ArticleSystematic reviews2024
Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study.
Article in Systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- To include or not to include? A prescription from the pharmacy on how to use active learning-assisted screening in systematic reviews.Systematic reviews · 2026Article
- Comparing the performance of narrow vs. broad search strategies when using machine learning-based software for title/abstract screening.Journal of the Medical Library Association : JMLA · 2026Article
- Screenathon 2.0: human-AI collaborative screening applied to patient-generated health data.Scientific reports · 2026Article
- The hunt for the last relevant paper: blending the best of humans and AI.European journal of psychotraumatology · 2025Article
- ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts.Patterns (New York, N.Y.) · 2025Article
- Machine learning to optimize literature screening in medical guideline development.Systematic reviews · 2024Article
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Authors and funding
5 authors.
Funding
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.