Evidence map›Paper›PMID 39931047›Full record

ReviewFrontiers in human neuroscience2025

Research progress in unilateral moyamoya disease.

Xiaokuan Hao, Cunxin Tan, Ziqi Liu, Yang Tie, Yanru Wang, Shihao He, Ran Duan, Rong Wang

Abstract readReview
In one paragraph

Review in Frontiers in human neuroscience, 2025. 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
–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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

8 authors.

Xiaokuan Hao *Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Cunxin Tan *Department of Neurosurgery, Peking University International Hospital, Beijing, China.
Ziqi LiuDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yang TieDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yanru WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Shihao HeDepartment of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Ran DuanDepartment of Neurosurgery, Peking University International Hospital, Beijing, China.
Rong WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unilateral moyamoya disease (U-MMD) is a chronic vascular disease characterized by progressive stenosis and occlusion of the terminal end of the internal carotid artery and its main branches, resulting in the appearance of moyamoya-like blood vessels at the base of the brain. The etiology of U-MMD is unknown, it accounts for 9.7-17.8% of all moyamoya disease, and the family incidence is 5.5-13.3%. The clinical characteristics are similar to those of typical moyamoya disease, but there are some differences. U-MMD can progress to bilateral moyamoya disease with a median probability of 29.01% (ranging from 6.3 to 58.8%), and there are many risk factors that promote its development. Surgical treatment can effectively reduce the incidence of ischemic stroke and improve prognosis. However, the timing and indications for surgery require further investigation. This article reviews the latest research progress on the etiology, epidemiology, clinical and radiological characteristics, progression, treatment, and prognosis of U-MMD.

Indexed as

clinical courseepidemiologyetiologyprogressionsurgeryunilateral moyamoya disease

Identifiers

PMID39931047
PMCPMC11807984

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.