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Record W2066860366 · doi:10.1097/qco.0b013e32833bc19d

Emerging viruses in transplantation

2010· review· en· W2066860366 on OpenAlexaff
Deepali Kumar

Bibliographic record

VenueCurrent Opinion in Infectious Diseases · 2010
Typereview
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHuman metapneumovirusMedicineImmunosuppressionHuman bocavirusTransplantationVirologyImmunologyDiseaseVirusValganciclovirRespiratory tract infectionsRespiratory systemInternal medicineHuman cytomegalovirusGanciclovir

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Several viruses have recently gained importance for the transplant recipient. The purpose of this review is to give an update on emerging viruses in transplantation. RECENT FINDINGS: BK virus-associated nephropathy (BKVAN) causes graft loss after kidney transplantation. Immunosuppression lowering strategies have now been shown to have benefit in decreasing the incidence of BKVAN. Guidelines for screening, prevention, and therapy have also been developed. Another polyomavirus, JC virus, is a cause of progressive multifocal leukoencephalopathy and has also gained prominence due to the increasing use of monoclonal antibodies in transplant recipients. The significance of human herpesvirus-6 and -7 continues to be debated in the literature, and new data is available on their association with clinical disease. Finally, newly discovered respiratory viruses, such as human metapneumovirus, bocavirus, KI and WU viruses, have also been described in transplant recipients. Human metapneumovirus appears to cause significant respiratory disease whereas the significance of bocavirus, KI and WU viruses in transplant recipients remains uncertain. SUMMARY: Viral infections, such as polyomaviruses, human herpesvirus-6 and -7 and respiratory viruses, are emerging as causes of significant disease in transplantation. Antiviral options for these viruses are limited, and decreasing immunosuppression is the cornerstone of therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.414
Teacher spread0.353 · 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.

Study designOther design
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

Citations38
Published2010
Admission routes1
Has abstractyes

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