Trends of denosumab-related publications in web of science
Bibliographic record
Abstract
Denosumab is a human monoclonal antibody that targets nuclear factor-kappa B ligand and is highly effective in blocking bone resorption. Bibliometrics can intuitively show the research development process, research status, research hotspots and development trend of a certain topic for researchers. This study assessed the course of research and development for denosumab in terms of publications over the past 2 decades. Web of Science databases were searched to identify publications related to research on denosumab from January 1, 2005 to December 31, 2022. The VOS Viewer software (version 1.6.17) and Bibliometrix package in R (version 4.1.3) were used in this study. There were 5119 denosumab-related publications during this period. The total number of citations of denosumab-related publications reached 94917. The most articles were published in the field of Endocrinology Metabolism. As an international language, English remains the most popular language for writing papers. Five of the top ten institutions originated in the USA. Through the VOS Viewer analysis, we found that the relationships between Amgen Inc. with its collaborations were grouped into 4 clusters, the USA was the mainland for research and development on denosumab, closely collaborating with many other countries, such as Canada, Japan, England, and China. Wagman RB from USA was the most prolific author with 119 publications. The journal with the most publications was Osteoporosis International (481 publications). The most cited article was "Denosumab for Prevention of Fractures in Postmenopausal Women with Osteoporosis" with 2053 citations. The clinical trial comprised 6 of the 10 most frequently cited publications, and the rest consisted of reviews. The most frequent keywords for publications since January 1, 2014 were "prevention" and "management," indicating that a number of prevention and management measures have been developed to regulate the use of denosumab in treating osteoporosis. Our research provided a comprehensive review of denosumab-related publications, suggesting that the development of denosumab is a long process and numerous clinical trials have been conducted before applications in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.070 | 0.131 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".