Comparison of Sensitivities of American College of Rheumatology and Systemic Lupus International Collaborating Clinics Classification Criteria in Childhood-onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Currently there are 2 different classification criteria for systemic lupus erythematosus (SLE): American College of Rheumatology (ACR) and Systemic Lupus International Collaborating Clinics (SLICC). The aim of this study was to compare the sensitivities of ACR and SLICC criteria in childhood-onset SLE (cSLE) using a large, multiethnic cohort. METHODS: We conducted a retrospective study of 722 patients diagnosed with cSLE at The Hospital for Sick Children (SickKids). Prospectively collected data from SickKids' Lupus Database were reviewed/validated against medical records prior to ACR and SLICC scoring based on cumulative symptoms up to the last visit. Sensitivities were compared using McNemar's test. Descriptive statistics were used to identify SLE features unique to each set of criteria and autoantibodies not included in either. RESULTS: ACR and SLICC sensitivities were as follows: 92.4% and 96.3% overall (p = 0.001); 82.5% and 91.3% (p = 0.01) in those scored ≤ 1 year from diagnosis; 92.7% and 97.9% (p = 0.02) in those scored 2-3 years from diagnosis. Forty-eight of 55 (87.3%) patients who did not meet ACR criteria met SLICC criteria through SLICC-specific criterion or renal biopsy. Twenty of 27 (74.1%) patients who did not meet SLICC criteria met ACR criteria as a result of photosensitivity (73.9%) and ACR lymphopenia criteria (26.1%). Six of 7 patients (85.7%) who were clinically diagnosed with cSLE but did not meet either SLICC or ACR criteria had anti-Ro antibodies. CONCLUSION: SLICC criteria were significantly more sensitive than ACR criteria in cSLE classification, especially early in the disease course. Because of the extreme rarity of primary Sjögren syndrome in children, one may consider adding anti-Ro antibodies to the classification criteria for cSLE because they are present in ∼40% of patents with cSLE.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".