Evidence map›Paper›PMID 34737899›Full record

ArticleCureus2021

Use of Hepatitis B-e Antigen to Identify Pregnant Women With Hepatitis B Virus Infection Who Need Antiviral Therapy for Prevention of Mother-to-child Transmission.

Chanya Jiragraivutidej, Pisit Tangkijvanich, Surasith Chaithongwongwatthana

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Chanya JiragraivutidejDepartment of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Bangkok, THA.
Pisit TangkijvanichDepartment of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, THA.
Surasith ChaithongwongwatthanaDepartment of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Bangkok, THA.
Bangkok Metropolitan Administration · THChulalongkorn University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective To evaluate the performance of hepatitis B-e antigen (HBeAg) for identifying pregnant women infected with hepatitis B virus (HBV) who are having a high viral load. Methods A cross-sectional study was conducted at the tertiary care hospital in Bangkok, Thailand between August 2017 and August 2018. Ninety-six pregnant women having positive hepatitis B-s antigen (HBsAg) results were invited to participate into the study. Clinical data and blood samples were collected and tested for HBeAg and HBV DNA levels. Data were reported as percentage and 95% confidence interval (CI). Results High viral load was found in 25 women (26.0%, 95% CI: 18.3% to 35.6%) and HBeAg showed positive results in 33 women (34.4%, 95% CI: 25.6% to 44.3%). Among antiviral-naïve women, 24 of 30 cases having positive HBeAg results had high viral load (80.0%, 95% CI: 62.7% to 90.5%) while only 1 of 62 negative HBeAg women had high viral load (1.6%, 95% CI: 0.3% to 8.6%). Conclusion About one-fourth of HBV-infected pregnant women were at high risk for mother-to-child transmission (MTCT) of the virus and needed antiviral drugs for reducing MTCT. HBeAg may be used to identify women at high risk for MTCT of HBV in a low-resource setting where HBV DNA level test is not available.

Indexed as

antiviral therapyhepatitis bhepatitis b-e antigenmother-to-child transmissionviral load

Identifiers

PMID34737899
PMCPMC8558031
OpenAlexW3203110452

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.