Thromboembolic risk in patients with high titre anticardiolipin and multiple antiphospholipid antibodies
Bibliographic record
Abstract
Summary Asymptomatic antiphospholipid antibody (aPL) carriers with high risk for thrombosis may benefit from preventive anticoagulation. It was our objective to test whether the risk of thrombosis increases with: 1) increasing titres of anticardiolipin antibodies (aCL) after adjustment for other cardiovascular risk factors and 2) the number of aPL detected. In a cross-sectional study, blood was collected from clinics in two teaching hospitals. The study included 208 individuals suspected of having an aPL and 208 age- and sex-matched controls having blood drawn for a complete blood count. Clinical variables included history of previous arterial (ATE) or venous (VTE) thrombotic events, traditional risk factors for cardiovascular disease, and systemic lupus erythematosus (SLE). Laboratory variables included IgG/IgM aCL, lupus anticoagulant, and IgG/IgM anti-β2-glycoprotein I. Mean age was 46.5 years and 83% were female. Seventy-five of the 416 participants had > 1 aPL, and 69 had confirmed > 1 ATE or VTE. Family history was positive in 48% of participants, smoking in 28%, hypertension in 16%, diabetes in 6%, and SLE in 20%. A 10-unit increase in aCL IgG titre was associated with an odds ratio (OR) [95% CI] of 1.07 [1.01-1.13] for ATE and 1.06 [1.02 - 1.11] for VTE. The odds of a previous thrombosis increased with each additional aPL detected: 1.5 [0.93-2.3] for ATE and 1.7 [1.1-2.5] for VTE. These results indicate that increased titres of aCL and multiple aPL were associated with an increased risk of a previous thrombotic event.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".