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Remdesivir for COVID-19: challenges of underpowered studies.

John Norrie
- 16 May 2020 - 
- Vol. 395, Iss: 10236, pp 1525-1527
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This article is published in The Lancet.The article was published on 2020-05-16 and is currently open access. It has received 77 citations till now. The article focuses on the topics: Betacoronavirus & Pneumonia.

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Citations
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Journal ArticleDOI

Remdesivir and its antiviral activity against COVID-19: A systematic review.

TL;DR: Although remdesivir has been used as a compassionate drug for treating COVID-19 patients, it has only moderate efficacy and more efficacy assessments are urgently warranted in clinical trials.
Journal ArticleDOI

Remdesivir in COVID-19: A critical review of pharmacology, pre-clinical and clinical studies.

TL;DR: Initial compassionate use of remdesivir has shown a fairly good result, but difficult to quantify, in the absence of control arm, and newer randomized controlled studies that have recently become available showed a mixed result.
Journal ArticleDOI

Targeting SARS-CoV-2 Proteases and Polymerase for COVID-19 Treatment: State of the Art and Future Opportunities.

TL;DR: An exhaustive comparative analysis of SARS-CoV-2 proteases and RdRp with respect to other coronavirus homologues is provided, and the most promising inhibitors of these proteins reported so far are highlighted, including the possible strategies for their further development.
References
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Journal ArticleDOI

First Case of 2019 Novel Coronavirus in the United States.

TL;DR: This case highlights the importance of close coordination between clinicians and public health authorities at the local, state, and federal levels, as well as the need for rapid dissemination of clinical information related to the care of patients with this emerging infection.
Journal ArticleDOI

Moving to a World Beyond “p < 0.05”

TL;DR: Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without offering any real ideas of what to do about the very hard problem of separating signal from noise in data.
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