Evidence map›Paper›PMID 37400379›Full record

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

[Assessment of liver transplant benefit and a novel strategy for liver donor allocation in acute-on-chronic liver failure].

P Li, J Li

Open access · greenAbstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article 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
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.

P LiState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
J LiState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
National Clinical Research · US

Funding

National Natural Science Foundation of China 81830073State's Key Project of Research and Development Plan of China 2022YFC2304801
6 · The paper itself

Abstract

Acute-on-chronic liver failure (ACLF) is a type of complex clinical syndrome that is mainly characterized by acute deterioration of liver function based on chronic liver disease, hepatic and extrahepatic organ failures, and a high short-term mortality rate. The comprehensive medical treatment efficacy of ACLF is currently limited; thus, liver transplantation is the only viable potential treatment method. However, considering the severe liver donor shortage, economic and social costs, as well as the differences in disease severity and prognosis of different disease courses, it is particularly important to accurately assess the benefits of liver transplantation in patients with ACLF. Early identification and prediction, timing, prognosis, and survival benefits are discussed here by combining the latest research findings so as to optimize the liver transplantation treatment strategy for ACLF.

Indexed as

Acute-On-Chronic Liver FailureLiver TransplantationHumansLiver CirrhosisPrognosisAcute-on-chronic liver failureLiver transplantationPredictionPrognosisSurvival benefit

Identifiers

PMID37400379
PMCPMC12677423
OpenAlexW4383059221

What OpenQuestion holds

Textmetadata
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.