Does Socioeconomic Status Affect Outcomes in Early Inflammatory Arthritis? Data from a Canadian Multisite Suspected Rheumatoid Arthritis Inception Cohort
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of socioeconomic status (SES) on outcomes in patients with early inflammatory arthritis, using data from the Canadian Early Arthritis Cohort (CATCH) study. METHODS: In an incident cohort, 2023 patients were recruited, and allocated to low SES or high SES groups based on education and income. Outcomes at baseline and 12 months were analyzed in relation to SES including the 28-joint Disease Activity Score (DAS28), Simplified Disease Activity Index (SDAI), pain, patient's global assessment scale (PtGA), the Health Assessment Questionnaire-Disability Index (HAQ-DI), and the SF12-v2 Health Survey, using the ANOVA, chi-squared test, and regression analyses. RESULTS: The CATCH population had 43% with high school education or less and 37% in the low-income group (< 50,000 Can$ per annum household income). The low-education group had higher DAS28 at baseline (p = 0.045), becoming nonsignificant at 12 months and lower physical component score on SF12-v2 at baseline (p = 0.022). Patients in the low-income group presented with higher HAQ-DI (p = 0.017), pain (p = 0.035), PtGA (p = 0.004), and SDAI (p = 0.022). Low-income versus high-income groups were associated with an OR above the median for HAQ-DI (1.20; 95% CI 1.00-1.45), PtGA (1.27; 95% CI 1.06-1.53), and SDAI (1.25; 95% CI 1.02-1.52) at baseline. The association with low income persisted at 12 months for HAQ-DI (OR 1.30; 95% CI 1.02-1.67), but not for other variables. CONCLUSION: Low SES was initially associated with higher disease activity, pain, and PtGA, and poorer function. At 1 year, outcomes were similar to those with high SES, with the exception of HAQ-DI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".