A bibliometric analysis of the current state and future directions of osteoporosis pharmacological treatment
Bibliographic record
Abstract
Introduction: Osteoporosis is a major health threat, particularly with the aging population in China. Medication remains a cornerstone of management, and bibliometric analysis can provide insights into current research status and future directions. Methods: Relevant literature from the Science Citation Index Core Collection (2015-2024) was analyzed using bibliometric methods. Visual maps were generated with Citespace 6.3R3 and VOSviewer 1.6.19 to assess research trends and hotspots. Results: A total of 2,738 publications were included, showing a steady growth in research since 2015. The United States led in output, with the University of Toronto as the most productive institution. Brandi, Maria Luisa, and Kanis JA were the most influential authors, while Osteoporosis International and The Journal of Bone and Mineral Research were the most cited journals. Key themes included extracellular vesicles, romosozumab, bisphosphonates, and breast cancer, with recent attention on targeted drug delivery, treatment efficacy, and medication management. Emerging keywords from 2022 to 2023, such as exosomes, inflammation, and osteogenic differentiation, reflected advances in therapeutic mechanisms and clinical applications. Conclusion: Future research will likely emphasize targeted drug delivery, clinical efficacy and safety, and molecular targeted therapies, with the development of new anti-osteoporosis drugs remaining a key focus.
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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 | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.229 | 0.282 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".