Anti-beta 2 glycoprotein I IgA in the SLICC classification criteria dataset
Bibliographic record
Abstract
OBJECTIVE: Anti-beta 2 glycoprotein I IgA is a common isotype of anti-beta 2 glycoprotein I in SLE. Anti-beta 2 glycoprotein I was not included in the American College of Rheumatology (ACR) SLE classification criteria, but was included in the Systemic Lupus International Collaborating Clinics (SLICC) criteria. We aimed to evaluate the prevalence of anti-beta 2-glycoprotein I IgA in SLE versus other rheumatic diseases. In addition, we examined the association between anti-beta 2 glycoprotein I IgA and disease manifestations in SLE. METHODS: The dataset consisted of 1384 patients, 657 with a consensus physician diagnosis of SLE and 727 controls with other rheumatic diseases. Anti-beta 2 glycoprotein I isotypes were measured by ELISA. Patients with a consensus diagnosis of SLE were compared to controls with respect to presence of anti-beta 2 glycoprotein I. Among patients with SLE, we assessed the association between anti-beta 2 glycoprotein I IgA and clinical manifestations. RESULTS: The prevalence of anti-beta 2 glycoprotein I IgA was 14% in SLE patients and 7% in rheumatic disease controls (odds ratio, OR 2.3, 95% CI: 1.6, 3.3). It was more common in SLE patients who were younger patients and of African descent (p = 0.019). Eleven percent of SLE patients had anti-beta 2 glycoprotein I IgA alone (no anti-beta 2 glycoprotein I IgG or IgM). There was a significant association between anti-beta 2 glycoprotein I IgA and anti-dsDNA (p = 0.001) and the other antiphospholipid antibodies (p = 0.0004). There was no significant correlation of anti-beta 2 glycoprotein I IgA with any of the other ACR or SLICC clinical criteria for SLE. Those with anti-beta 2 glycoprotein I IgA tended to have a history of thrombosis (12% vs 6%, p = 0.071), but the difference was not statistically significant. CONCLUSION: We found the anti-beta 2 glycoprotein I IgA isotype to be more common in patients with SLE and in particular, with African descent. It could occur alone without other isotypes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".