The 1000 Canadian faces of systemic lupus erythematosus: effect of ethnicity on baseline pediatric data
Bibliographic record
Abstract
Childhood-onset SLE (<18 birthday) patients at four pediatric centers (Halifax, Montreal, Toronto and Vancouver). Collected data included sociodemographics, disease manifestations, current/past medications, laboratory measures and multiple disease measures. The Child Health Questionnaire (CHQ), measuring multiple domains of health status was administered. For analysis, patients were categorized by their primary self-selected ethnic category. Between November 2005 and February 2009, 213 cSLE patients were enrolled. The number of patients enrolled at each site mirrored the size of the clinical centre: Toronto 134 (63%), Vancouver 54 (25%), Montreal 17 (8%), and Halifax 8 (4%). There were 176 (83%) females, mean age at diagnosis was 12.5 ± 0.3 years, mean disease duration was 2.5 ± 2.7 years, and 175 patients (82%) were born in Canada. Demographic data were similar across the geographic sites, except for a longer disease duration in Vancouver (3.9 ± 3.6 years, p<.001). Primary self-reported race/ethnicity data was available for 191 patients: White (31%), Asian (30%), South Asian (15%), Black (10%), Latino/Hispanic (4%), Aboriginal (4%) and Arab/Middle Eastern (3%). Because of low numbers, the Latino/Hispanic and Arab/Middle Eastern groups were excluded from the analysis. Ethnic distribution across the centers differed (p<.001), reflecting known differences in the urban populations. The distribution of household income, and prescription drug plan coverage did not differ across ethnicities, however, fewer Asians (64%) had dental insurance coverage as compared to White (88%) and Aboriginal (100%) patients (p<.01). Missed school days did not differ by ethnicity, although 26% of the entire cohort reported missing on average 6 days per month. CHQ scores were lower in 7 of 10 domains in white patients vs. non-white ethnicities (p<.05 for each). Autoantibodies and SLE classification criteria present at any time that differed by ethnicity are listed in table 1 . Medications were prescribed equally across ethnicities; most patients were taking prednisone (75%), hydroxychloroquine (84%), and 56% required additional immunosuppression (azathioprine, methotrexate, mycophenolate mofetil or cyclophosphamide). Disease measures were similar across ethnicities, overall SLEDAI was 3.1 ± 4.2, SLAM 3.4 ± 3.7; SDI median 0.3 (range 0-5), and physician global VAS was 15 ± 23 (range 0-99). Canadian cSLE patients reflect our multi-ethnic population, with observed differences in disease manifestations, antibody profiles and health status 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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".