Data Mining of Systematic Reviews 1934-2023: A Bibliometric Analysis
Bibliographic record
Abstract
Introduction Systematic reviews consolidate evidence and drive clinical practice guidelines, cost-effective analyses, and policy decisions; therefore, their annual publication rate has increased significantly. We used bibliometric analysis to identify research trends, the most searched topics, authors and organizations productivity and collaboration, the research network, and research gaps by examining keywords frequency and systematic reviews distribution. Methods We searched the PubMed database for systematic reviews using the systematic review filter described by Salvador-Oliván and coauthors, which has higher recall than the PubMed SR filter. The search period was from 1934 until February 3, 2023. Microsoft Excel and the VOSviewer application were used for analyzing yearly trends, institutions, authors, and keywords, as well as to create tables and network figures. Results A total of 378,685 articles were published. The number of articles published has been rising steadily during the past five years. The University of Toronto and McMaster University in Canada (n = 1415 and n = 1386) were the leading contributory universities. “Genetic predisposition to disease”, “postoperative complications”, “neoplasm”, “stroke”, and “covid-19” were the top 5 occurring keywords that are particular to a specialty in systematic reviews. Conclusion This bibliometric research examined systematic reviews, publication trends, the majority of publishing disciplines, authors and organizations productivity, and collaborative efforts. The results of this study could prove to be an invaluable resource for researchers, policymakers, and healthcare professionals.
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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 | 0.107 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.215 | 0.270 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".