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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Muller, Ashley Elizabeth Berg, Rigmor C. Meneses-Echavez, Jose Francisco Ames, Heather M. R. Borge, Tiril C. Jardim, Patricia Sofia Jacobsen Cooper, Chris Rose, Christopher James |
| Abstract | Background 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. 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. 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. |
| Related Links | https://systematicreviewsjournal.biomedcentral.com/counter/pdf/10.1186/s13643-023-02171-y.pdf |
| Ending Page | 8 |
| Page Count | 8 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 20464053 |
| DOI | 10.1186/s13643-023-02171-y |
| Journal | Systematic Reviews |
| Issue Number | 1 |
| Volume Number | 12 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2023-01-17 |
| Access Restriction | Open |
| Subject Keyword | Medicine Public Health Biomedicine Statistics for Life Sciences Health Sciences Systematic review Machine learning Artificial intelligence Research waste Business process management Medicine/Public Health |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine |
| Journal Impact Factor | 6.3/2023 |
| 5-Year Journal Impact Factor | 4.5/2023 |
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