S1106 Where Is the Science? Dissecting Geographic and Temporal Gaps in IBS Research
Bibliographic record
Abstract
Introduction: Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder characterized by abdominal discomfort and altered bowel habits. While global interest in IBS has grown over the past decades, disparities in research productivity and citation impact remain underexplored. Understanding these trends is crucial to identify gaps in knowledge dissemination and global equity in research. Methods: A bibliometric analysis was performed using the Web of Science Core Collection. The top most cited 1,000 IBS-related publications, spanning from 1965 to 2024, were analyzed using the Bibliometrix R package. Key metrics included publication volume, top contributing countries, institutional affiliations, journal sources, and citation counts. Results: Initially, a total of 16,566 publications from 1965 to 2024 were identified, of which 6,308 excluded as Meeting Abstract. The 1,000 most cited articles from the remaining 10,258 publications were selected for analysis, spanning 170 journals. The United States led with 344 publications (34.4%), followed by the United Kingdom (UK) (179), Sweden (45), Canada (42), and Australia (38). These 5 countries accounted for over 60% of all publications, highlighting a concentration of IBS research in high-income regions. The top institutions included University of California Los Angeles (USA), University of North Carolina (USA), and University of Manchester (UK). Most publications appeared in American Journal of Gastroenterology (141 articles), Gastroenterology (128) and Gut (109). International collaboration was moderate, with the UK and Sweden showing the highest multi-country publication percentages (25%–35%). Conversely, low- and middle-income countries were significantly underrepresented, contributing fewer than 10% of publications. Conclusion: IBS-related research remains highly concentrated in developed nations, with limited contributions from underrepresented regions. Enhancing global collaboration, increasing research capacity in low-resource settings, and promoting equitable authorship practices are critical to ensuring a more inclusive and representative body of IBS literature. Greater engagement from low- and middle-income countries is essential to better understand diverse populations and phenotypes of IBS, uncover culturally specific diagnostic and management strategies, and generate insights from different clinical perspectives.
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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 | 0.036 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.047 | 0.081 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".