Evidence map›Paper›PMID 35976897›Full record

ArticlePloS one2022

Identifying the knowledge structure of electromagnetic fields and health research: Text network analysis and topic modeling.

GyeongAe Seomun, Suyeon Ban, Jinkyung Park

Abstract read
In one paragraph

Article in PloS one, 2022. 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.

GyeongAe SeomunCollege of Nursing, Korea University, BK21FOUR R&E Center for Learning Health, Systems, Korea University, Seoul, Republic of Korea.
Suyeon BanCollege of Nursing, Korea University, BK21FOUR R&E Center for Learning Health, Systems, Korea University, Seoul, Republic of Korea.
Jinkyung ParkCollege of Nursing, Chonnam National University, Gwangju, Republic of Korea.ORCID 0000-0001-8538-0476

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith technological and scientific advancement, people are being increasingly exposed to electromagnetic fields, particularly from portable devices such as mobile phones. However, there is currently no consensus regarding the health effects of electromagnetic field exposure, despite the large amount of research conducted on this topic. This study aimed to understand the knowledge structure and trend of electromagnetic field and health research through text network analysis and topic modeling.

methodsPubMed, Embase, and Cochrane were searched, and 3,880 articles published before June 2021 were identified. We explored the main keywords and research topics regarding electromagnetic fields and human health by constructing a network of keywords. A social network analysis program was used to analyze the data, visualize the network, and perform topic modeling.

resultsFour keywords, "exposure," "effect," "cell," and "cancer," were highly correlated to other keywords and formed each colony in the knowledge structure of research on electromagnetic fields and health. Five topics were derived from topic modeling: cell research, research on the adaption of MRI, health effects of mobile phones, pain therapy, and exposure measurement. Cell research has been continuously performed, and many studies have been conducted on the health effects of mobile phones since 2000.

conclusionsThese findings will assist in gaining insights into and understanding changes in research on the health effects of electromagnetic fields, and suggest important areas and directions for future research.

Indexed as

Cell PhoneNeoplasmsElectromagnetic FieldsHumansTime

Identifiers

PMID35976897
PMCPMC9384997

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.