Evidence map›Paper›PMID 41436112›Full record

ArticleJournal of oral & facial pain and headache2025

Global research trends in MRI of temporomandibular disorders: a bibliometric study and visualization analysis via CiteSpace.

Yu Luo, Mengqi Liu, Yujiao Jiang, Kangkang Ma, Zhiye Chen

Abstract read
In one paragraph

Article in Journal of oral & facial pain and headache, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yu Luo *Department of Radiology, Hainan Hospital of PLA General Hospital, 572013 Sanya, Hainan, China.
Mengqi Liu *Department of Radiology, First Medical Center of PLA General Hospital, 100853 Beijing, China.
Yujiao Jiang *Department of Radiology, Hainan Hospital of PLA General Hospital, 572013 Sanya, Hainan, China.
Kangkang MaDepartment of Radiology, Hainan Hospital of PLA General Hospital, 572013 Sanya, Hainan, China.
Zhiye ChenDepartment of Radiology, Hainan Hospital of PLA General Hospital, 572013 Sanya, Hainan, China.

Funding

Hainan Provincial Natural Science Foundation of China 822MS197
6 · The paper itself

Abstract

backgroundTo conduct a bibliometric analysis mapping the intellectual landscape and emerging trends in Magnetic resonance imaging (MRI) research of temporomandibular disorders (TMD), identifying knowledge gaps and future directions.

methodsA total of 1017 articles were retrieved from the Web of Science Core Collection (WOSCC) database from 1995 to 2024 using the search formula: TS (Topic Search) = ("Temporomandibular Disorders" OR "Temporomandibular Joint Disease" OR TMD) AND TS = ("Magnetic Resonance Imaging" OR MRI). Author/country collaboration network, co-citation analysis, keyword clustering, and burst detection were conducted via CiteSpace 6.4.R1 (Parameters: Time slice: 1 year; g-index

resultsAnnual publications exhibited triphasic growth, peaking at 75 articles in 2022. The United States (160 articles, centrality = 0.22) dominated global collaborations, while Shanghai Jiao Tong University emerged as the most productive institution (30 articles). Key clusters revealed 15 clusters (

conclusionsThis study delineates an evolving research paradigm integrating Artificial Intelligence (AI)-driven diagnostics with multimodal imaging.

Indexed as

BibliometricsBiomedical ResearchMagnetic Resonance ImagingTemporomandibular Joint DisordersHumansArtificial intelligenceBibliometricsMagnetic resonance imagingResearch trendsTemporomandibular disorders

Identifiers

PMID41436112
PMCPMC12727186

What OpenQuestion holds

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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.