ArticleFrontiers in research metrics and analytics2023
Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.
Article in Frontiers in research metrics and analytics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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.
The trial behind it
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Article
- Strategizing AI utilization for psychological literature screening: A comparative analysis of machine learning algorithms and key factors to consider.Research synthesis methods · 2026Article
- 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
- Associations between self-reported symptoms and circulating protein biomarkers: A scoping review protocol.PloS one · 2026Article
- Determinants and outcomes of advanced practice nurses' leadership behaviours: an AI-aided mixed-methods systematic review protocol.Systematic reviews · 2025Article
- The hunt for the last relevant paper: blending the best of humans and AI.European journal of psychotraumatology · 2025Article
- 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
- ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts.Patterns (New York, N.Y.) · 2025Article
- Virtual reality offerings for wellbeing for and by marginalized populations: A scoping review on equity and intersectionality.Global mental health (Cambridge, England) · 2025Review
- Article
- Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in embedding space is high.Research synthesis methods · 2025Article
- Machine learning to optimize literature screening in medical guideline development.Systematic reviews · 2024Article
- Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study.Systematic reviews · 2024Article
- The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.Systematic reviews · 2024Article
- In search of prosociality in rodents: A scoping review.PloS one · 2024Article
- Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records.Systematic reviews · 2023Article
- Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.Frontiers in research metrics and analytics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies. Methods: Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance. Results: Lighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone. Discussion: The study's findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.
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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.