Global scientific trends on the islet transplantation in the 21st century: A bibliometric and visualized analysis
Bibliographic record
Abstract
BACKGROUND: Islet transplantation (IT) has emerged as a significant research area for the treatment of diabetes mellitus and has witnessed a surge in scholarly attention. Despite its growing importance, there is a lack of bibliometric analyses that encapsulate the evolution and scientific underpinnings of this field. This study aims to fill this gap by conducting a comprehensive bibliometric analysis to delineate current research hotspots and forecast future trajectories within the IT domain with a particular focus on evidence-based medicine practices. METHODS: This analysis scrutinized literature from January 1, 2000, to October 1, 2023, using the Web of Science Core Collection (WoSCC). Employing bibliometric tools such as VOSviewer, CiteSpace, and the R package "bibliometrix," we systematically evaluated the literature to uncover scientific trends and collaboration networks in IT research. RESULTS: The analysis revealed 8388 publications from 82 countries, predominantly the United States and China. However, global cross-institutional collaboration in IT research requires further strengthening. The number of IT-related publications has increased annually. Leading research institutions in this field include Harvard University, the University of Alberta, the University of Miami, and the University of Minnesota. "Transplantation" emerges as the most frequently cited journal in this area. Shapiro and Ricordi were the most prolific authors, with 126 and 121 publications, respectively. Shapiro also led to co-citations, totaling 4808. Key research focuses on IT sites and procedures as well as novel therapies in IT. Emerging research hotspots are identified by terms like "xenotransplantation," "apoptosis," "stem cells," "immunosuppression," and "microencapsulation." CONCLUSIONS: The findings underscore a mounting anticipation for future IT research, which is expected to delve deeper into evidence-based methodologies for IT sites, procedures, and novel therapeutic interventions. This shift toward evidence-based medicine underscores the field's commitment to enhancing the efficacy and safety of IT for diabetes treatment, signaling a promising direction for future investigations aimed at optimizing patient outcomes.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.209 | 0.306 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".