Evidence map›Paper›PMID 34056559›Full record

ArticleJAMIA open2021

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

Zhe He, Arslan Erdengasileng, Xiao Luo, Aiwen Xing, Neil Charness, Jiang Bian

Abstract read
In one paragraph

Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Zhe HeSchool of Information, Florida State University, Tallahassee, Florida, USA.ORCID https://orcid.org/0000-0003-3608-0244
Arslan ErdengasilengDepartment of Statistics, Florida State University, Tallahassee, Florida, USA.
Xiao LuoDepartment of Computer Information and Graphics Technology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA.
Aiwen XingDepartment of Statistics, Florida State University, Tallahassee, Florida, USA.
Neil CharnessDepartment of Psychology, Florida State University, Tallahassee, Florida, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
Systematic Analysis of Clinical Study Generalizability Assessment Methods with InformaticsR21AG061431 · NIA · FLORIDA STATE UNIVERSITY · PI BIAN, JIANG, HE, ZHE · 2019 to 2020
$793k
NCATS NIH HHS UL1 TR001427NIA NIH HHS R21 AG061431NLM NIH HHS R01 LM009886
6 · The paper itself

Abstract

objectiveIn the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.

methodsWe analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.

resultsOur analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies.

conclusionsMost COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.

Indexed as

clinical trialCOVID-19eligibility criterianatural language processing

Identifiers

PMID34056559
PMCPMC8083215

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.