Show simple item record

dc.contributor.authorMuller, Ashley
dc.contributor.authorBerg, Rigmor
dc.contributor.authorMeneses Echavez, Jose Francisco
dc.contributor.authorAmes, Heather Melanie R
dc.contributor.authorBorge, Tiril Cecilie
dc.contributor.authorJacobsen Jardim, Patricia Sofia
dc.contributor.authorCooper, Chris
dc.contributor.authorRose, Christopher James
dc.date.accessioned2023-02-13T09:22:43Z
dc.date.available2023-02-13T09:22:43Z
dc.date.issued2023-01-17
dc.description.abstractBackground Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews.<p> <p>Methods This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. <p>Conclusion The results of this study will allow us to quantitatively estimate the effect of ML adoption on resource use and time-to-completion, providing our organization and others with better information to make high-level organizational decisions about ML.en_US
dc.identifier.citationMuller AE, Berg RC, Meneses Echavez, Ames H, Borge TC, Jacobsen Jardim PS, Cooper C, Rose CJ. The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study. Systematic Reviews. 2023;12(7):1-8en_US
dc.identifier.cristinIDFRIDAID 2116279
dc.identifier.doihttps://doi.org/10.1186/s13643-023-02171-y
dc.identifier.issn2046-4053
dc.identifier.urihttps://hdl.handle.net/10037/28536
dc.language.isoengen_US
dc.publisherBMCen_US
dc.relation.journalSystematic Reviews
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleThe effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot studyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record

Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)