Evidence map›Paper›PMID 37105224›Full record

ReviewSeminars in liver disease2023

Screening for At-Risk Nonalcoholic Fatty Liver Disease in the Primary Care Setting.

Esteban Urias, Vincent L Chen

Abstract readReview
In one paragraph

Review in Seminars in liver disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. [Liver fibrosis screening, evaluation pathway, and management in patients with fatty liver].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2023
    Review
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.

Esteban UriasDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan.
Vincent L ChenDivision of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.

Funding

Predictive models for incident cirrhosis in non-alcoholic fatty liver disease using genetic and electronic medical record-based risk factorsK08DK132312 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Vincent Lingzhi Chen · 2022 to 2026
$834k
NIDDK NIH HHS K08 DK132312
6 · The paper itself

Abstract

While nonalcoholic fatty liver disease is a leading cause of end-stage liver disease, most patients with nonalcoholic fatty liver disease do not develop cirrhosis and its complications. Therefore, risk stratification using inexpensive, noninvasive screening modalities is critical to avoid overdiagnosis and overtreatment of a large proportion of the population. In this review, we discuss the data supporting screening and current professional society recommendations on this topic. Screening for at-risk nonalcoholic fatty liver disease is recommended in patients with risk factors including diabetes, the metabolic syndrome, hepatic steatosis, and elevated aminotransferases. Screening typically consists of noninvasive testing using serum biomarkers followed by elastography using specialized imaging modalities. This sequential screening approach accurately identifies both high- and low-risk patients and is cost-effective when applied to at-risk populations. In conclusion, screening for advanced nonalcoholic fatty liver disease in the primary care setting is a crucial part of identifying high-risk patients who may benefit from aggressive intervention while avoiding overtreatment of patients at low risk of liver-related complications.

Indexed as

Non-alcoholic Fatty Liver DiseaseHumansLiverLiver CirrhosisPrimary Health CareRisk Factors

Identifiers

PMID37105224
PMCPMC10668862

What OpenQuestion holds

Textmetadata
LicenceTDM
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.