Frontiers of cervical cancer research: an analysis from the top 100 most influential articles in the field
Liang, L.; Song, C.; Chang, C.; Chen, S.; Yang, B.; Sun, L.
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BackgroungCervical cancer (CC) is one of the common gynecological malignancies, which has a serious negative impact on the quality of life of patients, and brings huge economic pressure and burden to society. The most effective treatment for CC has become an important issue worldwide. MethodIn order to determine the current research hotspots and development trends in a particular field, we conducted a bibliometric analysis of the top 100 cited papers. To achieve this, we searched for the top 100 most cited papers on the Science Citation Index Expanded (SCI Expanded) on the Web of Science (WOS), based on the CCS (citation count score). We then reviewed relevant literature from different years, countries/regions, institutions, authors, keywords, and references. Using VOS viewer and Cite Space software, we constructed a knowledge map, and finally compiled the relevant information we retrieved from the literature using Excel. Through this process, we were able to make predictions about the current focus and trends in the field. ResultsBetween 2013 and 2023, numerous journals published the top 100 cited research papers. The International Agency for Research on Cancer and the American Cancer Society were the most cited institutions. The United States contributed the most papers, followed by France and England. The top 5 keyword co-occurrences were cervical cancer, women, human papillomavirus, United States, and colorectal cancer. Cluster analysis results suggest that future research in CCS may focus on prophylaxis and HPV vaccination. ConclusionBibliometric analysis can quickly and intuitively identify the focus and boundaries of cervical cancer research. Our findings suggest that prophylaxis and HPV vaccination may be the focus and trend for future research on cervical cancer.
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.