IgM Anti-ß<sub>2</sub>Glycoprotein I Is Protective Against Lupus Nephritis and Renal Damage in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Antibodies to ß(2) glycoprotein I (IgG and IgM isotypes) have recently been added to the laboratory criteria of the revised antiphospholipid syndrome classification criteria. We investigated whether IgM anti-ß(2)-glycoprotein I (anti-ß(2)-GPI) is associated with clinical manifestations of systemic lupus erythematosus (SLE). METHODS: Anti-ß(2)-GPI was measured in 796 patients with SLE (93% women, 53% white, 38% African American, mean age 45 yrs). IgM anti-ß(2)-GPI (> 20 phospholipid units) was found in 16%. Associations were determined with clinical manifestations of SLE and with components of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index. RESULTS: As expected, IgM anti-ß(2)-GPI was highly associated with both the lupus anticoagulant and with anticardiolipin. It was associated with transient ischemic attack (OR 2.64, p = 0.04), but not significantly with venous or arterial thrombosis. IgM anti-ß(2)-GPI was protective against lupus nephritis (OR 0.54, p = 0.049), renal damage (p = 0.019), and hypertension (OR 0.58, p = 0.008). This protective effect remained after adjustment for ethnicity. CONCLUSION: In SLE, IgM anti-ß(2)-GPI is not associated with thrombosis but is protective against lupus nephritis and renal damage. "Natural" autoantibodies of the IgM isotype may have a protective effect.
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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 | 0.000 |
| 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) | 0.002 | 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".