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Record W2031620041 · doi:10.1002/lt.21266

An Assessment of Interactions Between Hepatitis C Virus and Herpesvirus Reactivation in Liver Transplant Recipients Using Molecular Surveillance

2007· article· en· W2031620041 on OpenAlexaff
Atul Humar, Kenneth Washburn, Richard B. Freeman, Carlos V. Payá, H. Mouas, Emma Alecock, Raymund R. Razonable

Bibliographic record

VenueLiver Transplantation · 2007
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of Alberta
FundersF. Hoffmann-La Roche
KeywordsViremiaMedicineHepatitis C virusLiver transplantationCytomegalovirusVirologyIncidence (geometry)VirusViral loadImmunologyHuman herpesvirus 6HerpesviridaeHepatitis CTransplantationViral diseaseInternal medicine

Abstract

fetched live from OpenAlex

Hepatitis C virus (HCV) has been proposed to have immunomodulatory effects in transplant recipients and may promote herpesvirus reactivation. To assess this, we compared the incidence of herpesvirus reactivation in HCV-positive and HCV-negative liver transplant recipients. Quantitative viral load testing was performed at regular intervals posttransplantation for cytomegalovirus (CMV), Epstein-Barr virus (EBV), human herpesviruses (HHV) 6, 7, and 8, and varicella zoster virus (VZV) in 177 liver transplant patients who were HCV-positive (n=60) or HCV-negative (n=117). The incidence of CMV disease, CMV viremia, and the peak CMV viral load was not significantly different in HCV-positive vs. HCV-negative patients. Similarly, no differences in HHV-6 or EBV reactivation were observed. HHV-8 or VZV viremia was not detected in any patient in the study. A lower incidence of HHV-7 infection occurred in HCV-positive patients vs. HCV-negative patients (47.6% vs. 72.7%; P=0.006). In conclusion, these results suggest that HCV infection does not appear to promote herpesvirus reactivation after liver transplantation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.376
Teacher spread0.341 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations28
Published2007
Admission routes1
Has abstractyes

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