The 1000 Canadian Faces of Lupus: Determinants of Disease Outcome in a Large Multiethnic Cohort
Bibliographic record
Abstract
OBJECTIVE: To describe disease expression and damage accrual in systemic lupus erythematosus (SLE), and determine the influence of ethnicity and socioeconomic factors on damage accrual in a large multiethnic Canadian cohort. METHODS: Adults with SLE were enrolled in a multicenter cohort. Data on sociodemographic factors, diagnostic criteria, disease activity, autoantibodies, treatment, and damage were collected using standardized tools, and results were compared across ethnic groups. We analyzed baseline data, testing for differences in sociodemographic and clinical factors, between the different ethnic groups, in univariate analyses; significant variables from univariate analyses were included in multivariate regression models examining for differences between ethnic groups, related to damage scores. RESULTS: We studied 1416 patients, including 826 Caucasians, 249 Asians, 122 Afro-Caribbeans, and 73 Aboriginals. Although the overall number of American College of Rheumatology criteria in different ethnic groups was similar, there were differences in individual manifestations and autoantibody profiles. Asian and Afro-Caribbean patients had more frequent renal involvement and more exposure to immunosuppressives. Aboriginal patients had high frequencies of antiphospholipid antibodies and high rates of comorbidity, but disease manifestations similar to Caucasians. Asian patients had the youngest age at onset and the lowest damage scores. Aboriginals had the least education and lowest incomes. The final regression model (R2=0.27) for higher damage score included older age, longer disease duration, low income, prednisone treatment, higher disease activity, and cyclophosphamide treatment. CONCLUSION: There are differences in lupus phenotypes between ethnic populations. Although ethnicity was not found to be a significant independent predictor of damage accrual, low income was.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".