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Record W4313201229 · doi:10.1016/j.jcvp.2022.100131

Performance evaluation of the bio-rad BioPlex 2200 multiplex system in the detection of measles, mumps, rubella, and varicella-zoster antibodies

2022· article· en· W4313201229 on OpenAlexaff
Matthew A. Lafrenière, Eley Badr, John Beattie, Joseph Macri, Waliul I. Khan

Bibliographic record

VenueJournal of Clinical Virology Plus · 2022
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsHamilton Health SciencesHamilton Regional Laboratory Medicine ProgramMcMaster University
Fundersnot available
KeywordsRubellaMeaslesConfidence intervalMedicineMultiplexImmunoassayChickenpoxVirologyAntibodyImmunologyVaccinationBiologyVirusInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Measles, mumps, rubella, and varicella-zoster (MMRV) immunity testing is important in occupational screening of healthcare workers and at-risk populations. The goal of this study was to compare the performance of the Bio-Rad BioPlex 2200 MMRV multiplex fluorescence immunoassay (MFI) against two enzyme immunoassay (EIA) methods: the Bio-Rad Evolis Twin Plus measles, mumps, and varicella-zoster (MMV) IgG assay and the Abbott Architect Rubella IgG assay. Clinically uncharacterized serum specimens were obtained and analyzed using the Bio-Rad BioPlex assay and compared against the EIA methods. The Bio-Rad BioPlex demonstrated total agreement of 85.5% (95% confidence interval (CI), 78.0 to 90.7%), 92.7% (95% confidence interval (CI), 86.7 to 96.1%), 92.4% (95% confidence interval (CI), 86.9 to 95.7%), and 98.8% (95% confidence interval (CI), 93.7 to 99.8%) for measles, mumps, varicella-zoster, and rubella, respectively, against the current EIA methods. Furthermore, precision testing agreed with the manufacturer's package insert in 10 of 13 pooled samples. These data indicate that the Bio-Rad BioPlex has comparable performance to the EIA methods.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.377
Teacher spread0.286 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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