ArticleFrontiers in pharmacology2022
A bibliometric research based on hotspots and frontier trends of denosumab.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- A bibliometrics and visualization analysis of ropivacaine research from 2000 to 2023.Frontiers in medicine · 2024Pooled it
- Research Trends in Immunomodulatory Therapy for Postmenopausal Osteoporosis: A Bibliometric Analysis.Orthopaedic surgery · 2025Review
- Denosumab combined with microwave ablation excisional scraping for giant cell tumor of the thoracic spine: a case report and literature review.Frontiers in oncology · 2024Article
- Denosumab for giant cell tumors of bone from 2010 to 2022: a bibliometric analysis.Clinical and experimental medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Denosumab is a monoclonal antibody that targets and inhibits the osteoclast activating factor receptor activator for nuclear factor-κB ligand (RANKL). It has been widely used in the treatment of osteoporosis, giant cell tumors of bone, and in the prevention of malignant skeletal-related events (SREs). We collected the research results and related MeSH terms of denosumab from 2011 to 2021 through the Web of Science and PubMed, respectively. The literature was visualized and analyzed by CiteSpace and bibliometric online analysis platforms. The MeSH terms were biclustered using the Bibliographic Co-Occurrence Analysis System (BICOMB) and graph clustering toolkit (gCLUTO). The results show that the number of denosumab-related annual publications had increased from 51 to 215, with the United States leading and Amgen Inc. being the most influential in the past 10 years. Articles published in the Journal of Bone and Mineral Research had the highest total citations. Three scholars from Shinshu University in Matsumoto, Yukio Nakamura, Takako Suzuki, and Hiroyuki Kato, joined the field relatively late but produced the most. The clinical comparison and combination of denosumab with other drugs in the treatment of osteoporosis was the most significant focus of research. Drug withdrawal rebound and management strategies have gained more attention and controversy recently. MeSH analysis revealed eight major categories of research hotspots. Among them, exploring the multiple roles of the RANK-RANKL-OPG system in tumor progression, metastasis, and other diseases is the potential direction of future mechanism research. It is a valuable surgical topic to optimize the perioperative drug administration strategy for internal spinal fixation and orthopedic prosthesis implantation. Taken together, the advantages of denosumab were broad and cost-effective. However, there were still problems such as osteonecrosis of the jaw, severe hypocalcemia, a high recurrence rate of giant cells in the treatment of bone and individual sarcoidosis, and atypical femoral fractures, which need to be adequately solved.
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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.