A bibliometric review of oncolytic virus research as a novel approach for cancer therapy
Bibliographic record
Abstract
BACKGROUND: In recent years, oncolytic viruses (OVs) have drawn attention as a novel therapy to various types of cancers, both in clinical and preclinical cancer studies all around the world. Consequently, researchers have been actively working on enhancing cancer therapy since the early twentieth century. This study presents a systematic review of the literature on OVs, discusses underlying research clusters and, presents future directions of OVs research. METHODS: A total of 1626 published articles related to OVs as cancer therapy were obtained from the Web of Science (WoS) database published between January 2000 and March 2020. Various aspects of OVs research, including the countries/territories, institutions, journals, authors, citations, research areas, and content analysis to find trending and emerging topics, were analysed using the bibliometrix package in the R-software. RESULTS: In terms of the number of publications, the USA based researchers were the most productive (n = 611) followed by Chinese (n = 197), and Canadian (n = 153) researchers. The Molecular Therapy journal ranked first both in terms of the number of publications (n = 133) and local citations (n = 1384). The most prominent institution was Mayo Clinic from the USA (n = 117) followed by the University of Ottawa from Canada (n = 72), and the University of Helsinki from Finland (n = 63). The most impactful author was Bell J.C with the highest number of articles (n = 67) and total local citations (n = 885). The most impactful article was published in the Cell journal. In addition, the latest OVs research mainly builds on four research clusters. CONCLUSION: The domain of OVs research has increased at a rapid rate from 2000 to 2020. Based on the synthesis of reviewed studies, adenovirus, herpes simplex virus, reovirus, and Newcastle disease virus have shown potent anti-cancer activity. Developed countries such as the USA, Canada, the UK, and Finland were the most productive, hence, contributed most to this field. Further collaboration will help improve the clinical research translation of this therapy and bring benefits to cancer patients worldwide.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one teacher head, 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".