400 Ana-negative sle: re-evaluation in an international inception cohort
Bibliographic record
Abstract
Background and aims The prevalence of ANA-negative SLE is reportedly 5%–20%. Cytoplasmic or mitotic cell indirect immunofluorescence (IIF) patterns are usually reported as ANA-negative. This study examined the prevalence of ANA-negativity (no intracellular IIF pattern) and pure cytoplasmic and/or mitotic IIF patterns (CMP) in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort and examined demographic, clinical and autoantibody associations. Methods Three groups were examined 1) ANA-positive (presence of nuclear IIF pattern), 2) ANA-negative (no IIF pattern), and 3) pure CMP. ANA were detected by IIF on HEp-2000 substrate, SLE-related autoantibodies by laser bead immunoassay, and anti-dsDNA and anti-dense fine speckles 70 (DFS70) by chemiluminescence immunoassay. Results 1137 patients were included; 89.9% were female. 92.3% were ANA-positive, 6.2% were ANA-negative, and 1.5% had a CMP. In the multivariate analysis (Tables 1 and 2), patients from Canada (Odds Ratio (OR) 2.07 [95% CI: 1.28, 3.36]) or with anti-DFS70 (OR 4.45 [95% CI: 1.37, 14.39]) were more likely to be ANA-negative or have CMP. Patients of Asian descent (OR 0.34 [95% CI: 0.13, 0.86]) or with anti-dsDNA (OR 0.53 [95% CI: 0.30, 0.94]), anti-SSA/Ro60 (OR 0.51 [95% CI: 0.30, 0.87]), or anti-UI-RNP (OR 0.35 [95% CI: 0.17, 0.70]) were less likely to be ANA-negative or CMP. Conclusions In newly diagnosed SLE, the prevalence of ANA-negativity was at the lower end (6.2%) of the range previously published and an additional 1.5% had a CMP pattern. The prevalence of true ANA-negativity will likely decrease as future guidelines are expected to recommend that non-nuclear patterns, such as CMP, are also reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".