Evidence map›Paper›PMID 39958382›Full record

ReviewLiver research (Beijing, China)2023

COVID-19 associated liver injury: An updated review on the mechanisms and management of risk groups.

Yue Shi, Mina Wang, Liqun Wu, Xuexin Li, Zehuan Liao

Abstract readReview
In one paragraph

Review in Liver research (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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

5 authors.

Yue ShiSecond Clinical Medical College, Beijing University of Chinese Medicine, Beijing, China.
Mina WangGraduate School, Beijing University of Chinese Medicine, Beijing, China.
Liqun WuSecond Clinical Medical College, Beijing University of Chinese Medicine, Beijing, China.
Xuexin LiDepartment of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden.
Zehuan LiaoSchool of Biological Sciences, Nanyang Technological University, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) has been associated with various liver injury cases worldwide. To date, the prevalence, mechanism, clinical manifestations, diagnosis, and outcomes of COVID-19-induced liver injury in various at-risk groups are not well defined. Liver injury may arise in the prevention and treatment of COVID-19 from direct causes such as viral infection and indirect causes such as systemic inflammation, hypoxic changes, and drugs that exacerbate any pre-existing liver disease. Studies have found that patients with underlying liver disease are at higher risk of COVID-19-induced liver injury. Certain condition of cardiopulmonary and metabolic diseases and vulnerable stages in lifespan may also involve in the development of COVID-19-induced liver injury. This review summarized studies of COVID-19-induced liver injury in different at-risk groups regarding their clinical characteristics, parameters, and correlations of the severity with these indicators and signs as well as potential treatment suggestions, to increase attention to physiological and pathological conditions and continue liver function monitoring as they can help in strengthening early supportive treatment and reducing the incidence of adverse outcomes.

Indexed as

Coronavirus disease 2019 (COVID-19)Liver injuryManagementRisk groupsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Treatments

Identifiers

PMID39958382
PMCPMC11792068

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.