Evidence map›Paper›PMID 27703508›Full record

ArticleExperimental and therapeutic medicine2016

Diffusion tensor imaging of occult injury of optic radiation following optic neuritis in multiple sclerosis.

Jiafeng Chen, Lijun Zhu, He Li, Ziwen Lu, Xin Chen, Shaokuan Fang

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Diffusion tensor imaging of optic neuropathies: a narrative review.Quantitative imaging in medicine and surgery · 2024
    Review
  3. Article
  4. 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

6 authors at 2 institutions in 1 country.

Jiafeng ChenDepartment of Neurology, Neuroscience Centre, The First Teaching Hospital of Jilin University, Changchun, Jilin 130012, P.R. China.
Lijun ZhuDepartment of Neurology, The Third Teaching Hospital of Jilin University, Changchun, Jilin 130021, P.R. China.
He LiDepartment of Neurology, Neuroscience Centre, The First Teaching Hospital of Jilin University, Changchun, Jilin 130012, P.R. China.
Ziwen LuDepartment of Neurology, Neuroscience Centre, The First Teaching Hospital of Jilin University, Changchun, Jilin 130012, P.R. China.
Xin ChenDepartment of Neurology, Neuroscience Centre, The First Teaching Hospital of Jilin University, Changchun, Jilin 130012, P.R. China.
Shaokuan FangDepartment of Neurology, Neuroscience Centre, The First Teaching Hospital of Jilin University, Changchun, Jilin 130012, P.R. China.
First Hospital of Jilin University · CNJilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple sclerosis (MS) is easily detected by routine magnetic resonance imaging (MRI). However, it is not possible to detect early or occult lesions in MS by routine MRI, and this may explain the inconsistency between the severity of the lesions found by MRI and the degree of clinical disability of patients with MS. The present study included 10 patients with relapsing-remitting MS and 10 healthy volunteers. Each patient underwent routine 3.0 T MRI, diffusion tensor imaging (DTI), and diffusion tensor tractography (DTT). Optic nerve and optic radiation were analyzed by DTI and DTT. The fractional anisotropy (FA), mean diffusivity (MD), λ

Indexed as

diffusion tensor imagingdiffusion tensor tractographymultiple sclerosisoptic nerveoptic radiation

Identifiers

PMID27703508
PMCPMC5038910
OpenAlexW2509900153

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.