MétaCan
Menu
Back to cohort
Record W2331209914 · doi:10.1177/104063870501700117

Use of a Novel Virus Inactivation Method for a Multicenter Avian Influenza Real-Time Reverse Transcriptase—Polymerase Chain Reaction Proficiency Study

2005· article· en· W2331209914 on OpenAlexaboutno aff
Erica Spackman, David L. Suarez

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsVirologyInfluenza A virus subtype H5N1Real-time polymerase chain reactionPolymerase chain reactionReverse transcriptaseBiologyBiosecurityVirusGeneticsGene

Abstract

fetched live from OpenAlex

Proficiency assessments are important elements in quality control for diagnostic laboratories. Traditionally, proficiency testing for polymerase chain reaction (PCR)-based assays has involved the use of clinical samples, samples "spiked" with live agents or DNA plasmids. Because of government regulations and biosecurity concerns, distribution of live high-consequence pathogens of livestock and poultry, such as avian influenza, is not possible, and DNA plasmids are not technically suitable for evaluating RNA virus detection. Therefore, a proficiency testing panel using whole avian influenza in a diluent containing a phenolic disinfectant that inactivates the virus while preserving the RNA for at least 8 weeks at -70 C was developed and used in a multicenter proficiency assessment for a type A influenza real-time reverse transcriptase (RT)-PCR test. The test, which was highly standardized, except for variation in the real-time RT-PCR equipment used, was shown to be highly reproducible by proficiency testing in 12 laboratories in the United States, Canada, and Hong Kong. Variation in cycle threshold values among 35 data sets and 490 samples was minimal (CV = 5.19%), and sample identifications were highly accurate (96.7% correct identifications) regardless of real-time PCR instrumentation.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.127
GPT teacher head0.333
Teacher spread0.206 · 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

Citations28
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Diagnostic InvestigationSame topicAnimal Disease Management and EpidemiologyFrench-language works237,207