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.

About Followers

Latest preprint reviews

  1. Structural Entropy of Daily Number of COVID-19 Related Fatalities

    1. Eren Unlu

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. Are we ready for COVID-19’s Golden Passport? Insights from a Global Physician Survey

    1. P. Murali Doraiswamy
    2. Mohan Chilukuri
    3. Alexandra R. Linares
    4. Katrina A. Bramstedt

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

    1. Kenji Ikemura
    2. Eran Bellin
    3. Yukako Yagi
    4. Henny Billett
    5. Mahmoud Saada
    6. Katelyn Simone
    7. Lindsay Stahl
    8. James Szymanski
    9. D Y Goldstein
    10. Morayma Reyes Gil

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. At what times during infection is SARS-CoV-2 detectable and no longer detectable using RT-PCR-based tests? A systematic review of individual participant data

    1. Sue Mallett
    2. A. Joy Allen
    3. Sara Graziadio
    4. Stuart A. Taylor
    5. Naomi S. Sakai
    6. Kile Green
    7. Jana Suklan
    8. Chris Hyde
    9. Bethany Shinkins
    10. Zhivko Zhelev
    11. Jaime Peters
    12. Philip J. Turner
    13. Nia W. Roberts
    14. Lavinia Ferrante di Ruffano
    15. Robert Wolff
    16. Penny Whiting
    17. Amanda Winter
    18. Gauraang Bhatnagar
    19. Brian D. Nicholson
    20. Steve Halligan

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. A recombinant fragment of Human surfactant protein D binds Spike protein and inhibits infectivity and replication of SARS-CoV-2 in clinical samples

    1. Taruna Madan
    2. Barnali Biswas
    3. Praveen M. Varghese
    4. Rambhadur Subedi
    5. Hrishikesh Pandit
    6. Susan Idicula-Thomas
    7. Indra Kundu
    8. Sheetalnath Rooge
    9. Reshu Agarwal
    10. Dinesh M. Tripathi
    11. Savneet Kaur
    12. Ekta Gupta
    13. Sanjeev K. Gupta
    14. Uday Kishore

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Quantification of a Viromed Klinik Akut V 500 disinfection device to reduce the indirect risk of SARS-CoV-2 infection by aerosol particles

    1. Christian J. Kähler
    2. Thomas Fuchs
    3. Rainer Hain

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  7. The Impact of COVID-19 on Italian Web Users: A Quantitative Analysis of Regional Hygiene Interest and Emotional Response

    1. Alessandro Rovetta
    2. Lucia Castaldo

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Exploring the Healthy Behaviors of Nigerians during the COVID-19 Pandemic

    1. Ifeanyichukwu Meek Eyisi

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. Estimating Cases of COVID-19 from Daily Death Data in Italy

    1. Ali Raheem

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. Impaired performance of SARS-CoV-2 antigen-detecting rapid diagnostic tests at elevated and low temperatures

    1. Verena Haage
    2. Edmilson Ferreira de Oliveira-Filho
    3. Andres Moreira-Soto
    4. Arne Kühne
    5. Carlo Fischer
    6. Jilian A. Sacks
    7. Victor Max Corman
    8. Marcel A. Müller
    9. Christian Drosten
    10. Jan Felix Drexler

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity
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