Evidence map›Paper›PMID 35583933›Full record

ArticleJMIR formative research2022

One Hundred Years of Hypertension Research: Topic Modeling Study.

Mustapha Abba, Chidozie Nduka, Seun Anjorin, Shukri Mohamed, Emmanuel Agogo, Olalekan Uthman

Abstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Mustapha Abba *Warwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.ORCID https://orcid.org/0000-0002-5654-1186
Chidozie NdukaWarwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.ORCID https://orcid.org/0000-0001-7031-5444
Seun AnjorinWarwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.ORCID https://orcid.org/0000-0003-0187-6410
Shukri Mohamed *Warwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.ORCID https://orcid.org/0000-0002-8693-1943
Emmanuel AgogoCountry Office Nigeria, Resolve to Save Lives, Abuja, Nigeria.ORCID https://orcid.org/0000-0002-3570-3254
Olalekan UthmanWarwick Centre for Global Health, Division of Health Sciences, University of Warwick Medical School, University of Warwick, Coventry, United Kingdom.ORCID https://orcid.org/0000-0002-8567-3081

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundDue to scientific and technical advancements in the field, published hypertension research has developed substantially during the last decade. Given the amount of scientific material published in this field, identifying the relevant information is difficult. We used topic modeling, which is a strong approach for extracting useful information from enormous amounts of unstructured text.

objectiveThis study aims to use a machine learning algorithm to uncover hidden topics and subtopics from 100 years of peer-reviewed hypertension publications and identify temporal trends.

methodsThe titles and abstracts of hypertension papers indexed in PubMed were examined. We used the latent Dirichlet allocation model to select 20 primary subjects and then ran a trend analysis to see how popular they were over time.

resultsWe gathered 581,750 hypertension-related research articles from 1900 to 2018 and divided them into 20 topics. These topics were broadly categorized as preclinical, epidemiology, complications, and therapy studies. Topic 2 (evidence review) and topic 19 (major cardiovascular events) are the key (hot topics). Most of the cardiopulmonary disease subtopics show little variation over time, and only make a small contribution in terms of proportions. The majority of the articles (414,206/581,750; 71.2%) had a negative valency, followed by positive (119, 841/581,750; 20.6%) and neutral valency (47,704/581,750; 8.2%). Between 1980 and 2000, negative sentiment articles fell somewhat, while positive and neutral sentiment articles climbed substantially.

conclusionsThe number of publications has been increasing exponentially over the period. Most of the uncovered topics can be grouped into four categories (ie, preclinical, epidemiology, complications, and treatment-related studies).

Indexed as

cardiovascularhigh blood pressurehypertensionlatent Dirichlet allocationLDAmachine learningresearch trendstopic modeling

Identifiers

PMID35583933
PMCPMC9161044

What Socratic 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 Socratic graph.