How Do Patients With Newly Diagnosed Systemic Lupus Erythematosus Present? A Multicenter Cohort of Early Systemic Lupus Erythematosus to Inform the Development of New Classification Criteria
Bibliographic record
Abstract
Objective Systemic lupus erythematosus (SLE) presents with nonspecific signs and symptoms that are also found in other conditions. This study aimed to evaluate manifestations at disease onset and to compare early SLE manifestations to those of diseases mimicking SLE. Methods Academic lupus centers in Asia, Europe, North America, and South America collected baseline data on patients who were referred to them during the previous 3 years for possible SLE and who had a symptom duration of <1 year. Clinical and serologic manifestations were compared between patients diagnosed as having SLE and those diagnosed as having SLE‐mimicking conditions. Diagnostic performance of the 1997 American College of Rheumatology (ACR) SLE classification criteria and the 2012 Systemic Lupus International Collaborating Clinics (SLICC) SLE classification criteria was tested. Results Data were collected on 389 patients with early SLE and 227 patients with SLE‐mimicking conditions. Unexplained fever was more common in early SLE than in SLE‐mimicking conditions (34.5% versus 13.7%, respectively; P < 0.001). Features less common in early SLE included Raynaud's phenomenon (22.1% versus 48.5%; P < 0.001), sicca symptoms (4.4% versus 34.4%; P < 0.001), dysphagia (0.3% versus 6.2%; P < 0.001), and fatigue (28.3% versus 37.0%; P = 0.024). Anti–double‐stranded DNA, anti–β2‐glycoprotein I antibodies, positive Coombs’ test results, autoimmune hemolytic anemia, hypocomplementemia, and leukopenia were more common in early SLE than in SLE‐mimicking conditions. Symptoms detailed in the ACR and SLICC classification criteria were significantly more frequent among those with early SLE. Fewer patients with early SLE were not identified as having early SLE with use of the SLICC criteria compared to the ACR criteria (16.5% versus 33.9%), but the ACR criteria demonstrated higher specificity than the SLICC criteria (91.6% versus 82.4%). Conclusion In this multicenter cohort, clinical manifestations that could help to distinguish early SLE from SLE‐mimicking conditions were identified. These findings may aid in earlier SLE diagnosis and provide information for ongoing initiatives to revise SLE classification criteria.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".