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Performance characteristics of the Food and Drug Administration–licensed Roche Cobas TaqScreen West Nile virus assay

2008· article· en· W2165824150 on OpenAlexaffabout
Anuradha Pai, Steven Kleinman, Khushbeer Malhotra, Lorlelei Lee‐Haynes, Larry Pietrelli, J. Saldanha

Bibliographic record

VenueTransfusion · 2008
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsDynamic Systems Analysis (Canada)
Fundersnot available
KeywordsWest Nile virusFood and drug administrationMedicineVirologyConfidence intervalInternal medicineVirusPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: The cobas TaqScreen West Nile virus (WNV) test (Roche Molecular Systems) was licensed by the Food and Drug Administration (FDA) in August 2007 for detecting WNV RNA in pools of six or in individual donations (IDs). A series of studies established the performance characteristics of the assay and test system before FDA licensure. STUDY DESIGN AND METHODS: Analytic sensitivity was determined by probit analysis using multiple source materials. Clinical sensitivity was determined by testing a panel of 315 known WNV RNA-positive specimens. A large clinical specificity study was conducted by five laboratories during months when WNV activity was not expected. RESULTS: The 95 percent limit of detection for ID testing using the Lineage 1 Health Canada WNV reference standard was 40.3 copies per mL (95% individual donation, 35.1-47.8 copies per mL). Clinical sensitivity was 100 percent (95% confidence interval [CI], 98.8%-100%) for ID testing and 97.5 percent (95% CI, 95.1-98.9%) for minipool (MP) testing. Clinical specificity, when resolved to the ID, was 100 percent for both formats and was 99.986 percent at the MP level. CONCLUSION: The cobas TaqScreen WNV test performed on the cobas s 201 system is a fully automated test system with excellent clinical sensitivity and specificity that offers the benefits of automated sample preparation and a secure environment for donor testing information.

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)0.000
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 score0.332
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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

Citations19
Published2008
Admission routes2
Has abstractyes

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