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Record W2343492868 · doi:10.1128/jcm.03275-15

Detection of Human Herpesvirus 6B (HHV-6B) Reactivation in Hematopoietic Cell Transplant Recipients with Inherited Chromosomally Integrated HHV-6A by Droplet Digital PCR

2016· article· en· W2343492868 on OpenAlexaff
Ruth Hall Sedlak, Joshua A. Hill, Thuy Nguyen, Michelle Cho, Greg Levin, Linda Cook, Meei-Li Huang, Louis Flamand, Danielle M. Zerr, Michael Boeckh, Keith R. Jerome

Bibliographic record

VenueJournal of Clinical Microbiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesHHV-6 Foundation
KeywordsHuman herpesvirus 6VirologyDigital polymerase chain reactionBiologyHematopoietic cellBone marrow transplantPolymerase chain reactionHaematopoiesisHerpesviridaeImmunologyGeneticsStem cellVirusViral diseaseBone marrow transplantationBone marrowGene

Abstract

fetched live from OpenAlex

The presence of inherited chromosomally integrated human herpesvirus 6 (ciHHV-6) in hematopoietic cell transplant (HCT) donors or recipients confounds molecular testing for HHV-6 reactivation, which occurs in 30 to 50% of transplants. Here we describe a multiplex droplet digital PCR clinical diagnostic assay that concurrently distinguishes between HHV-6 species (A or B) and identifies inherited ciHHV-6. By applying this assay to recipient post-HCT plasma and serum samples, we demonstrated reactivation of HHV-6B in 25% (4/16 recipients) of HCT recipients with donor- or recipient-derived inherited ciHHV-6A, underscoring the need for diagnostic testing for HHV-6 infection even in the presence of ciHHV-6.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.329
Teacher spread0.298 · 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.

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

Citations51
Published2016
Admission routes1
Has abstractyes

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