Exploring the evolution of evidence synthesis: a bibliometric analysis of umbrella reviews in medicine
Bibliographic record
Abstract
Background: Umbrella review studies have become increasingly vital in evidence synthesis, offering a comprehensive overview by analyzing multiple systematic reviews and meta-analyses. This bibliometric study aimed to delineate the growth and thematic evolution of umbrella reviews within evidence-based medicine, illuminating their integral role in synthesizing high-level evidence. Methods: Utilizing the Web of Science Core Collection, we performed a search for publications on umbrella reviews, identifying relevant articles through a refined strategy. Analytical tools including VOS Viewer and CiteSpace were employed to visualize connections and trends among the gathered data, converting intricate bibliometric information into comprehensible visual maps. Results: Our search yielded 2965 pertinent publications, highlighting a marked growth in research output, particularly from 2010 to 2023. The United States, United Kingdom, and China were predominant in this field, with leading institutions like King’s College London and the University of Toronto at the forefront. The analysis identified major journals such as BMJ Open and PLOS One as key publishers. Co-citation and keyword analysis revealed current research focuses, with recent trends emphasizing COVID-19 and mental health. The study also uncovered a robust international collaboration network, underscoring the global impact of umbrella reviews. Conclusion: This bibliometric analysis confirms the expanding influence and utility of umbrella reviews in medical research and decision-making. By charting the evolution and current trends in this field, our study not only showcases the geographical and institutional distribution of research but also guides future scholarly efforts to advance evidence synthesis methodologies.
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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 | 0.338 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.033 | 0.006 |
| Bibliometrics | 0.155 | 0.330 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".