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Record W7100704044

Printed in Great Britain Antibody to Hepatitis C Virus in Selected Groups of a Canadian Urban

2016· article· en· W7100704044 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsSerologyAntibodyJaundiceHepatitisHepatitis C virusEpidemiologyHepatitis CVirus
DOInot available

Abstract

fetched live from OpenAlex

In an anonymous survey, 433 sera from Canadian individuals of selected categories were tested for the presence of antibody to hepatitis C virus (HCV) using a recombinant antigen-based immunoassay. About 50 % of intravenous drug abusers (IVDA), 10 % of transfusion recipients and an overall average of 7.9 % of male homosexuals were reactive for antibody to HCV. Individuals with jaundice and negative hepatitis B virus (HBV) serology were not reactive for antibody to HCV compared with 26.7 % of those with positive HBV serology. Similarly 58 % of male Federal prisoners with positive HBV serology were also HCV-antibody reactive compared with 15 % of those with negative HBV serology. A prevalence of 1.2 % was recorded for individuals not in any of the above groups. Of 433 sera, 92 were reactive and the discrimination in absorbance values between reactive and not reactive samples was good except for 13 sera, eight of which gave values considerably higher than the average negative value and five which were just above the positive threshold. Non-A, non-B (NANB) hepatitis has been recognized as a clinically separate entity1 from hepatitis A and B, for which specific serological tests have been available for at least 15 years.2 Two types of NANB hepatitis have been identified based on epidemiological data,

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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