ReviewFrontiers in oncology2023
Bibliometric and visualized analysis of drug resistance in ovarian cancer from 2013 to 2022.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Past and present: a bibliometric study on the treatment of recurrent ovarian cancer.Frontiers in pharmacology · 2024Pooled it
- Trends in nanomedicine for colorectal cancer treatment: Bibliometric and visualization analysis (2010-2024).World journal of gastrointestinal oncology · 2025Article
- A two-decade bibliometric analysis of drug resistance in oral cancer research: patterns, trends, and future directions.Discover oncology · 2025Article
- CRABP2 affects chemotherapy resistance of ovarian cancer by regulating the expression of HIF1α.Cell death & disease · 2024Article
- Dual-Targeted Zeolitic Imidazolate Frameworks Drug Delivery System Reversing Cisplatin Resistance to Treat Resistant Ovarian Cancer.International journal of nanomedicine · 2024Article
- Application of medical imaging in ovarian cancer: a bibliometric analysis from 2000 to 2022.Frontiers in oncology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: As one of the cancers that seriously threatens women's health, ovarian cancer has a high morbidity and mortality rate. Surgery and chemotherapy are the basic treatment strategies for ovarian cancer, and chemotherapy resistance is a significant factor in affecting the prognosis, survival cycle, and recurrence of ovarian cancer. This article aims to analyze articles about ovarian cancer and drug resistance via bibliometric software, offering new ideas and directions for researchers in this field. Methods: Both Citespace and Vosviewer are bibliometric software on the Java platform. Articles were collected on ovarian cancer and drug resistance in the Web of Science Core Collection database from 2013 to 2022. The countries, institutions, journals, authors, keywords, and references were analyzed, and the development status of this field was indicated from multiple perspectives. Results: Studies on ovarian cancer and drug resistance generally showed an increasing trend from 2013 to 2022. The People's Republic of China and Chinese institutions contributed more to this field. Conclusions: Many studies on the mechanism of drug resistance in ovarian cancer have been discovered; however, the deeper mechanism remains to be explored. Compared with traditional chemotherapy drugs, PARP inhibitors and bevacizumab have shown better efficacy, but PARP inhibitors have initially shown drug resistance. The future direction of this field should be to overcome the resistance of existing drugs and actively develop new ones.
Indexed as
Identifiers
What OpenQuestion holds
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.