Evidence map›Paper›PMID 39801783›Full record

ReviewJournal of clinical and translational hepatology2025

The Development and Appraisal of MELD 3.0 in Liver Diseases: Good Things Never Come Easy.

Gaoyue Guo, Wanting Yang, Jia Li, Ziyi Yang, Jing Liang, Chao Sun

Abstract readReview
In one paragraph

Review in Journal of clinical and translational hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. 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

6 authors.

Gaoyue GuoDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.
Wanting YangDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.
Jia LiDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.
Ziyi YangDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.
Jing LiangGastroenterology and Hepatology Department, The Third Central Hospital of Tianjin, Tianjin, China.ORCID https://orcid.org/0000-0001-5114-9030
Chao SunDepartment of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.ORCID https://orcid.org/0000-0002-0380-7999

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since its proposal, the Model for End-Stage Liver Disease (MELD) score has been employed to predict short-term mortality among patients with chronic liver disease and those awaiting liver transplantation, serving as the primary criterion for organ allocation. However, as the demographic and epidemiological characteristics of chronic liver disease and liver transplantation have evolved, a range of MELD-related scores has emerged, including MELD-Na, iMELD, delta MELD, MELD XI, MELD-LA, and pediatric end-stage liver disease, culminating in the recently proposed MELD 3.0, which builds upon MELD-Na. This study aimed to comprehensively review and summarize relevant studies on MELD 3.0 in various scenarios, assessing its effectiveness in organ allocation, post-transplantation outcomes, and mortality prediction for patients with end-stage liver disease. Our preliminary findings indicate superior predictive performance of MELD 3.0, warranting further in-depth investigations to broaden its clinical implications.

Indexed as

Advanced liver diseaseLiver cirrhosisLiver transplantationMELD 3.0MELD-NaOrgan allocationPrognostication

Identifiers

PMID39801783
PMCPMC11712091

What OpenQuestion holds

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Registered trials

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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.