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Prevalence of Hepatitis B Surface Antigen (HBsAg) Among HIV Seropositive Patients

2012· article· en· W2317791359 on OpenAlexvenueno aff
Anamika Vyas, Ramavtar Saini, Pooja Gangrade

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2012
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHBsAgHepatitis B virusMedicineHepatitis BTransmission (telecommunications)VirologyImmunologyVirusHepatitisHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Introduction: Human Immunodeficiency Virus (HIV) and Hepatitis B virus (HBV) share the routes of transmission as a consequences infection with Hepatitis B Virus are expected to occur in HIV infected patients. The co-infection of Hepatitis B Virus (HBV) with the Human Immunodeficiency Virus (HIV) have become a major health care catastrophe as it complicates the clinical course, management and therapy for HIV infection. Hence it is important to identify them as early as possible. Aim: The prevalence of HBV co-infection with HIV varies widely across different studies within India and outsides. This study is planned to evaluate the prevalence of HIV-HBV co-infection by HBsAg screening in HIV seropositive patients in our region. Material and Method: A total of 140 HIV seropositive patients were screened for the presence of Hepatitis B virus on the basis of the presence of HBsAg. Result: In patients infected with HIV the prevalence of HBsAg was 7.1% (10/140) wherease in control group it was 1% (5/500). Discussion: Our study documents fairly high rate of Hepatitis B co-infection among HIV seropositive patients suggesting that it should be mandatory to screen every HIV seropositive patient and their sexual partners for co infection with HBV and vice versa for early detection and a simultaneous treatment of hepatitis B co-infection beside HIV infection management to reduce the morbidity, delay mortality and improve quality of life in HIV-AIDS patients.

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)0.001
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 score0.019
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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