Antinuclear Antibody–Negative Systemic Lupus Erythematosus in an International Inception Cohort
Bibliographic record
Abstract
OBJECTIVE: The spectrum of antinuclear antibodies (ANAs) is changing to include both nuclear staining as well as cytoplasmic and mitotic cell patterns (CMPs) and accordingly a change is occurring in terminology to anticellular antibodies. This study examined the prevalence of indirect immunofluorescence (IIF) anticellular antibody staining using the Systemic Lupus International Collaborating Clinics inception cohort. METHODS: Anticellular antibodies were detected by IIF on HEp-2000 substrate using the baseline serum. Three serologic subsets were examined: ANA positive (presence of either nuclear or mixed nuclear/CMP staining), anticellular antibody negative (absence of any intracellular staining), and isolated CMP staining. The odds of being anticellular antibody negative versus ANA or isolated CMP positive was assessed by multivariable analysis. RESULTS: A total of 1,137 patients were included; 1,049 (92.3%) were ANA positive, 71 (6.2%) were anticellular antibody negative, and 17 (1.5%) had an isolated CMP. The isolated CMP-positive group did not differ from the ANA-positive or anticellular antibody-negative groups in clinical, demographic, or serologic features. Patients who were older (odds ratio [OR] 1.02 [95% confidence interval (95% CI) 1.00, 1.04]), of white race/ethnicity (OR 3.53 [95% CI 1.77, 7.03]), or receiving high-dose glucocorticoids at or prior to enrollment (OR 2.39 [95% CI 1.39, 4.12]) were more likely to be anticellular antibody negative. Patients on immunosuppressants (OR 0.35 [95% CI 0.19, 0.64]) or with anti-SSA/Ro 60 (OR 0.41 [95% CI 0.23, 0.74]) or anti-U1 RNP (OR 0.43 [95% CI 0.20, 0.93]) were less likely to be anticellular antibody negative. CONCLUSION: In newly diagnosed systemic lupus erythematosus, 6.2% of patients were anticellular antibody negative, and 1.5% had an isolated CMP. The prevalence of anticellular antibody-negative systemic lupus erythematosus will likely decrease as emerging nomenclature guidelines recommend that non-nuclear patterns should also be reported as a positive ANA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".