Bibliometric analysis of global rabies research between 1992 -2022
Bibliographic record
Abstract
Rabies is a deadly viral infection for which there is still no definitive cure. Many researchers are making publication on this subject. The current study used bibliometric techniques to examine the rabies literature and highlighted current rabies research trends as well as prospective future hotspots for rabies research. In this bibliometric study, all data were retrieved from the Web of Science Science Citation Index-Expanded (SCI-E) database on January 1, 2023, using the selected terms ("rabies virus" [MeSH Terms] OR "rabies virus" [Text Word] OR "rabies" [MeSH Terms] OR "rabies" [Text Word]) in the title field of the search engine. The search was further narrowed by the document type (article), language (English), and year of publication (1992–2022). According to the used search strategy, we reached a total of 5973 articles. The average number of citations per document was 21.3. Over 300 articles per year were published in the years 2020, 2021, 2019, 2018, and 2017. The rabies literature was written by authors from 158 different countries. The main countries with the highest number of articles on rabies were the USA, China, and France. Germany, India, Brazil, England, Japan, and Canada Research collaboration and cooperation between institutions and researchers in developing countries need to be supported by developed countries. The analysis provides information on the overall situation of rabies research worldwide. The analysis also provides a better understanding of the trends in rabies development over the past 30 years, which can serve as a scientific benchmark for subsequent studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.124 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".