The prevalence and determinants of anti-DFS70 autoantibodies in an international inception cohort of systemic lupus erythematosus patients
Bibliographic record
Abstract
Autoantibodies to dense fine speckles 70 (DFS70) are purported to rule out the diagnosis of SLE when they occur in the absence of other SLE-related autoantibodies. This study is the first to report the prevalence of anti-DFS70 in an early, multinational inception SLE cohort and examine demographic, clinical, and autoantibody associations. Patients were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. The association between anti-DFS70 and multiple parameters in 1137 patients was assessed using univariate and multivariate logistic regression. The frequency of anti-DFS70 was 7.1% (95% CI: 5.7-8.8%), while only 1.1% (95% CI: 0.6-1.9%) were monospecific for anti-DFS70. In multivariate analysis, patients with musculoskeletal activity (Odds Ratio (OR) 1.24 [95% CI: 1.10, 1.41]) or with anti-β2 glycoprotein 1 (OR 2.17 [95% CI: 1.22, 3.87]) were more likely and patients with anti-dsDNA (OR 0.53 [95% CI: 0.31, 0.92]) or anti-SSB/La (OR 0.25 [95% CI: 0.08, 0.81]) were less likely to have anti-DFS70. In this study, the prevalence of anti-DFS70 was higher than the range previously published for adult SLE (7.1 versus 0-2.8%) and was associated with musculoskeletal activity and anti-β2 glycoprotein 1 autoantibodies. However, 'monospecific' anti-DFS70 autoantibodies were rare (1.1%) and therefore may be helpful to discriminate between ANA-positive healthy individuals and SLE.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".