Bibliometric analysis of inflammatory bowel disease and emotional factors: trends, impact, and emerging research areas
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, is a chronic gastrointestinal condition influenced by genetic, environmental, and immunological factors. Emerging evidence underscores the significant role of emotional factors in the onset, progression, and management of IBD. This bidirectional relationship necessitates a multidisciplinary approach that integrates psychological care into IBD management strategies. This study employs bibliometric analysis to provide a comprehensive overview of the research landscape on IBD and emotional factors. Methods: We used the Web of Science Core Collection to search for pertinent publications. To conduct the analyses, we utilized tools like VOS Viewer, CiteSpace, and Biblioshiny. Results: Research in this field has shown exponential growth, with annual publications increasing from fewer than five in the 1980s to 193 in 2023. The United States leads in research output (521 publications) and collaboration centrality (0.72), followed by England and Canada. The University of Manitoba is the top contributing institution, and Charles N. Bernstein emerged as the most prolific author. Journals like Journal of Crohn’s & Colitis and Inflammatory Bowel Diseases were pivotal in disseminating research. Cocitation analysis revealed Antonina Mikocka-Walus and Charles N. Bernstein as influential contributors to the field. Conclusion: The field of IBD and emotional factors is experiencing rapid growth, driven by increasing recognition of the psychological dimensions of IBD management. While significant progress has been made, gaps remain in understanding the underlying mechanisms and developing integrative therapeutic approaches. Future research should focus on longitudinal studies, interdisciplinary collaboration, and the incorporation of emotional health into personalized treatment strategies.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.223 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".