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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".