9.4 Autoantibodies in pediatric systemic lupus erythematosus: ethnic grouping, autoantibody clustering and clinical correlations
Bibliographic record
Abstract
The aims of this study were: 1) to evaluate the spectrum of serum autoantibodies (AA) in pediatric-onset systemic lupus erythematosus (pSLE) with a focus on ethnic differences, 2) to use cluster analysis to identify patients with similar AA patterns and to determine their clinical associations. A single center cohort study of all newly diagnosed pSLE patients seen over an 8-year period was performed. Ethnicity, clinical and serological data were available in 156/169 patients (92%). The frequencies of 10 selected AA among ethnic groups were compared. Cluster analysis was used to identify groups of patients with similar AA profiles. Associations of these groups with clinical and laboratory features of pSLE were examined. Among our 5 ethnic groups, there were differences in the prevalence of anti-U1RNP and anti-Sm antibodies which occurred more frequently in non-Caucasian patients (p < 0.0001, p < 0.01, resp.). Cluster analysis revealed 3 AA clusters. Cluster 1 consisted of anti-dsDNA antibodies. Cluster 2 consisted of anti-dsDNA, anti-chromatin, anti-ribosomal P, anti-U1RNP, anti-Sm, anti-Ro and anti-La AA. Cluster 3 consisted of anti-dsDNA, anti-RNP and anti-Sm AA. The highest proportion of Caucasians was in cluster 1 (p < 0.05) which was characterized by a mild disease with infrequent major organ involvement compared to cluster 2 which had the highest frequency of nephritis, renal failure, serositis and hemolytic anemia, or cluster 3 which was characterized by frequent CNS disease and nephritis. This study demonstrated ethnic differences in AA profiles in pSLE. AA tended to cluster together and the clusters predicted subsequent clinical course.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".