Ethnic Differences in Pediatric Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Prevalence and severity of systemic lupus erythematosus (SLE) in adults is suggested to be distinctly different between ethnic groups. The impact of ethnicity is not as well delineated in pediatric SLE (pSLE). We compared prevalence and extent of major organ involvement, disease activity, and damage in pSLE between different ethnic groups. METHODS: Ethnic demographic profiles of an inception cohort of 265 patients with pSLE followed at Sick Kids Hospital in Toronto were determined and compared to the Metropolitan Toronto at-risk population. Patients were categorized into ethnic subsets based on self-designated ethnic origins. Disease characteristics including major organ involvement, disease activity, and damage measures were longitudinally determined and compared among ethnic groups. RESULTS: Ethnicity data were available on 259/265 pSLE patients (99.6%); the majority were non-Caucasian (60%) compared to the Metropolitan Toronto at-risk population (40%) (p < 0.0001). Non-Caucasian patients were younger at diagnosis than Caucasian patients, Black patients being the youngest at diagnosis (12.6 vs 14.6 yrs; p = 0.007). Renal disease was significantly more common in non-Caucasian than in Caucasian pSLE patients (62% vs 45%; p = 0.01). There was a trend toward increased prevalence of central nervous system disease in Black patients compared to Asian patients (p = 0.108). There was no difference in gender ratio, SLE Disease Activity Index, or damage scores between ethnic groups. CONCLUSION: Non-Caucasian ethnicity is associated with increased pSLE disease prevalence. Non-Caucasian pSLE patients were significantly younger and more likely to have nephritis. However, disease activity and damage were strongly associated with major organ disease independent of the patient's ethnicity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".