Evidence map›Paper›PMID 42071232›Full record

ReviewJournal of cardiothoracic surgery2026

Visualization analysis of research hotspots and trends in Type B Aortic Dissection based on bibliometrics.

Yinghong Li, Yierpani Aierken, Zhian Liang, Yuqi Wang, Yuanhao Sun, Zongwei Liu, Jiayin Guo, Jiaxin Wang, Jiaxue Bi, Xiangchen Dai

Abstract readReview
In one paragraph

Review in Journal of cardiothoracic surgery, 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

10 authors.

Yinghong Li *Department of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Yierpani Aierken *Department of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Zhian Liang *Department of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Yuqi WangDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Yuanhao SunDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Zongwei LiuDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Jiayin GuoDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Jiaxin WangDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Jiaxue BiDepartment of Vascular Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Xiangchen DaiDepartment of Vascular Surgery, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, China. 13302165917@163.com.

Funding

National Natural Science Foundation of China projects No. 82241207Tianjin Medical University Key Project in Integrative Medicine NO.2024XKZXY01
6 · The paper itself

Abstract

purposeThis study aims to systematically map and analyze the knowledge structure, research hotspots, and evolutionary trends in the field of Type B Aortic Dissection (TBAD) using bibliometric methods. PATIENTS AND

methodsCiteSpace and VOSviewer were employed to conduct quantitative and visual analyses across multiple dimensions, including publication trends, collaboration networks among countries/institutions/authors, keyword co-occurrence and evolution, as well as co-citation analysis and reference burst detection.

resultsThe number of publications in this field has shown an overall upward trend, with a significant increase since 2013, reflecting the growing clinical and scientific attention to this life-threatening vascular emergency. The United States (USA) and China form a dual-core global collaboration structure, with the USA leading in international cooperation depth and citation impact, and China emerging as a major contributor with rapid growth in research output - an evolution that mirrors the global redistribution of cardiovascular research capacity. Keyword analysis reveals a paradigm shift from technical application to precision intervention and individualized management, while current research focusing on Thoracic Endovascular Aortic Repair (TEVAR), complication prediction, computational fluid dynamics (CFD), and artificial intelligence (AI)-assisted diagnosis and treatment. Co-citation analysis confirms TEVAR as the gold-standard minimally invasive treatment for TBAD, with its widespread acceptance driving the standardization of clinical practice. Burst analysis of keywords indicates that "prediction model" and "deep learning" have become emerging research hotspots, marking the entry of TBAD research into an intelligent, data-driven era.

conclusionResearch on TBAD has developed a sophisticated knowledge system over the past two decades, shifting from traditional surgical exploration to an intelligent, data-driven research paradigm. This bibliometric analysis identifies a USA-China dual-core global collaboration pattern in the field and a three-stage evolution of research focus from pathophysiological exploration to evidence-based TEVAR standardization, and further to the integration of CFD and AI. Critical research gaps are also highlighted, including under-investigation of high-risk populations, insufficient long-term evidence for TEVAR, and inadequate cross-disciplinary integration of CFD, AI and genomics. Future TBAD research should prioritize multicenter prospective trials to upgrade clinical evidence, advance interdisciplinary precision medicine models, and build globally standardized big data platforms for the development and validation of AI-based diagnostic and therapeutic tools, thereby achieving more scientific and personalized management of TBAD.

Indexed as

Aortic DissectionBibliometricsBiomedical ResearchDissection, Thoracic AortaEndovascular Aneurysm RepairEndovascular ProceduresHumansAortic dissectionCiteSpaceComputational fluid dynamicsDeep learningEndovascular repairTEVARVOSviewer

Identifiers

PMID42071232
PMCPMC13289555

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.