Evidence map›Paper›PMID 41099052›Full record

ReviewIranian journal of pathology2025

Efforts Focused on Fatty Liver over Two Years (2023-2025): A Thematic Literature Review.

Mahdi Abdorrashidi, Mohammad Hossein Peypar, Amirmohammad Tohidinia, Fatemeh Zali, Sobhan Eisazadeh, Mohammad Ali Abyazi, Mohammad Heiat

Abstract readReview
In one paragraph

Review in Iranian journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Mahdi AbdorrashidiStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mohammad Hossein PeyparStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Amirmohammad TohidiniaStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Fatemeh ZaliStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Sobhan EisazadehStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mohammad Ali AbyaziBaqiyatallah Research Center for Gastroenterology and Liver Diseases (BRCGL), Clinical Sciences Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mohammad HeiatBaqiyatallah Research Center for Gastroenterology and Liver Diseases (BRCGL), Clinical Sciences Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & Objective: Nonalcoholic fatty liver disease, recently recognized as metabolic dysfunction-associated steatotic liver disease (MASLD), is a key factor in the development of chronic liver disease and the progression of liver fibrosis. It plays a significant role in increasing the risk of cirrhosis and hepatocellular carcinoma. Given the rapid developments in this field, keeping information up-to-date is essential to prevent misconceptions and ineffective decision-making. This study employs a scientometric approach to review scientific literature and analyze recent findings, offering a comprehensive overview of the current state of research in this field. Methods: In this approach, we explored data related to publication metrics, public perceptions, scientific conference, mobile apps, AI tools, and new medications. This was done using a set of keywords, including "Non-alcoholic fatty liver disease," "Metabolic dysfunction-associated steatotic liver disease," "Mobile application," and "Artificial intelligence." Results and Conclusion: This research highlights significant scientific advances in the field of MASLD, including major scientific meetings, highly cited publications, and the latest FDA-approved therapies. In addition, it examines emerging digital tools and public search frameworks, providing a structured picture of recent developments. These findings provide a comprehensive view of the dynamic MASLD research landscape and emphasize the roles of AI, mobile apps, and emerging therapies in its management.

Indexed as

Artificial IntelligenceFatty Liver DiseaseMetabolic Dysfunction-Associated Steatotic Liver DiseaseMobile ApplicationNon-alcoholic Fatty Liver Disease

Identifiers

PMID41099052
PMCPMC12520595

What Socratic 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 Socratic graph.