Evidence map›Paper›PMID 33490185›Full record

ArticleAnnals of translational medicine2020

Susceptibility weighted imaging (SWI) for evaluating renal dysfunction in type 2 diabetes mellitus: a preliminary study using SWI parameters and SWI-based texture features.

Zhenxing Jiang, Yu Wang, Jiule Ding, Shengnan Yu, Jinggang Zhang, Hua Zhou, Jia Di, Wei Xing

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2020. 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
0.3field-weighted citation impact, top 35% 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

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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, 3 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Zhenxing JiangDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Yu WangDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Jiule DingDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Shengnan YuDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Jinggang ZhangDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Hua ZhouDepartment of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Jia DiDepartment of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Wei XingDepartment of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China.
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSusceptibility weighted imaging (SWI) could reflect tissue blood oxygen levels, and then whether it could be used to evaluate renal injury remains to be further studied. This study aimed to examine the performance of SWI parameters and SWI-based texture features in evaluating renal dysfunction of type 2 diabetes mellitus (T2DM).

methodsForty-five patients with T2DM were included. With the estimated glomerular filtration rate (eGFR), the patients were divided into non-moderate-severe renal injured group (non-msRI, eGFR >60 mL/min/1.73 m

resultsThe signal intensity ratio of the medulla to psoas muscle (MPswi) was significantly lower than the signal intensity ratio of the cortex to psoas muscle (CPswi) in non-msRI and msRI group (t=8.619, 3.483, respectively, P<0.05). MPswi was higher, and the signal intensity ratio of the cortex to the medulla (CMswi), Skewness, Correlation were lower in msRI than in non-msRI (P<0.05). These parameters showed similar diagnostic efficacies for msRI (P>0.05), and AUCs were 0.703

conclusionsSWI was valuable for assessing renal dysfunction, which may be helpful for the evaluation of moderate-severe renal injured patients with T2DM.

Indexed as

image processing, computer-assistedmagnetic resonance imagingrenal insufficiencysusceptibility-weighted imagingType 2 diabetes mellitus (T2DM)

Identifiers

PMID33490185
PMCPMC7812222
OpenAlexW3115810683

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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.