ArticleJAMIA open2021
How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov.
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.
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.
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.
Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of whether COVID-19 randomized controlled trials reported on demographic and clinical characteristics.Pharmacoepidemiology and drug safety · 2022Pooled it
- Hydroxychloroquine in the treatment of coronavirus disease 2019: Rapid updated systematic review and meta-analysis.Reviews in medical virology · 2022Pooled it
- Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping.Journal of medical Internet research · 2024Article
- A Systematic Analysis of COVID-19 Clinical Trials Registered in the Clinical Trials Registry of India.Cureus · 2024Article
- Clinical trials and their impact on policy during COVID-19: a review.Wellcome open research · 2024Review
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- Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.