Evidence map›Paper›PMID 34939507›Full record

ArticleScience progress2021

Analysis of research on metabolic syndrome in cancer survivors using topic modeling and social network analysis.

Ji-Su Kim, Hyejin Kim, Eunkyung Lee, Yeji Seo

Open access · diamondAbstract read
In one paragraph

Article in Science progress, 2021. 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
0.2field-weighted citation impact, top 38% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

4 authors at 3 institutions in 1 country.

Ji-Su KimDepartment of Nursing, 26729Chung-Ang University, Seoul, Republic of Korea.ORCID 0000-0002-9512-1934
Hyejin KimDepartment of Nursing, 26729Chung-Ang University, Seoul, Republic of Korea.
Eunkyung LeeDepartment of Nursing, 92220Kyung-In Women's University, Incheon, Republic of Korea.
Yeji SeoDepartment of Nursing, 35007Semyung University, Jecheon, Chungbuk, Republic of Korea.ORCID 0000-0002-8447-2157
Chung-Ang University · KRKyung-in Women's University · KRSemyung University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify the relationships between the keywords of research on metabolic syndrome in cancer survivors and the entire knowledge research structure, through topic extraction from a macro perspective. From six electronic databases, 918 studies published between 1996 and 2019 were identified and reviewed, and 365 were included. Keyword network analysis and topic modeling were applied to examine the studies. In keyword network analysis, "obesity," "treatment," "breast cancer," "body mass index," and "prostate cancer" were the major keywords, whereas "obesity" and "breast" were the dominant keywords and ranked high in frequency, degree centrality, and betweenness centrality. In topic modeling, five clustered topics emerged, namely metabolic syndrome component, post CTX(chemotherapy) sequence, prostate-specific antigen-sensitive plot, lifestyle formation, and insulin fluctuation. Topic 2, post CTX sequence, showed the highest salience in earlier studies, but this has decreased over time, and the themes of the studies have also broadened. This study may provide critical basic data for determining the changing trends of research on metabolic syndrome in cancer survivors and for predicting the direction of future research through the visualization of the effects and interactions between the major keywords in research on metabolic syndrome in cancer survivors.

Indexed as

Cancer SurvivorsMetabolic SyndromeNeoplasmsDatabases, FactualHumansMalePublicationsSocial Network Analysiscancer survivorsMetabolic syndromeresearch analysissocial network analysistopic modeling

Identifiers

PMID34939507
PMCPMC10450613
OpenAlexW4200583898

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.