Antiphospholipid antibodies and thrombosis: association with acquired activated protein C resistance in venous thrombosis and with hyperhomocysteinemia in arterial thrombosis
Bibliographic record
Abstract
Although antiphospholipid antibodies (aPL) are associated with thrombosis, it is not known who with aPL is at higher risk for thrombosis. It was the aim of this cross-sectional study to investigate how thrombophilic factors contribute to venous or arterial thrombosis in aPL-positive individuals. In outpatient test centres at two tertiary care hospitals, two hundred and eight (208) persons requiring aPL testing were matched by age, gender and centre to 208 persons requiring a complete blood count. Persons were classified as aPL-positive (having anticardiolipin, lupus anticoagulant and/or anti-beta(2)-glycoprotein I antibodies) or aPL-negative. Several thrombophilic factors were studied using logistic regression modelling. Results showed that the aPL-positive group had three-fold more events (37%) than the aPL-negative group (12%). In unadjusted analyses, clinically important associations were observed between factor V Leiden and venous thrombosis, hyperhomocysteinemia and arterial thrombosis, and activated protein C resistance (APCR) and venous thrombosis (OR, 95% CI = 4.00, 1.35-11.91; 4.79, 2.03-11.33; and 2.03, 1.03-3.97, respectively). After adjusting for recruitment group, persons with both APCR and aPL had a three-fold greater risk (OR, 95% CI = 3.31, 1.30-8.41) for venous thrombosis than those with neither APCR nor aPL. Similarly, after adjusting for hypertension, family history of cardiovascular disease, gender and recruitment group, persons with both hyperhomocysteinemia and aPL had a five-fold increased risk (OR, 95% CI = 4.90, 1.37-17.37) for arterial thrombosis compared to those with neither risk factor. In conclusion, APCR phenotype and hyperhomocysteinemia are associated with a higher risk of venous and arterial thrombosis, respectively, in the presence of aPL.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".