Heterogeneity in Patient Characteristics and Differences in Treatment Across 4 Canadian Rheumatoid Arthritis Cohorts
Bibliographic record
Abstract
OBJECTIVE: To compare clinical characteristics and treatment of patients with rheumatoid arthritis (RA) across 4 Canadian cohorts. METHODS: The 4 longitudinal cohorts included the following: the Canadian Early Arthritis Cohort (CATCH; n = 2878), Ontario Best Practices Research Initiative (OBRI; n = 3734), RHUMADATA (Quebec, n = 2890), and the Rheum4U Precision Health Registry (Calgary, Alberta, n = 709). Data were from cohort inception (range 1998-2016) to 2020. Clinical characteristics and drug treatments were summarized descriptively. RESULTS: In total, 10,211 patients with RA were included. The percentage of patients who entered the cohort with early RA (2 yrs of disease at enrollment) ranged from 29% (Rheum4U) to 100% (CATCH). Mean age (55 yrs), sex (74% female), and seropositivity (69%) were similar between cohorts. At the time of initial disease-modifying antirheumatic drug (DMARD) use, median Disease Activity Score in 28 joints (DAS28) varied, ranging from 2.99 (Rheum4U) to 5.19 (CATCH), but were more similar at the time of the first DMARD switch (range 3.57-5.03), first biologic (bDMARD) or targeted synthetic DMARD (tsDMARD) use (range 4.01-4.67), and second bDMARD or tsDMARD (range 3.71-4.39). The initial DMARD was most commonly methotrexate, either in monotherapy (32%, range 18-40%) or dual therapy (34%, range 29-42%). The first DMARD switch was to another DMARD monotherapy in 20% (range 10-32%), dual therapy in 49% (range 39-56%), and bDMARD or tsDMARD in 24% (range 15-28%). The first bDMARD was an anti-tumor necrosis factor in 79% (range 78-82%). CONCLUSION: Canadian RA cohorts demonstrate some heterogeneity in treatment, which could reflect differences in inclusion criteria, calendar year, or regional differences. This project is a first step toward conducting harmonized analyses across Canadian RA cohorts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".