Visualisation of research hotspots in the surgical management of inflammatory bowel disease based on the web of science database
Bibliographic record
Abstract
Objective In recent years, there has been a continuous growth in the number of publications related to surgical treatment of IBD globally. However, there is currently a scarcity of bibliometric analyses based on VOSviewer to evaluate the past and present global research in this field. This study aims to analyze the bibliometric characteristics of papers related to IBD surgery to reveal research hotspots and trends in this domain. Methods As of August 31, 2024, we retrospectively collected scientific papers on IBD surgery published in the Web of Science Core Collection. Bibliometric metadata from each selected paper was extracted for analysis. VOSviewer was utilized to visualize the results. Results A total of 6,239 papers met the inclusion criteria. The United States exhibited the highest total link strength and published the most papers ( n = 2,334). The University of Toronto's Temerty Faculty of Medicine was the most prolific institution ( n = 102), while Professor Shen Bo authored the most papers ( n = 120). The journal “INFLAMMATORY BOWEL DISEASES” published the highest number of relevant papers ( n = 741). Based on co-occurrence data, keywords were categorized into five clusters, with Cluster 1 and Cluster 2 containing the most prominent keywords. Conclusion In this study, a bibliometric analysis of IBD surgery research was conducted using VOSviewer. USA emerged as the leading country in this field and “INFLAMMATORY BOWEL DISEASES” is the most influential journal in this field. Scientists and research institutes all over the world should transcend national boundaries and establish deeper collaboration. The main focus of the research is on the use of robotic surgery in the treatment of IBD, which is essential to expand our understanding of surgical treatment of IBD and to optimise treatment outcomes. Further research in this area could greatly improve the effectiveness of personalised therapies.
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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 | 0.133 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.074 | 0.198 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".