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Detection of Measles, Mumps, and Rubella Viruses

2010· article· en· W63843810 on OpenAlexaff
Graham Tipples, Joanne Hiebert

Bibliographic record

VenueMethods in molecular biology · 2010
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsRubellaVirologyMeaslesMumps virusRubella virusMeasles virusOutbreakGenotypingSerologyBiologyVaccinationMedicineVirusImmunologyAntibodyGeneGenotypeGenetics

Abstract

fetched live from OpenAlex

Measles, mumps, and rubella are infections caused by RNA viruses of the same name and are vaccine preventable. The vaccines are frequently administered in a trivalent form. Laboratory diagnostic methods can include indirect detection via antibody (IgM and IgG) detection methods and direct detection by viral culture or viral genome detection. There are challenges for the laboratory in areas with low prevalence due to high vaccine uptake. In those areas, routine serological methods such as IgM detection may have a reduced positive predictive value and thus require confirmation by other methods. Direct detection of viral genomic material using reverse transcription polymerase chain reaction (RT-PCR) methodologies can play an important role for laboratory confirmation of acute infections. Furthermore, genotyping of these three viruses provides useful molecular epidemiological data for differentiating vaccine from wild-type strains, linking cases and outbreaks, and tracking geographic spread and elimination. The purpose of this chapter is to provide guidance for the laboratory diagnosis of measles, mumps, and rubella virus infections. Where assays are commercially available or previously published, the appropriate references are provided as well as brief comments on the interpretation of results. Detailed protocols are provided for the molecular assays which have been developed and more commonly applied in recent years.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.056
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.419
Teacher spread0.392 · 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 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

Citations23
Published2010
Admission routes1
Has abstractyes

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