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Record W4221018299 · doi:10.1556/1886.2022.00003

Validation of the Cepheid Xpert® Xpress SARS-CoV-2 using upper and lower respiratory tract specimens

2022· article· en· W4221018299 on OpenAlexaff
Kassie Rong, Ana Cabrera, Johan Delport, Shannon Schofield, Fatimah AlMutawa

Bibliographic record

VenueEuropean Journal of Microbiology and Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsReproducibilityRespiratory tractMedicineStandard operating procedureRespiratory systemVirologyChromatographyInternal medicineChemistry

Abstract

fetched live from OpenAlex

This study validated the performance of the reverse transcriptase-polymerase chain reaction (rRT-PCR) based Cepheid Xpert® Xpress SARS-CoV-2 assay against the TIB MOLBIOL E-gene/EAV, a standard laboratory rRT-PCR SARS-CoV-2 assay. Upper and lower respiratory tract samples (nasopharyngeal and nasal swabs, bronchoalveolar lavage, and tracheal aspirate) were obtained from patients suspected to have contracted COVID-19. Results from the Xpert® Xpress and standard rRT-PCR assays were compared for positive and negative agreement and analyzed for precision, reproducibility, 95% confidence intervals, and coefficients of variation. The Xpert® Xpress assay demonstrated 100% agreement with the standard lab rRT-PCR for both upper and lower respiratory tract samples. Both the Xpert® Xpress and lab rRT-CPR identified weakly positive (Ct values 35-39) sample replicates with 100% reproducibility and showed 100% precision in identifying triplicates of upper respiratory tract samples. The single-cartridge Xpert® Xpress system has a short turnaround time and can be employed to improve patient management and hospital bed allocation. Further verification of the system is required before implementation and consideration must be paid to its higher cost and impracticality for high-throughput use.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.042
GPT teacher head0.272
Teacher spread0.230 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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