Decoding the Landscape of Cytomegalovirus Research in Liver Transplantation: An In-Depth Analysis
Bibliographic record
Abstract
Introduction: Cytomegalovirus (CMV), a prevalent viral infection post-liver transplantation, significantly influences transplant outcomes. This bibliometric analysis explores the evolving landscape of CMV-related research in liver transplantation, emphasizing research output and key areas of interest. Methods: Utilizing the Web of Science (WOS) database, we systematically searched for CMV and liver transplantation documents on October 16, 2023. R programming language, VOSviewer, and Microsoft Excel Office 365 were used for analysis. Results: Analyzing 801 publications on CMV-related research in liver transplantation unveiled a variable publication pattern, peaking in 2010 and 2021. "Transplantation" stood out as the predominant journal. Leading contributors included the University of Pittsburgh, Mayo Clinic, and the University of Washington. The United States led in contributions, followed by Spain and the United Kingdom. The analysis highlighted substantial international collaboration, notably involving the United States, the United Kingdom, Canada, China, and Italy. Key themes revolved around recipients, prophylaxis, prevention, and antiviral therapies, with ganciclovir and valganciclovir as primary medications. Recently, there has been significant discussion regarding medications such as letermovir and maribavir. Conclusion: This research highlights the dynamic landscape of CMV infection studies, focusing on emerging trends and new medications like 'letermovir' and' maribavir'. Given the persistent challenges in transplantation, leveraging these insights is crucial for collaborative efforts and innovative research initiatives. As the transplantation community grapples with the challenges of CMV infections, our paper aims to serve as a cornerstone among contributors, fostering collaboration among authors, centers, and countries. We hope this collaboration will significantly benefit patients and elevate healthcare standards.
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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 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.088 | 0.133 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".