Research hotspots and trend analysis of abdominal pain in inflammatory bowel disease: a bibliometric and visualized analysis
Bibliographic record
Abstract
Aims: The study aimed to provide a bibliometric and visual analysis of research on abdominal pain in inflammatory bowel disease and discuss the current status, research hotspots, and future developments. Methods: We used the Web of Science Core Collection to comprehensively search the literature on abdominal pain-related research in IBD published between 2003 and 2022. The bibliometric and visual analysis was performed through CiteSpace, VOSviewer software, R language, and the bibliometric online analysis platform, including authors, institutions, countries, journals, references, and keywords in the literature. Results: A total of 3,503 relevant articles are included, indicating that the number of articles in this field has increased in recent years. The United States leads the way with a dominant position in terms of article output, followed by China and JAPAN. United States (967 articles), University of Calgary (98 articles), and World Journal of Gastroenterology (127 articles) are the top publishing countries, institutions, and journals, respectively; keyword analysis shows that gut microbiota, depression, stress, visceral hypersensitivity, and multidisciplinary approach are the hot spots and trends in this research area. Conclusion: Abdominal pain-related studies in IBD have received increasing attention in the past two decades. This study provides the first bibliometric analysis of papers in this research area using visualization software and data information mining. It provides insights into this field’s current status, hot spots, and trends. However, many outstanding issues in this research area still need further exploration to provide a theoretical basis for its clinical application.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.146 | 0.142 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".