The persistence of anticardiolipin antibodies is associated with an increased risk of the presence of lupus anticoagulant and anti-β2-glycoprotein I antibodies
Bibliographic record
Abstract
OBJECTIVE: We studied antiphospholipid antibodies (aPL) in blood samples from a cohort of individuals followed for thrombosis to determine whether the persistent presence of anticardiolipin antibodies (aCL) is associated with a greater likelihood of having lupus anticoagulant and/or anti-beta2-glycoprotein I antibodies (LA/abeta2GPI). METHODS: Blood samples from 353 individuals who had been tested for aCL on at least two occasions were tested for abeta2GPI and LA. Two groups were defined: aCL-persistent, who tested aCL-positive on at least two occasions, and aCL non-persistent, who tested aCL-positive on fewer than two occasions. Multivariate logistic regressions were performed using LA/abeta2GPI, LA and abeta2GPI as outcome variables and the percentage of aCL-positive tests as the predictor variable, adjusted for age, gender, family history of cardiovascular disease (CVD), systemic lupus erythematosus (SLE), smoking and number of venous (VT) and arterial thromboses (AT). RESULTS: Sixty-eight (19%) individuals were aCL persistent and 285 (81%) were aCL non-persistent. LA/abeta2GPI was found in 36 (53%) of the aCL persistent group and 38 (13%) of the aCL non-persistent group. The two groups were similar for age, gender and smoking. Family history of CVD, SLE, VT and AT were more frequent in the aCL persistent group. Multivariate analyses revealed that odds ratios for LA/abeta2GPI, LA and abeta2GPI were 1.34 [95% confidence interval (CI) = 1.22-1.47], 1.36 (95% CI = 1.24-1.50) and 1.47 (95% CI = 1.31-1.65) respectively for each 10% increase in aCL-positive tests vs 0% positive tests. CONCLUSION: Persistence of aCL positivity is associated with an increased risk of LA/abeta2GPI.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".