Evidence map›Paper›PMID 39669562›Full record

ArticleFrontiers in immunology2024

Research hotspots and trends for Duchenne muscular dystrophy: a machine learning bibliometric analysis from 2004 to 2023.

Pingping Fang, Jingzhe Han, Di An, Yi Bu, Guang Ji, Mingjuan Liu, Jinliang Deng, Moran Guo, Xu Han, Hongran Wu and 2 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. 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
–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

1 citing paper in PubMed.

  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

12 authors.

Pingping FangDepartment of Neurology, Hebei Medical University, Shijiazhuang, Hebei, China.
Jingzhe HanDepartment of Neurology, Hengshui People's Hospital, Hengshui, Hebei, China.
Di AnDepartment of Neurology, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
Yi BuDepartment of Neurology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Guang JiDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Mingjuan LiuDepartment of Neurology, Hebei Medical University, Shijiazhuang, Hebei, China.
Jinliang DengDepartment of Neurology, Hebei Medical University, Shijiazhuang, Hebei, China.
Moran GuoDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xu HanDepartment of Neurology, Peking University First Hospital, Beijing, China.
Hongran WuDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Shaojuan MaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xueqin SongDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: The aim of this study was to conduct a bibliometric analysis of the relevant literature on Duchenne muscular dystrophy (DMD) to ascertain its current status, identify key areas of research and demonstrate the evolution of the field. Methods: The analysis sourced documents from the Science Citation Index Expanded in the Web of Science core collection, utilizing CiteSpace software and an online bibliometric platform to analyze collaborative networks among authors, institutions and countries, and to map out the research landscape through journal and reference evaluations. Keyword analyses, including clustering and emergent term identification, were conducted, alongside the development of knowledge maps. Results: The study included 9,277 documents, indicating a rising publication trend in the field. The Institut National de la Santé et de la Recherche Médicale emerged as the top publishing institution, with Francesco Muntoni as the most prolific author. The United States dominated in publication output, showcasing significant leadership. The keyword analysis highlighted 786 key emergent terms, primarily focusing on the mechanisms, diagnostics and treatment approaches in DMD. Conclusion: The field of DMD research is experiencing robust growth, drawing keen interest globally. A thorough analysis of current research and trends is essential for advancing knowledge and therapeutic strategies in this domain.

Indexed as

BibliometricsMachine LearningMuscular Dystrophy, DuchenneBiomedical ResearchHumansbibliometricCiteSpace VOSviewerDuchenne muscular dystrophyhotspotsknowledgemaptopics

Identifiers

PMID39669562
PMCPMC11634759

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.