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Record W2202946415 · doi:10.1371/journal.pone.0136074

Characterization of Acute and Chronic Hepatitis B Virus Genotypes in Canada

2015· article· en· W2202946415 on OpenAlexafffundabout
Carla Osiowy, Elizabeth Giles, M Trubnikov, Y Choudhri, Anton Andonov

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsGenotypeHBsAgHepatitis B virusMedicineVirologyTransmission (telecommunications)Chronic infectionImmunologyVirusBiologyGeneGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE: The prevalence and distribution of hepatitis B virus (HBV) genotypes in Canada is not known. Genotypic analysis may contribute to a better understanding of HBV strain distribution and transmission risk. METHODS: HBV surface antigen (HBsAg) positive samples of acute (n = 152) and chronic (n = 1533) HBV submitted for strain analysis or reference genotype testing between 2006 and 2012 were analyzed. The HBsAg coding region was amplified to determine the HBV genotype by INNO-LiPA assay or sequence analysis. Single and multivariate analyses were used to describe genotypes' associations with known demographic and behavioral risk factors for 126 linked cases of acute HBV. RESULTS: Nine genotypes were detected (A to I), including mixed infections. Genotype C (HBV/C) dominated within chronic infections while HBV/D and A prevailed among acute HBV cases. History of incarceration and residing with a chronic HBV carrier or injection drug user were the most frequently reported risks for acute HBV infection. Over time, HBV/A increased among both acute and chronic infections, and HBV/C and HBV/D decreased among chronic infections. CONCLUSION: Chronic and acute HBV genotypes in Canada differ in the relative distribution and their associations with known risk factors, suggesting different routes of transmission and clinical progression of infection.

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 score0.132
Threshold uncertainty score0.248

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 head0.036
GPT teacher head0.228
Teacher spread0.192 · 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

Citations21
Published2015
Admission routes3
Has abstractyes

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