GLOBAL RESEARCH PATTERNS IN ORGAN DONATION: A BIBLIOMETRIC PERSPECTIVE
Bibliographic record
Abstract
This bibliometric analysis examines global research trends in organ donation, using data from 813 articles extracted from the Scopus database. Organ donation remains a crucial area of study, involving ethical, cultural, and medical challenges that influence public policy, healthcare practices, and donor participation worldwide. However, despite its significance, there is limited understanding of how research on this topic is distributed globally, particularly regarding collaborative networks and keyword usage. This study addresses that gap by analyzing publication volume, keyword patterns, and country-based co-authorship. Using Scopus Analyzer and VOSviewer software, thematic clusters were identified, highlighting frequent keywords such as "ethics," "organ transplantation," and "informed consent," which collectively reveal a strong focus on ethical and procedural dimensions of organ donation. Analysis of co-authorship patterns revealed that the United States (US) leads in research output and international collaboration, followed by the United Kingdom (UK), Canada, and Germany, each contributing region-specific insights into ethical, cultural, and policy aspects. The high citation rates for countries like the US and the UK suggest these regions are central to shaping global discourse. Overall, the findings emphasize a need for greater global collaboration to address the diverse ethical and cultural contexts of organ donation, ultimately aiming to bridge gaps in donor availability and foster equitable healthcare policies worldwide. This bibliometric study offers a comprehensive overview of current research patterns, providing insights that may inform future studies and international initiatives in organ donation research.
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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 | 0.018 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.212 | 0.356 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".