Evidence map›Paper›PMID 30190800›Full record

ArticleWorld journal of radiology2018

Yu-Ting Wang, Xiong Yan, Hong Pu, Long-Lin Yin

Open access · hybridAbstract read
In one paragraph

Article in World journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Multiparametric MR Imaging for Evaluating Renal Function and Microstructure.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  2. Review
  3. 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

4 authors at 2 institutions in 2 countries.

Yu-Ting WangDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, China. wangyuting_330@163.com.
Xiong YanDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, China.
Hong PuDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, China.
Long-Lin YinDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, China.
University of Electronic Science and Technology of China · CNCreative Research Enterprises (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo investigate the utility of renal diffusion tensor imaging (DTI) to detect early renal damage in patients with type 2 diabetes.

methodsTwenty-six diabetic patients (12 with microalbuminuria (MAU), and 14 with normoalbuminuria) and fourteen healthy volunteers were prospectively included in this study. Renal DTI on 3.0 T MR was performed, and estimated glomerular filtration rate (eGFR) was recorded for each subject. Mean cortical and medullary fractional anisotropy (FA) values were calculated by placing multiple representative regions of interest. Mean FA values were statistically compared among groups. Correlations between FA values and eGFR were evaluated.

resultsBoth cortical and medullary FA were significantly reduced in diabetic patients compared to healthy controls (0.403 ± 0.064

conclusionFA of renal parenchyma on DTI might serve as a more sensitive biomarker of early diabetic nephropathy than MAU.

Indexed as

CorrelationDiabetic nephropathyEstimated GFRFractional anisotropyMicroalbuminuriaRenal diffusion tensor imaging

Identifiers

PMID30190800
PMCPMC6120998
OpenAlexW2888943689

What OpenQuestion holds

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LicenceCC BY-NC
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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.