Global Publication Trends and Research Hotspots of Diabetes and Osteoporosis
Bibliographic record
Abstract
BACKGROUND: Diabetes and osteoporosis, as chronic diseases with high incidence, have caused deep concern in the field of global public health due to their high morbidity and mortality. More importantly, the complex and close relationship between diabetes and osteoporosis has gradually become the focus of scientific research. It is very meaningful to carry out bibliometric analysis in the research field of diabetes and osteoporosis to describe the current international trend and present a visual representation of the past and emerging trends of diabetes and osteoporosis in the past decade. METHODS: In this study, the characteristics of the articles on "diabetes and osteoporosis" retrieved and downloaded from the Web of Science Core Collection (WoSCC) database from January 1, 2011 to December 1, 2022 were analyzed by bibliometrics to clarify the evolution and theme trends between the two diseases. Citespace software was used for data analysis and visualization, including countries, academic institutions, journals, authors, subject categories, keywords, references, and citations. In addition, some important subtopics identified by bibliometric characterization were further discussed and reviewed. RESULTS: Finally, 3372 articles were included in the analysis, including a total of 96 countries, 407 organizations, 1161 journals, and 617 keywords. Articles related to diabetes and osteoporosis were first published in 2011 and then showed an increasing trend year by year. The United States, China, Italy, England, and Japan were the top 5 countries associated with the largest number of publications. University of California-San Francisco, China Medical University, University of Toronto, Shanghai Jiao Tong University, and Mayo Clinic were the top 5 academic institutions in terms of the number of published papers. The top 5 authors with the highest number of publications were William D, Ann V, Nicola, Peter, and Toshitsugu. Osteoporosis International has published 130 articles in this field, ranking first among highly productive journals. In addition to diabetes and osteoporosis, the most frequently used keywords were bone mineral density, obesity, and fracture. CONCLUSION: More and more studies have been conducted on diabetes and osteoporosis, and the current research mainly focuses on the pathogenesis of various chronic diseases. In the future, more attention may be paid to the prevention and management of these two chronic diseases and the production and application of new drugs.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.062 | 0.110 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".