Autoantibodies in systemic lupus erythematosus: comparison of historical and current assessment of seropositivity
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is characterized by multiple autoantibodies and complement activation. Recent studies have suggested that anti-nuclear antibody (ANA) positivity may disappear over time in some SLE patients. Anti-double-stranded DNA (dsDNA) antibody titers and complement levels may vary with time and immunosuppressive treatment, while the behavior of anti-extractable nuclear antigen (ENA) over time is less well understood. This study sought to determine the correlation between historical autoantibody tests and current testing in patients with SLE. Three hundred and two SLE patients from the ACR Reclassification of SLE (AROSE) database with both historical and current laboratory data were selected for analysis. The historical laboratory data were compared with the current autoantibody tests done at the reference laboratory and tested for agreement using percent agreement and Kappa statistic. Serologic tests included ANA, anti-dsDNA, anti-Smith, anti-ribonucleoprotein (RNP), anti-Ro, anti-La, rheumatoid factor (RF), C3 and C4. Among those historically negative for immunologic markers, a current assessment of the markers by the reference laboratory generally yielded a low percentage of additional positives (3-13%). However, 6/11 (55%) of those historically negative for ANA were positive by the reference laboratory, and the reference laboratory test also identified 20% more patients with anti-RNP and 18% more with RF. Among those historically positive for immunologic markers, the reference laboratory results were generally positive on the same laboratory test (range 57% to 97%). However, among those with a history of low C3 or C4, the current reference laboratory results indicated low C3 or C4 a low percentage of the time (18% and 39%, respectively). ANA positivity remained positive over time, in contrast to previous studies. Anti-Ro, La, RNP, Smith and anti-dsDNA antibodies had substantial agreement over time, while complement had less agreement. This variation could partially be explained by variability of the historical assays, which were done by local laboratories over varying periods of time. Variation in the results for complement, however, is more likely to be explained by response to treatment. These findings deserve consideration in the context of diagnosis and enrolment in clinical trials.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".