Depression and the risk of rheumatoid arthritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Rheumatoid arthritis (RA) is associated with negative changes in mental health. This is generally attributed to symptoms of inflammation and the adverse impact of RA on quality of life and functioning. Until recently, causal pathways in the opposite direction have not been fully appreciated. This review examines the recent literature on the risk of RA associated with depression. RECENT FINDINGS: Current literature links depression with an increased risk of RA and with a more detrimental disease course. These effects are likely to be partially mediated by negative effects of depression on coping with RA and on factors such as medication adherence, both of which lead to poorer disease outcomes. Growing evidence also suggests that inflammation is central both to depression and RA and may account for some of the complex interplay between these conditions. SUMMARY: Awareness of a bidirectional relationship between depression and RA through a biopsychosocial framework may assist clinicians in maintaining an appropriate index of suspicion about the co-occurrence of these conditions. This review also suggests an important need for integration of rheumatologic and mental health services and generates hypotheses for future research towards a better understanding of both depression and RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".