Research Trends of Rheumatoid Arthritis and Depression from 2019 to 2023: A Bibliometric Analysis
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Bibliographic record
Abstract
Background: The co-occurrence of rheumatoid arthritis and depression typically exacerbates pain and leads to a range of adverse consequences, becoming a research hotspot in recent years. This study conducted the systematic retrieval of relevant articles within the past five years and employed bibliometric methods for scientometric analysis. Methods: Setting the keywords "Rheumatoid arthritis", "Depression" and "Depressive Disorder", relevant literature published between 2019 and 2023 was retrieved from the Web of Science database. Subsequently, the core information from the literature was subjected to visual analysis via CiteSpace software and bibliometric techniques. Results: A total of 974 articles related to rheumatoid arthritis and depression were identified through the search strategy, and 877 articles were retained for further analysis after duplicates. The United States (n=173), England (n=82), China (n=69), Canada (n=68), and Germany (n=54) ranked top five countries by publication count. The King's College London was the leading institution with the highest number of publications (n = 20). LANCET PSYCHIATRY was the most frequently cited journal (n = 72) despite having only one article. The top five authors with the largest number of publications include CHARLES N BERNSTEIN (n=14), RUTH ANN MARRIE (n=13), JOHN D FISK (n=12), CAROL A HITCHON (n=12) and SCOTT B PATTEN (n=12), and all these are based in Canada. The keywords with a centrality score exceeding 0.1 were depression, rheumatoid arthritis, symptom, quality of life, impact, fibromyalgia, disease activity, prevalence, inflammation, health, anxiety, pain, fatigue, disease, arthritis and disability. Conclusion: Related research between the co-occurrence of rheumatoid arthritis and depression was a persistent hotspot, but it still lacks of international collaboration and in-depth mechanistic exploration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.090 | 0.078 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it