Makita—A workflow generator for large-scale and reproducible simulation studies mimicking text labeling

Teijema, Jelle Jasper; van de Schoot, Rens; Ferdinands, Gerbrich; Lombaers, Peter; de Bruin, Jonathan

doi:10.1016/j.simpa.2024.100663

Software Impacts

2024

DOI: 10.1016/j.simpa.2024.100663

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Makita—A workflow generator for large-scale and reproducible simulation studies mimicking text labeling

Jelle Jasper Teijema,

Rens van de Schoot,

Gerbrich Ferdinands

et al.

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Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study

Byrne,

Hofstee,

Teijema

et al. 2024

Syst Rev

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Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers. The main aim of this project was to investigate how the choice of the model and prior knowledge influence the TD values of the hard-to-find relevant papers and their rank orders. A simulation study was conducted, mimicking the screening process on a dataset containing titles, abstracts, and labels used for an already published systematic review. The results demonstrated that AL model choice, and mostly the choice of the feature extractor but not the choice of prior knowledge, significantly influenced the TD values and the rank order of the elusive relevant papers. Future research should examine the characteristics of elusive relevant papers to discover why they might take a long time to be found.

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Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study

Byrne,

Hofstee,

Teijema

et al. 2024

Syst Rev

View full text Add to dashboard Cite

show abstract

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Makita—A workflow generator for large-scale and reproducible simulation studies mimicking text labeling

Cited by 1 publication

References 5 publications

Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study

Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study

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