Self-reported comorbidity is common in early inflammatory arthritis and associated with poorer function and worse arthritis disease outcomes: results from the Canadian Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: Comorbid medical conditions may influence treatment and contribute to poor outcomes in early RA. We aimed to assess the association of baseline comorbidity with outcomes in early inflammatory arthritis using data from the Canadian Early Arthritis Cohort. METHODS: Patients (n = 2090) with early inflammatory arthritis (symptom duration of < 1 year) reported comorbid medical conditions at baseline. Functional status (HAQ), detailed clinical assessments and treatment were assessed. Treatment is not protocolized but participating rheumatologists aim for remission. The influence of comorbidity on clinical outcomes was determined using multivariate models. RESULTS: At least one comorbid condition was reported by 76% of patients. Patients with comorbidity were older (mean age 56 vs 44 years, P < 0.0001) and had worse baseline function [median (interquartile range, IQR) HAQ score (0.88 (1) vs 0.75(1), P < 0.0001] compared with those without comorbidity even after controlling for age, sex and symptom duration. At 1 year, patients with baseline comorbidity were less likely to achieve remission (odds ratio, OR = 0.67; 95% CI: 0.51, 0.88, P = 0.004) and had higher HAQ [median (IQR) 0.25 (1) vs 0 (0), P < 0.0001] and pain scores [median (IQR) 2.85 (4) (out of 10) vs 1 (4), P < 0.0001] than patients without comorbidity after adjusting for age, sex, symptom duration, baseline disease activity and arthritis treatment. CONCLUSION: Comorbidity is common in early inflammatory arthritis and associated with higher disease activity, worse functional status and greater pain scores during the first year of follow-up. While the mechanisms for this association require investigation, addressing comorbidity may improve clinical outcomes in early RA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".