Evaluating the Applicability of the EULAR/ACR 2019, SLICC 2012, and ACR 1997 Classification Criteria for Systemic Lupus Erythematosus in Children: A Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects approximately 15% to 20% of patients during childhood at initial onset. In childhood-onset SLE (cSLE), clinical manifestations are not typical in the early stages; therefore, cSLE-specific classification criteria are lacking, making diagnosis difficult. To evaluate suitable classification criteria for these patients, we conducted a multicenter cohort study to compare the practicability of the European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) 2019, Systemic Lupus International Collaborating Clinics (SLICC) 2012, and the ACR1997 classification criteria for SLE. METHODS: There were patients from 3 different regions, including children with cSLE (n = 348), a pediatric control group (n = 59), adults with SLE (n = 80), and an adult control group (n = 76). Sensitivity, specificity, and area under the curve (AUC) values for the EULAR/ACR 2019, SLICC 2012, and ACR 1997 classification criteria were calculated. Serial and parallel tests were conducted, and data were compared after adjusting for the EULAR/ACR 2019 classification criteria score thresholds. RESULTS: There were 348 cases with a firmly established clinical diagnosis of cSLE (83.62% female) included. Among children with SLE, the ACR 1997 criteria showed the highest specificity (98.31%, 95% CI 90.9-100%), whereas SLICC 2012 criteria had the highest sensitivity (94.54%, 95% CI 91.6-96.7%). In comprehensive comparisons, the EULAR/ACR 2019 criteria yielded the highest AUC (0.944) and Youden index (0.89) values. Parallel testing using the SLICC 2012 or EULAR/ACR 2019 criteria for cSLE achieved the highest AUC (0.962) and increased sensitivity to 99.14%. Finally, a EULAR/ACR 2019 score of 10 (cSLE cutoff) produced the highest AUC (0.953, 95% CI 0.93-0.97). CONCLUSION: The EULAR/ACR 2019 criteria were the most appropriate for diagnosing cSLE. Moreover, parallel testing using the SLICC 2012 or EULAR/ACR 2019 criteria enhanced diagnostic sensitivity. In addition, a total EULAR/ACR 2019 score of ≥ 10 was appropriate for classifying cSLE. Our findings provide a basis for determining the most appropriate diagnostic strategy for children with 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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".