ArticleSystematic reviews2021
srBERT: automatic article classification model for systematic review using BERT.
Article in Systematic reviews, 2021. 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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Performance of Zero-Shot Classifiers for Categorizing RCT Abstracts by Intervention Type: Validation Study.JMIR medical informatics · 2026Article
- Article
- Beyond human gold standards: A multimodel framework for automated abstract classification and information extraction.Research synthesis methods · 2026Article
- ReviewGenie: a novel automated system for systematic reviews-an exploratory study in speech and language disorders.Systematic reviews · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- Artificial Intelligence and Machine Learning to Improve Evidence Synthesis Production Efficiency: An Observational Study of Resource Use and Time-to-Completion.Cochrane evidence synthesis and methods · 2025Article
- A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening.Research synthesis methods · 2025Article
- MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model.Nature communications · 2025Article
- Evaluating GPT Models for Automated Literature Screening in Wastewater-Based Epidemiology.ACS environmental Au · 2025Article
- The WHO's critical bacteria list: scientific response eight years after its implementation and development of an AI-based tool for its monitoring.Frontiers in pharmacology · 2025Article
- Reported methodological quality of medical systematic reviews: Development of an assessment tool (ReMarQ) and meta-research study.Research synthesis methods · 2025Article
- Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study.JMIR formative research · 2024Article
- Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.Systematic reviews · 2024Article
- Assessing the performance of large language models in literature screening for pharmacovigilance: a comparative study.Frontiers in drug safety and regulation · 2024Article
- Semi-automating abstract screening with a natural language model pretrained on biomedical literature.Systematic reviews · 2023Article
- Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.Systematic reviews · 2023Article
- The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study.Systematic reviews · 2023Article
- BertSRC: transformer-based semantic relation classification.BMC medical informatics and decision making · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystematic reviews (SRs) are recognized as reliable evidence, which enables evidence-based medicine to be applied to clinical practice. However, owing to the significant efforts required for an SR, its creation is time-consuming, which often leads to out-of-date results. To support SR tasks, tools for automating these SR tasks have been considered; however, applying a general natural language processing model to domain-specific articles and insufficient text data for training poses challenges.
methodsThe research objective is to automate the classification of included articles using the Bidirectional Encoder Representations from Transformers (BERT) algorithm. In particular, srBERT models based on the BERT algorithm are pre-trained using abstracts of articles from two types of datasets, and the resulting model is then fine-tuned using the article titles. The performances of our proposed models are compared with those of existing general machine-learning models.
resultsOur results indicate that the proposed srBERT
conclusionsOur research shows the possibility of automatic article classification using machine-learning approaches to support SR tasks and its broad applicability. However, because the performance of our model depends on the size and class ratio of the training dataset, it is important to secure a dataset of sufficient quality, which may pose challenges.
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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.