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Record W4399266313 · doi:10.59707/hymrcqbq3107

Decoding the Landscape of Cytomegalovirus Research in Liver Transplantation: An In-Depth Analysis

2024· article· en· W4399266313 on OpenAlexaboutno aff
Laurie Hung, Haneen Al‐Abdallat, Aasem Rawshdeh, Esra’a Rasmi Al-Zghoul, Amani Al-Rawashdeh, Mohammad Alzoubi, Badi Rawashdeh

Bibliographic record

VenueHigh Yield Medical Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsnot available
Fundersnot available
KeywordsLiver transplantationDecoding methodsTransplantationCytomegalovirusCytomegalovirus infectionComputer scienceMedicineVirologyHuman cytomegalovirusVirusInternal medicineHerpesviridaeViral diseaseAlgorithm

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0880.133
Science and technology studies0.0010.001
Scholarly communication0.0100.007
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.147
GPT teacher head0.431
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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