The presence of multiple prothrombotic risk factors is associated with a higher risk of thrombosis in individuals with anticardiolipin antibodies.
Bibliographic record
Abstract
OBJECTIVE: To explore the effect of multiple prothrombotic risk factors in individuals with anticardiolipin antibodies (aCL), we evaluated immunologic, coagulation, and genetic prothrombotic abnormalities in a cohort of individuals with different aCL titers. METHODS: We recruited 87 individuals into 4 categories (normal, low, intermediate, or high) based on their baseline IgG aCL (aCL-IgG) titers. We measured at followup: repeat aCL-IgG, IgM aCL (aCL-IgM), antibodies to beta2-glycoprotein I (anti-beta2-GPI), lupus anticoagulant (LAC) antibodies, protein C, protein S, activated protein C resistance, factor V506 Leiden mutation, methyl tetrahydrofolate reductase (MTHFR) C677T genotype, and prothrombin 20210A gene mutation. Thrombotic events were confirmed. RESULTS: At recruitment, 20 individuals were negative for aCL-IgG and 67 were positive (22 low, 20 intermediate, and 25 high titer). Twenty of the 87 participants had experienced a previous thrombotic event: 4 in the aCL-IgG negative group and 16 in the aCL-IgG positive group. Among the 87 individuals, the number of those with concomitant prothrombotic risk factors was as follows: 5 had no other prothrombotic risk factors, 32 had 1 risk factor, 24 had 2 risk factors, 10 had 3 risk factors, 10 had 4 risk factors, and 6 had 5 risk factors. Thrombotic events were observed in 20%, 13%, 33%, 10%, 30%, and 50% of these groups, respectively, and the odds ratio associated with a previous thrombotic event was 1.46 per each additional prothrombotic risk factor (95% confidence interval: 1.003-2.134). CONCLUSION: In individuals with positive aCL-IgG, we observed an association between the number of prothrombotic risk factors and history of thrombotic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".