Evidence map›Paper›PMID 36267571›Full record

SynthesisFrontiers in endocrinology2022

Current insights of applying MRI in Graves' ophthalmopathy.

Cheng Song, Yaosheng Luo, Genfeng Yu, Haixiong Chen, Jie Shen

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 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

17 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  6. Bibliometrics in thyroid eye disease.Journal of endocrinological investigation · 2026
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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.

Cheng SongDepartment of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
Yaosheng LuoDepartment of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
Genfeng YuDepartment of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
Haixiong ChenThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Jie ShenDepartment of Endocrinology and Metabolism, Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graves' ophthalmopathy (GO) is an autoimmune disease related to Grave's disease (GD). The therapeutic strategies for GO patients are based on precise assessment of the activity and severity of the disease. However, the current assessment systems require development to accommodate updates in treatment protocols. As an important adjunct examination, magnetic resonance imaging (MRI) can help physicians evaluate GO more accurately. With the continuous updating of MRI technology and the deepening understanding of GO, the assessment of this disease by MRI has gone through a stage from qualitative to precise quantification, making it possible for clinicians to monitor the microstructural changes behind the eyeball and better integrate clinical manifestations with pathology. In this review, we use orbital structures as a classification to combine pathological changes with MRI features. We also review some MRI techniques applied to GO clinical practice, such as disease classification and regions of interest selection.

Indexed as

Graves DiseaseGraves OphthalmopathyHumansMagnetic Resonance Imagingassessmentextraocular musclesGraves ophthalmopathyMRIorbital fat

Identifiers

PMID36267571
PMCPMC9577927

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.