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Record W2084237311 · doi:10.1002/jmv.20221

Development and evaluation of a real‐time nucleic acid sequence based amplification assay for rapid detection of influenza A

2004· article· en· W2084237311 on OpenAlexaff
Catherine Moore, Sam Hibbitts, Neil Owen, Sally Corden, G.A.J. Harrison, Julie D. Fox, Colin Gelder, D. Westmoreland

Bibliographic record

VenueJournal of Medical Virology · 2004
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsProvincial Laboratory of Public HealthUniversity of Calgary
Fundersnot available
KeywordsNASBANeuraminidaseVirologyVirusBiologyInfluenza A virusNucleic acidReal-time polymerase chain reactionOrthomyxoviridaeGeneRNAGenetics

Abstract

fetched live from OpenAlex

The development and introduction of effective treatment for influenza A in the form of neuraminidase inhibitors have made the rapid diagnosis of infection important especially in high-risk populations. The aim of this study was to develop a real-time nucleic acid sequenced based amplification (NASBA) using a molecular beacon that could detect a wide range of influenza A subtypes and strains in a single reaction by targeting a conserved region of the influenza genome, and to evaluate its sensitivity and specificity against traditional laboratory techniques on a range of clinical samples usefulness during the 2003/2004 influenza season. The results demonstrated the assay to be highly sensitive and specific, detecting <0.1 TCID50 of virus stock. Three hundred eighty-nine clinical samples were tested in total from two patient groups. Overall, the real-time NASBA assay detected 64% (66/103) more influenza positive samples than cell culture and direct immunofluorescence (IF) and, therefore, was shown to be more sensitive in detecting influenza A in a wide range of respiratory samples than traditional methods. In conclusion, the real-time influenza A assay demonstrated clinical usefulness in both hospital and community populations.

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)0.008
metaresearch head score (Gemma)0.006
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 score0.175
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
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 head0.156
GPT teacher head0.432
Teacher spread0.276 · 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

Citations81
Published2004
Admission routes1
Has abstractyes

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