SynthesisFrontiers in oncology2026
A bibliometric analysis and visualization of trends in cervical cancer screening technologies based on dual-database analysis.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cervical cancer is a leading cause of gynecologic cancer mortality, especially in low- and middle-income countries. Early detection through screening is essential to reduce incidence and death rates. Advances in high-risk HPV molecular assays, liquid-based cytology (LBC), and AI-assisted diagnostics have transformed screening strategies. However, a systematic, quantitative assessment remains lacking. This study uses cross-database validation and bibliometrics to explore cervical cancer screening trends. It provides insights for clinical practice and future research. Methodology: This study is a bibliometric analysis rather than a traditional systematic review. We searched Web of Science and PubMed for cervical cancer screening articles from 2000 to 2024. Criteria included clinical trials, systematic reviews, meta-analyses, observational studies, and animal experiments. Conference abstracts and non-academic publications were excluded. After deduplication using CiteSpace, 640 unique articles from PubMed were cross-validated with WoSCC data. We used VOSviewer, CiteSpace, and R for data analysis. Co-occurrence and cluster analysis identified research trends. This bibliometric analysis was reported in accordance with the BIBLIO checklist and informed, where applicable, by the GLOBAL guidance for reporting bibliometric analyses. Objectives: The study analyzes research trends in cervical cancer screening using Web of Science and PubMed. It identifies high-frequency keywords, research collaboration networks, technological advances, and geographical patterns. Findings highlight global focus on HPV, cytology, and AI. These trends help guide scientific policy and clinical practices. Results: The study includes 1,160 articles from 2000 to 2024, showing a steady increase in publications. The US, China, and India lead in research output. Philip E. Castle is the most prolific author, and Harvard University has the highest citation rate. Core topics include "cervical cancer," "HPV," and "screening." Emerging trends involve "positive women," "impact," and "AI." This signals a shift toward precise and automated screening. Conclusion: The study demonstrates increased academic interest in cervical cancer screening technologies from 2000 to 2024. Research has focused on advancing screening methods, early detection, and effective program implementation. Future efforts should prioritize detection accuracy and enhance accessibility in low-resource settings. We must also standardize clinical protocols for cervical cancer prevention.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.