Evidence map›Paper›PMID 40491220›Full record

ArticleNMR in biomedicine2025

Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.

Milan Hajek, Petr Sedivy, Martin Burian, Irena Mikova, Pavel Trunecka, Dita Pajuelo, Monika Dezortova

Abstract read
In one paragraph

Article in NMR in biomedicine, 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.

Milan HajekMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0003-4704-6164
Petr SedivyMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0002-9216-2810
Martin BurianMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0002-5797-5090
Irena MikovaDepartment of Hepatogastroenterology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0001-7928-7558
Pavel TruneckaDepartment of Hepatogastroenterology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0002-4753-4419
Dita PajueloMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0001-9046-7736
Monika DezortovaMR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.ORCID https://orcid.org/0000-0003-0194-1200

Funding

Ministry of Education, Youth and Sports, Czech Republic, Next Generation EU, programme EXCELES LX22NPO5104Ministry of Health, Czech Republic 00023001
6 · The paper itself

Abstract

Machine learning identifies liver fat fraction (FF) measured by

Indexed as

Fatty LiverLiverLiver TransplantationMachine LearningAdultElasticity Imaging TechniquesFemaleHumansMaleMiddle Agedbiomarkersdecision treesliver fatliver transplantationmachine learningmagnetic resonance spectroscopymetabolic dysfunction‐associated steatohepatitismetabolic syndrome

Identifiers

PMID40491220
PMCPMC12149787

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.