Influence of ethnicity on childhood‐onset systemic lupus erythematosus: Results from a multiethnic multicenter Canadian cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of ethnicity and sociodemographic factors on disease characteristics of the Canadian pediatric lupus population. METHODS: Childhood-onset systemic lupus erythematosus (SLE) patients at 4 pediatric centers in Halifax, Montreal, Toronto, and Vancouver were consecutively recruited. Sociodemographics and disease data were collected. Patients were categorized by their primary self-selected ethnicity, and exploratory cluster analyses were examined for disease expression by ethnicity. RESULTS: We enrolled 213 childhood-onset SLE patients, and ethnicity data were available for 206 patients: white (31%), Asian (30%), South Asian (15%), black (10%), Latino/Hispanic (4%), Aboriginal (4%), and Arab/Middle Eastern (3%). The frequency of clinical classification criteria (malar rash, arthritis, serositis, and renal disease) and autoantibodies significantly differed among ethnicities. Medications were prescribed equally across ethnicities: 76% were taking prednisone, 86% antimalarials, and 56% required additional immunosuppressants. Cluster analysis partitioned into 3 main groups: mild (n = 50), moderate (n = 82), and severe (n = 68) disease clusters. Only 20% of white patients were in the severe cluster compared to 51% of Asian and 41% of black patients (P = 0.03). However, disease activity indices and damage scores were similar across ethnicities. CONCLUSION: Canadian childhood-onset SLE patients reflect our multiethnic population, with differences in disease manifestations, autoantibody profiles, and severity of disease expression by ethnicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".