Evidence map›Paper›PMID 33380947›Full record

ReviewContrast media & molecular imaging2020

Diagnostic Value of Structural and Functional Neuroimaging in Autoimmune Epilepsy.

Limei Luo, Nian Wei, Jing Wang, Yuemei Luo, Fei Yang, Zucai Xu

Open access · hybridAbstract readReview
In one paragraph

Review in Contrast media & molecular imaging, 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 38% 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. Review
  2. Article
  3. Review
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 at 1 institution in 1 country.

Limei LuoDepartment of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Nian WeiDepartment of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Jing WangPrevention and Health Care, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Yuemei LuoDepartment of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Fei YangDepartment of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Zucai XuDepartment of Neurology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.ORCID 0000-0002-6849-7928
Zunyi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy is a common nervous system disease, which affects about 70 million people all over the world. In 2017, the International League Against Epilepsy (ILAE) considered immune factors as its independent cause, and the concept of autoimmune epilepsy (AE) was widely accepted. Early diagnosis and timely treatment can effectively improve the prognosis of the disease. However, due to the diversity of clinical manifestations, the expensive cost of autoantibody detection, and the increased prevalence in Western China, the difficulty for clinicians in early diagnosis and treatment has increased. Fortunately, convenient and fast imaging examinations are expected to help even more. The imaging manifestations of AE patients were characteristic, especially the combined application of structural and functional neuroimaging, which improved the diagnostic value of imaging. In this paper, several common autoantibodies associated with AE and their structure and function changes in neuroimaging were reviewed to provide help for neurologists to achieve the goal of precision medicine.

Indexed as

Early DiagnosisAnimalsAutoimmune DiseasesEpilepsyFunctional NeuroimagingHumans

Identifiers

PMID33380947
PMCPMC7752299
OpenAlexW3110754543

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.