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Record W3183753795 · doi:10.1111/tid.13698

International survey of human herpes virus 8 screening and management in solid organ transplantation

2021· article· en· W3183753795 on OpenAlexaff
Alessandra Mularoni, Małgorzata Mikulska, Maddalena Giannella, Lucia Adamoli, Monica A. Slavin, Christian van Delden, José María Aguado García, Carlos Cervera, Paolo Grossi

Bibliographic record

VenueTransplant Infectious Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSerologyMedicineEpidemiologyOrgan transplantationTransplantationDiseaseInternal medicineImmunologyVirologyAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: HHV-8/Kaposi Sarcoma herpesvirus has been associated with a broad spectrum of diseases in solid organ transplant (SOT) recipients. Primary donor-derived infection can be associated with severe and rapidly fatal non-neoplastic disease, and diagnosis is made with high HHV-8 DNAemia. METHODS: We carried out an international survey to investigate the current approach to HHV-8 screening, and management in SOT since a protocol has not been established by international guidelines. RESULTS: A total of 51 transplant centers from 15 countries filled out the survey. HHV-8-associated diseases in SOT have been diagnosed during the previous 5 years in 67% of centers. Pretransplant serological screening is performed in 17 centers (33%), and posttransplant HHV-8 nucleic acid testing (NAT) monitoring is performed in 21 centers (41%). Performing HHV-8 NAT monitoring and serological screening were found associated with having diagnosed in the previous 5 years a non-malignant HHV-8-associated disease. CONCLUSIONS: Serological pretransplant screening of donors and recipients and post-transplant HHV-8 NAT monitoring recommendations should be standardized. Even though serological assays are not optimal, they could contribute to increasing knowledge on epidemiology and management of HHV-8-associated diseases after SOT.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.034
GPT teacher head0.336
Teacher spread0.302 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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