The Application of Rapid Review in the Field of Medical Research: a Bibliometric Analysis
Bibliographic record
Abstract
Background In the face of the surge of primary studies within a certain period of time, the traditional time-consuming systematic review method is difficult to provide evidence-based basis for clinical practice. Rapid review (RR), as an extension of systematic review, can integrate existing research in a limited time to meet the need for rapid decision-making. Currently, RR has been widely used in the field of medical research, but its application status remains unclear. Objective To explore the current status and hotspot of RR research by using bibliometric analysis. Method CNKI and WOS databases were searched for the researches in the field of RR application from 2001 to 2023, a visualization analysis was performed on annual publication volume, countries, institutions, authors, journals, and keywords of Chinese and English literature by the bibliometrics software of VOSviewer and CiteSpace. Results A total of 151 articles in Chinese and 1197 in English were included. The publication volume of RR application increased gradually from 2001 to 2023, but the publication volume in foreign was higher than that in China, with more obvious increasing trend. The United Kingdom was the country with the highest publication volume (252), the University of Toronto in Canada was the institution with the highest publication volume (52), and Peking University Third Hospital ranked first in publication volume in China (23). The journal Evaluation and Analysis of Drug-use in Hospitals of China had the highest publication volume in China (22), and the journal BMJ Open had the highest publication volume abroad (42). In China, the author team mainly composed of MEN Peng, ZHAI Suodi and ZHAO Zinan published more research. In foreign, the authors of NUSSBAUMER-STREIT, GARTLEHNER and TRICCO published more studies. The most frequently cited literature in China was mainly about RR application and methodology, rapid assessments of drugs or technologies, and the impact of COVID-19, while the most frequently cited literature in foreign was mainly about the the impact, intervention, and epidemiological factors of COVID-19, or methodological studies of RR. Domestic research hotspots mainly focused on the field of rapid health technology assessment in the safety, efficacy, and cost effectiveness of intervention for chronic or serious diseases. Foreign research hotspots mainly focused on the etiology, intervention, diagnosis, prevention, and impact of COVID-19, and rapid evidence synthesis related to decision-making, such as the safety and effectiveness of the drug intervention in children, health care, cancer treatment or mortality risk in middle-aged and elderly populations. Conclusion At present, there is a great difference in the development of RR application in the medical field at home and abroad. The application of RR in foreign is gradually maturing, but in China, it is still in the preliminary stage. The experience of RR application in foreign can be learned to expand the development of domestic RR application.
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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 | 0.159 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.166 | 0.196 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".