Research Trends of Rheumatoid Arthritis and Depression from 2019 to 2023: A Bibliometric Analysis
Bibliographic record
Abstract
Yan Zhao,1,2 Guang-Yao Chen,3 Meng Fang1,2 1Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, People’s Republic of China; 3Department of TCM Rheumatology, China-Japan Friendship Hospital, Beijing, 100029, People’s Republic of ChinaCorrespondence: Guang-Yao Chen; Meng Fang, Email chenguangyao1994@163.com; fangmeng_fm@mail.ccmu.edu.cnBackground: 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.Keywords: rheumatoid arthritis, depression, bibliometric analysis, Web of Science, CiteSpace
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.107 | 0.144 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".