ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Epidemiological and clinical characteristics of early COVID-19 cases, United Kingdom of Great Britain and Northern Ireland
This article has 22 authors:Reviewed by ScreenIT
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Detection of SARS-CoV-2 from raw patient samples by coupled high temperature reverse transcription and amplification
This article has 9 authors:Reviewed by ScreenIT
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Prevalence of Mental Health Problems During Virus Epidemics in the General Public, Health Care Workers and Survivors: A Rapid Review of the Evidence
This article has 7 authors:Reviewed by ScreenIT
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̃Evaluating reduction in CoViD-19 cases by isolation and protective measures in São Paulo State, Brazil, and scenarios of release
This article has 4 authors:Reviewed by ScreenIT
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Contributions of Latin American researchers in the understanding of the novel coronavirus outbreak: a literature review
This article has 2 authors:Reviewed by ScreenIT
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Effect of Heat inactivation on Real-Time Reverse Transcription PCR of the SARS-COV-2 Detection
This article has 9 authors:Reviewed by ScreenIT
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Clinical features of COVID-19 mortality: development and validation of a clinical prediction model
This article has 6 authors:Reviewed by ScreenIT
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Multifocal laminar cortical brain lesions: a consistent MRI finding in neuro-COVID-19 patients
This article has 8 authors:Reviewed by ScreenIT
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Factors contributing to healthcare professional burnout during the COVID-19 pandemic: A rapid turnaround global survey
This article has 12 authors:Reviewed by ScreenIT
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Low blood sodium increases risk and severity of COVID-19: a systematic review, meta-analysis and retrospective cohort study
This article has 3 authors:Reviewed by ScreenIT