Evidence map›Paper›PMID 41744019›Full record

ArticleIntractable & rare diseases research2026

Visualization analysis of the use of traditional Chinese medicine in the diagnosis and treatment of rare diseases in mainland China based on CiteSpace.

Yun Shi, Shijing Xiao, Da He

Abstract read
In one paragraph

Article in Intractable & rare diseases research, 2026. 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

3 authors.

Yun ShiShanghai Literature Institute of Traditional Chinese Medicine, Shanghai, China.
Shijing XiaoYueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Da HeShanghai Health Development Research Center, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study used CiteSpace (version 6.4.R1) to perform a visualization analysis of 3,058 articles on traditional Chinese medicine (TCM) diagnosis and treatment of rare diseases retrieved from the China National Knowledge Infrastructure (CNKI) database, the VIP Chinese Science and Technology Periodical Database (VIP), the Wanfang database (Wanfang), and the Chaoxing database (Chaoxing). The goal was to ascertain the current status of research, hotspots in research, and trends in the development of TCM for rare disease diagnosis and treatment in mainland China, providing insights for future TCM research in this field. Visual maps of annual publication volume, authors, institutions, keywords, and other content have revealed that TCM demonstrates prominent advantages in 5 out of 207 defined rare diseases: idiopathic pulmonary fibrosis, hepatolenticular degeneration (Wilson's disease), osteosarcoma, retinitis pigmentosa, and multiple sclerosis. Potential advantages are identified in treating melanoma, amyotrophic lateral sclerosis, homocysteinemia, primary biliary cholangitis, and lymphangioleiomyomatosis. TCM research on rare diseases focuses on etiology, pathogenesis, and syndrome differentiation-based treatment. Case-control studies and mechanism investigations have been initiated for some conditions, while clinical research is gradually incorporating integrated TCM-Western medicine approaches. However, enhanced team and institutional collaboration, development of multicenter networks, exploration of multidisciplinary research, and clinical studies yielding high-level evidence are still needed to provide quality evidence-based support for clinical decision-making in the TCM treatment of rare diseases.

Indexed as

ChinaCiteSpacerare diseasestraditional Chinese medicine

Identifiers

PMID41744019
PMCPMC12932006

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.