Web of Science‐Based Visualization of Mapping Global Scientific Production on Epstein–Barr Virus‐Associated Autoimmune Diseases: A Bibliometric Study
Bibliographic record
Abstract
ABSTRACT Background and Aims Epstein–Barr virus (EBV) has been implicated in autoimmune diseases (AIDs), yet a comprehensive analysis of global research trends, knowledge gaps, and translational opportunities remains lacking. Therefore, we aimed to study the research output of EBV‐associated AIDs globally. Methods All publications related to EBV‐associated AIDs from 1993 to 2023 were collected from the Science Citation Index‐Expanded of Web of Science. Subsequently, the data were evaluated using the bibliometric methodology. Bibliometrix package in R software was used for data retrieval. VOSviewer and CiteSpace were used to visualize the research focus and trend regarding the effect of EBV‐associated AIDs. Results We analyzed 1589 publications to explore the global scientific landscape on EBV‐associated AIDs. Growth in publications exhibited two peaks, with post‐2020 acceleration coinciding with increased interest in EBV's immunological role. The USA exhibited the highest publications with 543 publications, many of which investigated molecular pathways such as lipid metabolism in EBV‐associated AIDs. Then, Italy ( n = 161) and Japan ( n = 140) took the second and third places, respectively. Among the institutions involved, Tel Aviv University provided the biggest nodes in each cluster of the cooperation network. The most frequently cited author in the field, according to our results, was Shoenfeld Y. Finally, the results of keyword co‐occurrence analysis showed that systemic lupus erythematosus and rheumatoid arthritis are the most extensively investigated topics in this study area. Conclusion This study highlights pivotal milestones in EBV‐AIDs research and proposes future directions, including genetic–host immune system interaction, prevention trials, and collaborative mechanisms. Prioritizing these emerging hotspots could advance therapeutic strategies and interdisciplinary synergies.
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How this classification was reachedexpand
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.208 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".