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

Authors :
Fionn Byrne
Laura Hofstee
Jelle Teijema
Jonathan De Bruin
Rens van de Schoot
Source :
Systematic Reviews, Vol 13, Iss 1, Pp 1-8 (2024)
Publication Year :
2024
Publisher :
BMC, 2024.

Abstract

Abstract 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.

Details

Language :
English
ISSN :
20464053
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Systematic Reviews
Publication Type :
Academic Journal
Accession number :
edsdoj.6de3fc8fc114c67a54928d81d327387
Document Type :
article
Full Text :
https://doi.org/10.1186/s13643-024-02587-0