Evidence map›Paper›PMID 33343281›Full record

ArticleFrontiers in neuroscience2020

Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics.

Fangxue Yang, Minli Qu, Youming Zhang, Linmei Zhao, Wu Xing, Gaofeng Zhou, Jingyi Tang, Jing Wu, Yuanchao Zhang, Weihua Liao

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. 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

10 authors.

Fangxue YangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Minli QuDepartment of Endocrinology, Xiangya Hospital, Central South University, Changsha, China.
Youming ZhangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Linmei ZhaoDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Wu XingDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Gaofeng ZhouDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Jingyi TangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Jing WuDepartment of Endocrinology, Xiangya Hospital, Central South University, Changsha, China.
Yuanchao ZhangKey Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Weihua LiaoDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (

Indexed as

cortical thicknessdiabetic peripheral neuropathygraph theoryintegrationsegregationstructural covariance networks

Identifiers

PMID33343281
PMCPMC7746555

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.