Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning
This article has been Reviewed by the following groups
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- Evaluated articles (ScreenIT)
- Evaluated articles (Rapid Reviews Infectious Diseases)
Abstract
Mitigating transmission of SARS-CoV-2 has been complicated by the inaccessibility and, in some cases, inadequacy of testing options to detect present or past infection. Immunochromatographic lateral flow assays (LFAs) are a cheap and scalable modality for tracking viral transmission by testing for serological immunity, though systematic evaluations have revealed the low performance of some SARS-CoV-2 LFAs. Here, we re-analyzed existing data to present a proof-of-principle machine learning framework that may be used to inform the pairing of LFAs to achieve superior classification performance while enabling tunable False Positive Rates optimized for the estimated seroprevalence of the population being tested.
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Kevin Nichols, Courosh Mehanian
Review 1: "Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning"
Reviewers: Kevin Nichols, Courosh Mehanian (Global Health Labs) 📒📒📒◻️◻️
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Kevin Nichols, Courosh Mehanian
Review of "Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning"
Reviewers: Kevin Nichols and Courosh Mehanian (Global Health Labs) 📒📒📒◻️◻️
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SciScore for 10.1101/2020.07.15.20154773: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Informed combination LFA testing could help to minimize supply chain limitations by spreading the burden of meeting the world’s SARS-CoV-2 testing demand across multiple manufacturers and LFA vendors. In doing so, our work could effectively expand the number of acceptable SARS-CoV-2 immunoassay testing options, serving as a proof of …
SciScore for 10.1101/2020.07.15.20154773: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Informed combination LFA testing could help to minimize supply chain limitations by spreading the burden of meeting the world’s SARS-CoV-2 testing demand across multiple manufacturers and LFA vendors. In doing so, our work could effectively expand the number of acceptable SARS-CoV-2 immunoassay testing options, serving as a proof of principle demonstrating the utility of combination LFA testing for more accurate determination of anti-SARS-CoV-2 antibody status.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
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