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Record W2049807718 · doi:10.1177/101053950101300107

Sero-epidemiology and Risk Factors of Positive Hepatitis B Surface Antigen amongst Chinese Adolescents

2001· article· en· W2049807718 on OpenAlexfundno aff
Albert Lee, Frances Cheng, Cynthia Chan, L. Lau, Amelia S. C. Lo

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2001
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsnot available
FundersFaculty of Medicine and Dentistry, University of Alberta
KeywordsHBsAgMedicineDemographyEpidemiologyHepatitis BMainland ChinaOdds ratioVaccinationMultivariate analysisCross-sectional studyTransmission (telecommunications)Public healthFamily historyHepatitis B virusImmunologyInternal medicineChinaGeographyPathology

Abstract

fetched live from OpenAlex

This paper reports the prevalence rate of hepatitis B antigen (HBsAg) amongst the Hong Kong Chinese adolescents (age 11 to 19), and the risk factors associated with HBsAg positive. The study is cross sectional and 1,580 students were randomly selected from 12 secondary schools in four regions of Hong Kong. For those subjects who agreed to participate and were randomly selected, their blood was tested for HBsAg and anti-HBs. The overall prevalence of HBsAg positive was reported to be 5.8% (7.9% in male and 4.1% in female), lower than 8.1% in 1978. Males, those born in Mainland China and family history of carriers had higher prevalence of HBsAg positive (7.9% vs 4.1%, 12.2% vs 4.7%, 52.9% vs 3.8% respectively) with statistical significance. Males and those born in mainland China were found to have significantly higher odds ratio 1.8 (95% CI. 0.98-3.52) and 4.4 (95% CI. 2.2-8.8) respectively of HBsAg positive by multivariate analysis. Findings suggest that family history of carriers and those born in endemic area are at a higher risk. Therefore it is worthwhile to consider vaccination programme for adolescents to reduce the carrier rate, and to also reduce the injection amongst the adults by horizontal transmission.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.046
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 integrity0.0000.001
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 head0.047
GPT teacher head0.336
Teacher spread0.290 · 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

Citations15
Published2001
Admission routes1
Has abstractyes

Explore more

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