the cohort, a nested case-control study was conducted to investigate the role of endothelial and inflammatory markers in predicting new vascular events. Forty-five individuals had new vascular events: 18 occurred during the first year of follow-up. The proportion of event-free survivors at eight years was 90% (95%CI = 87%, 94%) for aPL-negative and 72% (60%, 85%) for aPL-positive individuals, respectively. Predictors for new AE were previous AE (HR=5.7 [2.7, 12.0]), diabetes (5.6 [2.4, 13.2]), aPL positivity (2.6 ([1.2, 5.9]), and age (1.04 [1.01, 1.07]). New VE were predicted by previous VE (6.1 [1.9, 19.9]), anti-β2-glyco-protein I (aβ2GPI) positivity (5.8 [1.4, 24.1]), activated protein C resistance (APCR) (4.1 [1.1, 15.1]), and gender (3.7 [1.1, 12.9]). In the nested case-control study, similar predictors were observed for AE, while abnormal APCR (OR=5.5 [1.1, 26.6]) and elevated von Willebrand factor (vWF) (OR=5.0 [1.2, 19.8]) best predicted VE. We demonstrate that aPL independently predict new vascular events and discriminate between individuals with and without events in the first two years of follow-up, indicating that aPL are associated with a short-term risk of developing new and recurrent vascular events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".