Decoding semi-automated title-abstract screening: findings from a convenience sample of reviews

BACKGROUND: We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening and explored whether Abstrackr’s predictions varied by review or study-level characteristics. METHODS: For a convenience sample of 16 reviews for which adequate...

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Detalles Bibliográficos
Autores principales: Gates, Allison, Gates, Michelle, DaRosa, Daniel, Elliott, Sarah A., Pillay, Jennifer, Rahman, Sholeh, Vandermeer, Ben, Hartling, Lisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694314/
https://www.ncbi.nlm.nih.gov/pubmed/33243276
http://dx.doi.org/10.1186/s13643-020-01528-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694314/
https://www.ncbi.nlm.nih.gov/pubmed/33243276
http://dx.doi.org/10.1186/s13643-020-01528-x

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