MétaCan
Menu
Back to cohort
Record W14514531

Comparison of assays for the detection of West Nile virus antibodies in chicken serum.

2003· article· en· W14514531 on OpenAlexaff
Hana M. Weingartl, Michael Drebot, Zdeněk Hubálek, Jiřı́ Halouzka, Maya Andonova, Antonia Dibernardo, Colleen Cottam-Birt, June E. Larence, Peter Marszal

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsCanadian Science Centre for Human and Animal HealthHealth Canada
Fundersnot available
KeywordsPlaque reduction neutralization testVirologyAntibodySerologyJapanese encephalitisVirusWest Nile virusMedicineHemagglutination assayBiologyImmunologyEncephalitisTiter
DOInot available

Abstract

fetched live from OpenAlex

Six tests for the detection of West Nile virus (WNV) antibodies in the serum of experimentally infected chickens were compared. The tests included the hemagglutination-inhibition test (HIT), immunoglobulin M (IgM)-capture enzyme-linked immunosorbent assay (ELISA) with WNV-infected mouse brain antigen, immunoglobulin G (IgG) indirect ELISA with tickborne encephalitis viral antigen, the microtitre virus neutralization test, the standard plaque reduction neutralization test (PRNT), and the microtitre PRNT (micro-PRNT). Thirty adult chickens, intravenously and intramuscularly inoculated with 10(7) plaque-forming units (PFU) of WNV strain Egypt 101, were bled and given a booster of 10(7) PFU at 7,15, and 21 d postinoculation; the final blood collection was on day 28. Although the micro-PRNT is capable of detecting the highest antibody titres during both early and late infection, because of the technical complexity and time requirements of this test a combination of IgM and IgG ELISAs is recommended for serologic screening. Serum samples that give positive results in the ELISAs can then be tested by the micro-PRNT to determine the specificity of antibodies to WNV.

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 score0.402
Threshold uncertainty score0.187

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 head0.032
GPT teacher head0.295
Teacher spread0.263 · 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

Citations61
Published2003
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicMosquito-borne diseases and controlFrench-language works237,207