What can we learn from top-cited articles in inflammatory bowel disease? A bibliometric analysis and assessment of the level of evidence
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite increasing number of publications in inflammatory bowel disease (IBD), no bibliometric analysis has been conducted to evaluate the significance of highly cited articles. Our objectives were to identify the top-cited articles in IBD, assessing their characteristics and determining the quality of evidence provided by these articles. DESIGN AND OUTCOME MEASURES: IBD and related terms were used in searching the Web of Science to identify English language articles. The 50 top-cited articles were analysed by year, journal impact factor (JIF), authorship, females in authorship, institute, country and grants received. The level of evidence was determined using the Oxford Centre for Evidence-Based Medicine guidelines. RESULTS: The number of citations varied from 871 to 3555 with a total of 74 638, and a median 1339.50 (IQR=587). No correlations were found between the number of citations and number of years since publication (r=0.042, p=0.771), JIF (r=0.186, p=0.196), number of authors (r=0.061, p=0.674), females in authorship (r=0.064, p=0.661), number of institutes (r=0.076, p=0.602), number of countries (r=0.101, p=0.483) or number of grants (r=-0.015, p=0.915). The first authors were from the USA (n=24), the UK (n=6), Germany (n=5), France (n=5), Belgium (n=3) and Canada (n=3). The levels of evidence were 12 articles at level 1b, 9 articles at level 3a and 15 articles at level 3b and fewer were at other levels. CONCLUSIONS: Research papers represented 66% of articles. The majority of items have reasonably high levels of evidence, which may have contributed to the higher number of citations. The study also shows a gender gap in authorship in this area.
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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 | 0.072 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.196 | 0.166 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".