Evidence map›Paper›PMID 27127759›Full record

ReviewAnnals of translational medicine2016

Precision medicine in myasthenia graves: begin from the data precision.

Hai-Feng Li, Yu Hong, Yanchen Xie, Hong-Jun Hao, Ren-Cheng Sun

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Annals of translational medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 3 countries.

Hai-Feng Li1 Department of Neurology, Qilu Hospital of Shandong University, Jinan 250012, China ; 2 Department of Clinical Medicine, University of Bergen, Bergen, Norway ; 3 Department of Neurology, The George Washington University, Washington, DC, USA ; 4 Department of Neurology, Peking University First Hospital, Beijing 100034, China ; 5 College of Information and Engineering, Qingdao University, Qingdao 266071, China.
Yu Hong1 Department of Neurology, Qilu Hospital of Shandong University, Jinan 250012, China ; 2 Department of Clinical Medicine, University of Bergen, Bergen, Norway ; 3 Department of Neurology, The George Washington University, Washington, DC, USA ; 4 Department of Neurology, Peking University First Hospital, Beijing 100034, China ; 5 College of Information and Engineering, Qingdao University, Qingdao 266071, China.
Yanchen Xie1 Department of Neurology, Qilu Hospital of Shandong University, Jinan 250012, China ; 2 Department of Clinical Medicine, University of Bergen, Bergen, Norway ; 3 Department of Neurology, The George Washington University, Washington, DC, USA ; 4 Department of Neurology, Peking University First Hospital, Beijing 100034, China ; 5 College of Information and Engineering, Qingdao University, Qingdao 266071, China.
Hong-Jun Hao1 Department of Neurology, Qilu Hospital of Shandong University, Jinan 250012, China ; 2 Department of Clinical Medicine, University of Bergen, Bergen, Norway ; 3 Department of Neurology, The George Washington University, Washington, DC, USA ; 4 Department of Neurology, Peking University First Hospital, Beijing 100034, China ; 5 College of Information and Engineering, Qingdao University, Qingdao 266071, China.
Ren-Cheng Sun1 Department of Neurology, Qilu Hospital of Shandong University, Jinan 250012, China ; 2 Department of Clinical Medicine, University of Bergen, Bergen, Norway ; 3 Department of Neurology, The George Washington University, Washington, DC, USA ; 4 Department of Neurology, Peking University First Hospital, Beijing 100034, China ; 5 College of Information and Engineering, Qingdao University, Qingdao 266071, China.
George Washington University · USPeking University First Hospital · CNQingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myasthenia gravis (MG) is a prototypic autoimmune disease with overt clinical and immunological heterogeneity. The data of MG is far from individually precise now, partially due to the rarity and heterogeneity of this disease. In this review, we provide the basic insights of MG data precision, including onset age, presenting symptoms, generalization, thymus status, pathogenic autoantibodies, muscle involvement, severity and response to treatment based on references and our previous studies. Subgroups and quantitative traits of MG are discussed in the sense of data precision. The role of disease registries and scientific bases of precise analysis are also discussed to ensure better collection and analysis of MG data.

Indexed as

common element data (CDE)Myasthenia gravis (MG)phenotypeprecision medicine

Identifiers

PMID27127759
PMCPMC4828738
OpenAlexW2326990077

What OpenQuestion holds

Textmetadata
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.