Scientometric Assessment of Global Research Output about Monkeypox during 1970–2022
Bibliographic record
Abstract
Introduction and Methodology: The authors used bibliometric analysis and visualization tools to analyze 950 documents on monkeypox with a focus on annual publication productivity, most productive and impactful source titles, countries, most productive institutions, and authors, and their contribution to the subject areas. Results and Conclusion: The findings reveal that the most productive year of publication was 2003 which included 62 publications with the majority published in Emerging Infectious Diseases (35 papers) and Journal of Virology (32 papers) and Science (60.6), and Morbidity and Mortality Weekly Reports (60.44) achieved the highest citation impact per paper. USA (55.37% share), UK (6.63% share), and Germany (6.11% share) are the most productive countries, and Switzerland (47.93 and 1.84), Canada (46.57 and 1.79), and Belgium (37.32 and 1.43) are the most impactful countries. The Centers for Disease Control & Prevention, USA (145 papers) and National Institutes of Health, USA (64 papers) emerged as the most productive, and the University of California, Los Angeles, USA (84.15 and 3.23) and Viral and Rickettsial Zoonoses Br., USA (66.9 and 2.57) as the most impactful organizations. I.K. Damon (68 papers) and M.G. Reynolds (51 papers) emerged as the most prolific authors, and Esposito, J.J. (60.0 and 2.30) and Jezek, Z. (56.89 and 2.18) as the most impactful authors. In the subject category type, the most prominent subject fields were Medicine (60.95% share) and Immunology & Microbiology (39.16% share). The prominent keywords in the papers were “monkeypox” (570 times), “monkeypox virus” (411 times), “poxviridae infections” (332 times), “small pox” (266 times), “orthopox virus” (248 times), “vaccinia virus” (203 times), and “disease outbreaks” (179 times).
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 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.070 | 0.105 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".