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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".