Evidence map›Paper›PMID 38253066›Full record

ReviewZhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology2023

[Research advances in the non-invasive diagnosis of metabolic dysfunction-associated fatty liver disease based on magnetic resonance technology].

H Q Zhang, J P Shi

Open access · greenAbstract readReviewEnglish Abstract
In one paragraph

Review in Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology, 2023. 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
–field-weighted citation impact, top 59% 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, 0 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

2 authors at 1 institution in 1 country.

H Q ZhangHangzhou Normal University, Hangzhou 310015, China.
J P ShiDepartment of Infectious Disease and Hepatology Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, China.
Hangzhou Normal University · CN

Funding

Ministry of Education, China University Industry-University-Research Innovation Fund 2022BC158
6 · The paper itself

Abstract

Metabolic dysfunction-associated fatty liver disease is becoming the most common cause of chronic liver disease worldwide, with a disease spectrum including simple steatosis, steatohepatitis, hepatic fibrosis/cirrhosis, and liver cancer. Most metabolic dysfunction-associated fatty liver diseases progress slowly, but steatohepatitis, especially in patients accompanied by significant liver fibrosis, has a significantly increased risk of adverse liver disease outcomes and all-cause death. Therefore, early-stage identification of medium-and high-risk groups carried out by stratified management has important clinical significance. Pathological diagnosis is the gold standard for diagnosing steatohepatitis and liver fibrosis. However, its invasiveness, sampling errors, and unsuitability for dynamic monitoring limit its clinical application. In recent years, a large number of non-invasive diagnostic methods based on somatology, serology, and imaging have shown great development prospects in order to meet the clinical needs of assessing disease severity and risk stratification. This article reviews and summarizes the application and progress of magnetic resonance imaging technology in the non-invasive diagnosis of metabolic dysfunction-associated fatty liver disease.

Indexed as

Magnetic Resonance ImagingNon-alcoholic Fatty Liver DiseaseHumansLiver CirrhosisMagnetic Resonance SpectroscopyTechnologyMagnetic resonance imagingMetabolic associated fatty liver diseaseNon-invasive diagnosis

Identifiers

PMID38253066
PMCPMC12814024
OpenAlexW4391145147

What OpenQuestion holds

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Registered trials

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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.