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Record W4280531240 · doi:10.1128/jcm.01716-21

Utility of Cytomegalovirus Cell-Mediated Immunity Assays in Solid Organ Transplantation

2022· review· en· W4280531240 on OpenAlexaff
Victoria Hall, Atul Humar, Deepali Kumar

Bibliographic record

VenueJournal of Clinical Microbiology · 2022
Typereview
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsCytomegalovirusTransplantationSerologyImmunologyOrgan transplantationImmunityCytomegalovirus infectionsCytomegalovirus infectionViral loadVirologySolid organMedicineHuman cytomegalovirusViral diseaseHerpesviridaeBiologyVirusImmune systemAntibodyInternal medicine

Abstract

fetched live from OpenAlex

Cytomegalovirus (CMV) is one of the most important viral complications after solid organ transplantation (SOT). Current preventive and management strategies rely primarily on serologic and viral load testing and remain suboptimal. To address these issues, multiple techniques to measure CMV-specific cell-mediated immunity (CMI) have been developed and evaluated in clinical studies over the past two decades. These assays show significant promise for the personalization of CMV management. For example, CMI assays can be used to help determine the optimal duration of antiviral prophylaxis or whether antiviral therapy is indicated in patients with low levels of CMV reactivation. However, despite numerous studies showing potential utility, these assays are not yet in widespread routine clinical use. Barriers to adoption include variations in test complexity, standardization, and thresholds for positivity and insufficient interventional clinical trials. Here, we provide an updated assessment of commonly available tests and the clinical utility of CMV-specific CMI testing in SOT recipients.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.151
GPT teacher head0.459
Teacher spread0.308 · 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 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

Citations37
Published2022
Admission routes1
Has abstractyes

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