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Evaluation of the INNO-LiPA HBV Genotyping Assay for Determination of Hepatitis B Virus Genotype

2003· article· en· W2145620204 on OpenAlexaff
Carla Osiowy, Elizabeth Giles

Bibliographic record

VenueJournal of Clinical Microbiology · 2003
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsCanadian Science Centre for Human and Animal Health
Fundersnot available
KeywordsGenotypingGenotypeVirologyAmpliconHepatitis B virusBiologyTypingPolymerase chain reactionConcordanceOrthohepadnavirusHepadnaviridaeVirusGeneticsGene

Abstract

fetched live from OpenAlex

Specific genotypes of hepatitis B virus (HBV) are increasingly recognized for their clinical significance and association with particular viral mutations. Although many HBV genotyping methods exist, there has been no standardized or commercially available method for direct molecular typing of the HBV genome. A newly available line probe assay (INNO-LiPA HBV Genotyping assay; Innogenetics N.V., Ghent, Belgium) that allows the identification of HBV genotypes A to G was assessed by comparison with pre-S1/pre-S2 sequence analysis of the isolates in 188 serum specimens. All seven genotypes were detected by the line probe assay (LiPA), and complete concordance between LiPA and sequence analysis was observed for 152 specimens (81%). LiPA was able to detect 19 mixed genotype infections not detected by amplicon sequencing, which for the most part were confirmed by cloning and sequencing of the pre-S1/pre-S2 amplicon. Four specimens had discrepant results between the two methods, and five specimens had indeterminate results by LiPA. The HBV DNA in four specimens was unable to be amplified by the nested INNO-LiPA HBV DR amplification primers; however, the HBV DNA in six specimens unable to be genotyped by sequencing was clearly genotyped by LiPA. The INNO-LiPA HBV Genotyping assay appears to be useful for the rapid genotyping of HBV, particularly for the sensitive detection of mixed genotype infections.

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)0.010
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.120
GPT teacher head0.426
Teacher spread0.306 · 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.

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

Citations113
Published2003
Admission routes1
Has abstractyes

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