Higher comorbidities associated with less improvement in disease activity in early RA: results from CATCH cohort
Bibliographic record
Abstract
OBJECTIVES: Comorbidities negatively influence remission rates in RA. This study estimated the effects of comorbidities on components of disease activity in early RA (ERA). METHODS: Using the Rheumatic Disease Comorbidity Index (RDCI), the influence of comorbidities on trajectories of components of the SDAI (Simple Disease Activity Index) was assessed in participants with ERA enrolled in Canadian Early Arthritis Cohort (CATCH) over the first year of treatment. Adjusted effects of RDCI scores categorized 0, 1, 2 and ≥3 on SDAI trajectories over time were analysed using multivariable generalized estimating equations (GEEs) models adjusted for multiple confounders. RESULTS: ERA participants (N = 2248) had a mean (S.D.) symptom duration of 5.7 (3) months; mean age 55 (15) years and 72% were female. Baseline SDAI was 29 (15), with 90% having moderate-high SDAI. Baseline RDCI scores were 0 in 888 (40%), 1 in 547 (24%), 2 in 451 (20%) and ≥3 in 362 (16%). While baseline disease activity was similar across comorbidity groups, patients with higher RDCI scores showed worse SDAI trajectories over the first year of RA treatment. Higher RDCI scores were independently associated with pain, patient and physician global assessments over time. CONCLUSION: This large real-world analysis of ERA patients seen in routine rheumatology practice across Canada showed that while RA disease activity across comorbidity groups at diagnosis was similar, higher comorbidity was associated with slower improvement in RA disease activity over the first year of treatment, likely driven by independent associations with patient and physician global assessments and pain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".