Evidence map›Paper›PMID 36568760›Full record

ArticleFrontiers in public health2022

Bibliometric and visual analysis of cardiovascular diseases and COVID-19 research.

Namin Wei, Yan Xu, Huan Wang, Qiulei Jia, Xintian Shou, Xuesong Zhang, Nan Zhang, Ya'nan Li, Huaqiang Zhai, Yuanhui Hu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 12% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. COVID-19 and chronic kidney disease: a bibliometric analysis.Annals of medicine and surgery (2012) · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. A Forty-Year Analysis of the Literature onInternational journal of environmental research and public health · 2023
    Review
  11. Article
  12. Review
  13. Intraocular lens power calculation formulas: a scientometric analysis.Medical hypothesis, discovery & innovation ophthalmology journal · 2023
    Article
  14. 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

10 authors at 3 institutions in 1 country.

Namin WeiStandardization Research Center of Traditional Chinese Medicine Dispensing, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Yan XuStandardization Research Center of Traditional Chinese Medicine Dispensing, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Huan WangDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Qiulei JiaDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Xintian ShouDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Xuesong ZhangDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Nan ZhangStandardization Research Center of Traditional Chinese Medicine Dispensing, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Ya'nan LiStandardization Research Center of Traditional Chinese Medicine Dispensing, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Huaqiang ZhaiStandardization Research Center of Traditional Chinese Medicine Dispensing, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Yuanhui HuDepartment of Cardiovascular Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Beijing University of Chinese Medicine · CNGuang’anmen Hospital · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The global community has been affected by the coronavirus disease 2019 (COVID-19), which emerged in December 2019. Since then, many studies have been conducted on cardiovascular diseases (CVDs) and COVID-19. The aim of this study was to perform a bibliometric and visual analysis of the published relationship between CVDs and COVID-19. Methods: 1,890 publications were retrieved from the Web of Science Core Collection database on January 5, 2022. Microsoft Office Excel and CiteSpace were then used to carry out scientometric analysis on the relevant literature according to seven aspects: document type, countries/regions, institutions, authors, journals, references, and keywords. Results: The research on CVDs and COVID-19 is currently in a period of rapid development, with China, USA, England, and Italy leading the field. There is active cooperation between most countries and institutions. Harvard Medical School stands out among the many institutions not only for the largest number of publications, but also for their high quality. Banerjee A, Solomon SD and Narula J are three representative authors in this field. Conclusions: This study provides useful information for the research of CVDs and COVID-19, including potential collaborators, popular research topics, and a reference for more extensive and in-depth research in the future.

Indexed as

Cardiovascular DiseasesCOVID-19Heart FailureAngiotensin-Converting Enzyme 2BibliometricsHumansAngiotensin-Converting Enzyme 2bibliometricscardiovascular diseasesCiteSpaceCOVID-19visualization

Identifiers

PMID36568760
PMCPMC9773213
OpenAlexW4311865394

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.