Evidence map›Paper›PMID 39207825›Full record

ArticleJMIR infodemiology2024

Descriptions of Scientific Evidence and Uncertainty of Unproven COVID-19 Therapies in US News: Content Analysis Study.

Sara Watson, Tyler J Benning, Alessandro R Marcon, Xuan Zhu, Timothy Caulfield, Richard R Sharp, Zubin Master

Abstract read
In one paragraph

Article in JMIR infodemiology, 2024. 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. Barriers to Long COVID Care in the U.S.: An Application of Levesque et al.'s Access Framework.Health care analysis : HCA : journal of health philosophy and policy · 2026
    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

7 authors.

Sara Watson *Biomedical Ethics Research Program, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-9116-5940
Tyler J Benning *Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0003-2359-4078
Alessandro R MarconHealth Law Institute, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0001-5018-423X
Xuan ZhuRobert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-6675-2155
Timothy CaulfieldHealth Law Institute, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0001-5471-6184
Richard R SharpBiomedical Ethics Research Program, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-5441-2084
Zubin MasterDepartment of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, United States.ORCID 0000-0002-3462-4546

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
NCATS NIH HHS UL1 TR002377
6 · The paper itself

Abstract

backgroundPoliticization and misinformation or disinformation of unproven COVID-19 therapies have resulted in communication challenges in presenting science to the public, especially in times of heightened public trepidation and uncertainty.

objectiveThis study aims to examine how scientific evidence and uncertainty were portrayed in US news on 3 unproven COVID-19 therapeutics, prior to the development of proven therapeutics and vaccines.

methodsWe conducted a media analysis of unproven COVID-19 therapeutics in early 2020. A total of 479 discussions of unproven COVID-19 therapeutics (hydroxychloroquine, remdesivir, and convalescent plasma) in traditional and online US news reports from January 1, 2020, to July 30, 2020, were systematically analyzed for theme, scientific evidence, evidence details and limitations, safety, efficacy, and sources of authority.

resultsThe majority of discussions included scientific evidence (n=322, 67%) although only 24% (n=116) of them mentioned publications. "Government" was the most frequently named source of authority for safety and efficacy claims on remdesivir (n=43, 35%) while "expert" claims were mostly mentioned for convalescent plasma (n=22, 38%). Most claims on hydroxychloroquine (n=236, 79%) were offered by a "prominent person," of which 97% (n=230) were from former US President Trump. Despite the inclusion of scientific evidence, many claims of the safety and efficacy were made by nonexperts. Few news reports expressed scientific uncertainty in discussions of unproven COVID-19 therapeutics as limitations of evidence were infrequently included in the body of news reports (n=125, 26%) and rarely found in headlines (n=2, 2%) or lead paragraphs (n=9, 9%; P<.001).

conclusionsThese results highlight that while scientific evidence is discussed relatively frequently in news reports, scientific uncertainty is infrequently reported and rarely found in prominent headlines and lead paragraphs.

Indexed as

Adenosine MonophosphateAlanineCOVID-19 Drug TreatmentCOVID-19 SerotherapyHydroxychloroquineAntiviral AgentsCOVID-19Evidence-Based MedicineHumansImmunization, PassiveMass MediaSARS-CoV-2UncertaintyUnited StatesAdenosine MonophosphateAlanineAntiviral AgentsHydroxychloroquineremdesivircommunicationcontent analysisCOVID-19COVID-19 drug treatmentCOVID-19 therapyefficacyevidencehealth communicationinformation disseminationinformation sourcesmedia analysismisinformationnewsnews reportpandemicpublic awarenesspublic trepidationreportreportssafetyscientific evidencetherapeutictherapeuticstherapytreatmentuncertaintyUnited Statesvaccinevaccines

Identifiers

PMID39207825
PMCPMC11393509

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.