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Comparison of DNA Amplification, mRNA Amplification, and DNA Hybridization Techniques for Detection of Cytomegalovirus in Bone Marrow Transplant Recipients

2003· article· en· W2043298486 on OpenAlexafffund
Francisco Díaz‐Mitoma, Chantal S. Leger, H. Miller, Antonio Giulivi, Rita Frost, Laura Shaw, Lothar Huebsch

Bibliographic record

VenueJournal of Clinical Microbiology · 2003
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsCanadian Blood ServicesHealth CanadaUniversity of OttawaChildren's Hospital of Eastern Ontario
FundersHealth Canada
KeywordsViral loadCytomegalovirusBetaherpesvirinaeVirologyBone marrowHerpesviridaeBiologyViral diseaseMolecular biologyHuman cytomegalovirusBone marrow transplantReal-time polymerase chain reactionImmunologyVirusBone marrow transplantationGene

Abstract

fetched live from OpenAlex

A total of 676 specimens from 63 recipients of bone marrow allografts were tested for cytomegalovirus (CMV) by the following assays: CMV pp67 NucliSens (NS), AMPLICOR CMV MONITOR (RA), and the Digene CMV DNA test (DG). In a consensus analysis, the sensitivities and specificities were 60 and 99% (NS), 96 and 98% (RA), and 90 and 76% (DG), respectively; for detection of symptomatic CMV infection, they were 60 and 97% (NS), 65 and 97% (RA), and 95 and 77% (DG), respectively. In multivariate analysis, the major risk factor for symptomatic CMV infection was an increase in the viral load in the DG assay; in univariate analyses, maximum viral loads in both DG and RA assays and a rising viral load in the RA assay were also significant. The earliest detection of CMV replication was provided by the RA assay (mean, 39 days posttransplantation), followed by the DG assay (mean, 48 days) and the NS assay (mean, 58 days).

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.079
GPT teacher head0.414
Teacher spread0.335 · 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 designBench or experimental
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

Citations10
Published2003
Admission routes2
Has abstractyes

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