High Disease Activity Is Associated with Self-reported Depression and Predicts Persistent Depression in Early Rheumatoid Arthritis: Results from the Ontario Best Practices Research Initiative
Bibliographic record
Abstract
OBJECTIVE: We sought to determine if initial high disease activity or changes in disease activity contribute to persistent depression in early rheumatoid arthritis (ERA). We also determined if disease activity and depression is modified by sex. METHODS: Depression was ascertained by self-report among patients enrolled in the Ontario Best Practices Research Initiative. The association between baseline disease activity, measured by the Clinical Disease Activity Index (CDAI), and persistent depression was evaluated with multivariate regression models, and effect modification by sex was tested. A general estimating equation assessed the association between change in CDAI over time and risk of depression. RESULTS: The sample of 469 ERA subjects was predominantly female (73%). At baseline, the prevalence of depression was 26%, and 23% reported persistent depression. After adjusting for potential confounders, higher baseline CDAI was associated with both baseline and persistent depression (OR 1.03, 95% CI 1.01-1.05). Female sex was an effect modifier of this relationship (OR 1.04, 95% CI 1.01-1.06). Maintaining a moderate or high CDAI score over 2 years also increased the risk of future depression. CONCLUSION: Depression in ERA is common and initial high disease activity is associated with the probability of depression and its persistence. This risk seems particularly modified in women with active disease and represents an area for targeted focus and screening. Future studies in ERA are needed to determine if intervening during the "window of opportunity" to control disease activity has the potential to mitigate the development and maintenance of adverse mental health outcomes, including depression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".