Bibliometric analysis of oncolytic virus research, 2000 to 2018
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence in the last decade has pointed to the effectiveness of oncolytic virus in the treatment of a variety of cancer types in preclinical or clinical studies, showing high potency and low toxicity compared to conventional treatments. To track this research trend and highlight future directions, we conducted a bibliometric analysis of oncolytic virus research to date. METHODS: Relevant studies were obtained from the Web of Science Core Collection between January 2000 and December 2018. Data were collected in terms of the number of publications, country, journal of publication, journal scope, author, and keywords or topics. Analysis and visual representation of the data were performed with CiteSpace V. RESULTS: The trend in publications related to oncolytic virus showed a dramatic increase, from 10 publications in 2000 to 199 publications in 2018. The United States clearly dominates this field (981 publications, 52.770%), followed by Canada (244, 13.125%) and China (205, 11.027%). The top 15 academic journals account for over one third of the total publications on oncolytic virus research (724, 38.95%). Most of the related papers were published in journals with a focus on biology, medicine, immunology, medicine, molecular biology, and clinical perspectives, as represented by the dual-map overlay. The most highly cited papers were published in journals in the fields of nursing, molecular biology, general biology, genetics, health, and medicine. Over 1300 institutions have focused their attention on oncolytic virus research to date, and cooperation among mainstream institutions is common. CONCLUSION: The global field of oncolytic virus research has expanded at a rapid pace from 2000 to 2018. There is no doubt that North America currently has the most powerful impact on the field with respect to both productivity and contribution. However, European and some East Asian institutions are also prominent in this field. Overall, this bibliometric study identifies the top 4 hotspots in oncolytic virus research: T-cells, vaccinia virus, dendritic cells, and apoptosis. Thus, further research focuses on these topics may be more helpful to promote the clinical translation of this treatment strategy to bring a benefit to cancer patients in the near future.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.137 | 0.206 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".