Prevalence, Severity, and Measures of Anxiety in Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Many studies have reported high rates of anxiety in adults with rheumatoid arthritis (RA). The aim of this systematic review was to examine those findings and determine the overall prevalence, severity, and commonly used measures of anxiety in individuals with RA. METHODS: Six databases were searched from January 2000 without restrictions on language/location, study design, or gray literature. All identified studies that examined anxiety prevalence and severity in adults with RA, as assessed with clinical diagnostic interview and/or standardized self-report measures, were considered for inclusion. Quality assessment of included studies was conducted using a modified Newcastle-Ottawa Evaluation Scale, and the findings were synthesized via a narrative approach. RESULTS: Across the 47 studies (n = 11,085 participants), the sample size ranged from 60 to 1,321 participants with seven studies including healthy controls or groups with other health conditions. The studies were conducted across 23 countries, and anxiety prevalence ranged from 2.4% to 77%, predominantly determined with standardized self-report measures, of which Hospital Anxiety and Depression scale was used most frequently; only eight studies used a clinical diagnostic interview to confirm a specific anxiety diagnosis. Notable associations with anxiety in RA were physical disability, pain, disease activity, depression, and quality of life. CONCLUSION: The reported prevalence of anxiety in RA varied widely potentially because of use of different self-report measures and cutoff points. Such cutoff points will need to be standardized to clinical thresholds to inform appropriate interventions for anxiety comorbidity in 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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".