Evidence map›Paper›PMID 34823308›Full record

ReviewClinical and molecular hepatology2022

The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection.

Jung Hwan Yu, Soon Gu Cho, Young-Joo Jin, Jin-Woo Lee

Open access · goldAbstract readReview
In one paragraph

Review in Clinical and molecular hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.9field-weighted citation impact, top 5% 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

27 citing papers in PubMed, 36 citations in OpenAlex.

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  7. Narra J · 2025
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  13. [Risk prediction model of hepatitis B associated hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024
    Review
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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

4 authors at 1 institution in 1 country.

Jung Hwan YuDepartment of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon, Korea.
Soon Gu ChoDepartment of Radiology, Inha University Hospital, Inha University School of Medicine, Incheon, Korea.
Young-Joo JinDepartment of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon, Korea.
Jin-Woo LeeDepartment of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon, Korea.
Inha University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic hepatitis B (CHB) seriously threatens human health. About 820,000 deaths annually are due to related complications such as hepatitis B and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has significantly improved the prognosis of patients with CHB infection and reduced the risk of HCC. However, hepatitis B virus still remains a major factor in the development of HCC, raising many concerns. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infection and many models have been proposed to predict the risk of developing HCC. However, as each study has different models for predicting HCC development that can be applied depending on the use of antiviral agents or the type of antiviral agents, it is necessary to properly understand characteristics of each model when using it for the evaluation of HCC in patients with CHB infection. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is necessary to assess the risk by carefully verifying which variables are used. Recently, studies have also evaluated the risk of HCC using risk prediction models through transient elastography and artificial intelligence (AI) system. These HCC risk predication models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infection reported to date to confirm variables used and specificity between each model to determine an appropriate HCC risk prediction method.

Indexed as

Carcinoma, HepatocellularHepatitis B, ChronicLiver NeoplasmsAntiviral AgentsArtificial IntelligenceHepatitis B virusHumansLiver CirrhosisRisk FactorsAntiviral AgentsChronic hepatitis BHepatocellular carcinoma

Identifiers

PMID34823308
PMCPMC9293610
OpenAlexW3216354379

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.