ArticleSystematic reviews2020
Statistical stopping criteria for automated screening in systematic reviews.
Article in Systematic reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Wearable Sensing for Personal Thermal Comfort in the Built Environment: A Systematic Review of the Gap from Sensing to Actuation.Sensors (Basel, Switzerland) · 2026Pooled it
- A meta-analysis assessing the effectiveness of demand-side interventions for sustainable food consumption and food waste reduction.Nature food · 2026Pooled it
- Addressing cancer survivors' information needs and satisfaction: a systematic review of potential intervention components for survivors with a rare cancer type.Orphanet journal of rare diseases · 2024Pooled it
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.Clinical child and family psychology review · 2026Article
- A comparative scoping review approach: identifying the intersection of carbon, biodiversity, and water offsetting.MethodsX · 2026Article
- When to stop reviewing: validation of stop criteria in ASReview.BMC medical research methodology · 2026Article
- Strategizing AI utilization for psychological literature screening: A comparative analysis of machine learning algorithms and key factors to consider.Research synthesis methods · 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
- Study protocol for evaluating automation of systematic review processes with EPPI-Reviewer and Copilot 365 in updating the cataract evidence gap map.Systematic reviews · 2026Article
- A hybrid approach to large-scale systematic literature reviews: combining automated tools with text-mining techniques.BMC research notes · 2026Article
- Don't Stop Me Now, `Cause I'm Having a Good Time Screening: Evaluation of Stopping Methods for Safe Use of Priority Screening in Systematic Reviews.Cochrane evidence synthesis and methods · 2026Article
- Extrapolation steps in quantitativeCurrent research in toxicology · 2026Review
- Evaluation of semi-automated record screening methods for systematic reviews of prognosis studies and intervention studies.Research synthesis methods · 2025Article
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 2025Article
- Artificial intelligence as team member versus manual screening to conduct systematic reviews in medical sciences.iScience · 2025Article
- Article
- Artificial Intelligence and Automation in Evidence Synthesis: An Investigation of Methods Employed in Cochrane, Campbell Collaboration, and Environmental Evidence Reviews.Cochrane evidence synthesis and methods · 2025Article
- Rapid reviews methods series: guidance on rapid scoping, mapping and evidence and gap map ('Big Picture Reviews').BMJ evidence-based medicine · 2025Article
- Search strategies for systematic reviews in reproductive medicine: a narrative review and practical guide.Journal of assisted reproduction and genetics · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents. However, current approaches lack robust stopping criteria, so that reviewers do not know when they have seen all or a certain proportion of relevant documents. This means that such systems are hard to implement in live reviews. This paper introduces a workflow with flexible statistical stopping criteria, which offer real work reductions on the basis of rejecting a hypothesis of having missed a given recall target with a given level of confidence. The stopping criteria are shown on test datasets to achieve a reliable level of recall, while still providing work reductions of on average 17%. Other methods proposed previously are shown to provide inconsistent recall and work reductions across datasets.
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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.