Evidence map›Paper›PMID 41953000›Full record

ArticleiScience2026

A machine learning-based model to predict multi-time-point prognosis for acute-on-chronic hepatitis B liver failure.

Jieyang Jin, Zhong Liu, Mei Liao, Ting Zhang, Jingming Liu, Yudi Luo, Kai Yang, Minhua Lu, Jie Zeng, Xin Chen and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. 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

11 authors.

Jieyang JinDepartment of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, P.R. China.
Zhong LiuNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Mei LiaoDepartment of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, P.R. China.
Ting ZhangDepartment of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, P.R. China.
Jingming LiuNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Yudi LuoNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Kai YangNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Minhua LuNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Jie ZengDepartment of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, P.R. China.
Xin ChenNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P.R. China.
Jie RenDepartment of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our prognostic model and mobile application enable multi-time-point prognostic evaluation for patients with acute-on-chronic hepatitis B liver failure, thereby improving patient care and facilitating clinical decision-making for liver transplantation.

Indexed as

health informaticshealth scienceshepatologyinternal medicinemedical specialtymedicine

Identifiers

PMID41953000
PMCPMC13053764

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.