Evidence map›Paper›PMID 31132815›Full record

ReviewKorean journal of radiology2019

Multiparametric Functional Magnetic Resonance Imaging for Evaluating Renal Allograft Injury.

Yuan Meng Yu, Qian Qian Ni, Zhen Jane Wang, Meng Lin Chen, Long Jiang Zhang

Open access · hybridAbstract readReview
In one paragraph

Review in Korean journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 8% of its field
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Yuan Meng YuDepartment of Medical Imaging, Jinling Hospital, Clinical School of Southern Medical University, Nanjing, China.ORCID 0000-0003-3610-0962
Qian Qian NiDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.ORCID 0000-0003-4230-5059
Zhen Jane WangDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-2065-5296
Meng Lin ChenMedical Imaging Teaching and Research Office, Nanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0002-8129-1684
Long Jiang ZhangDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. kevinzhlj@163.com.ORCID 0000-0002-6664-7224
Nanjing General Hospital of Nanjing Military Command · CNSouthern Medical University · CNUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney transplantation is the treatment of choice for patients with end-stage renal disease, as it extends survival and increases quality of life in these patients. However, chronic allograft injury continues to be a major problem, and leads to eventual graft loss. Early detection of allograft injury is essential for guiding appropriate intervention to delay or prevent irreversible damage. Several advanced MRI techniques can offer some important information regarding functional changes such as perfusion, diffusion, structural complexity, as well as oxygenation and fibrosis. This review highlights the potential of multiparametric MRI for noninvasive and comprehensive assessment of renal allograft injury.

Indexed as

AdolescentAdultElasticity Imaging TechniquesFibrosisHumansKidneyKidney Failure, ChronicKidney TransplantationMaleMultiparametric Magnetic Resonance ImagingPerfusionQuality of LifeAllograft dysfunctionAllograft fibrosisFunctional MRIKidney transplantationMultiparametric magnetic resonance imaging

Identifiers

PMID31132815
PMCPMC6536799
OpenAlexW2943335036

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.