ArticleBMC research notes2026
A hybrid approach to large-scale systematic literature reviews: combining automated tools with text-mining techniques.
Article in BMC research notes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Toward Digital Self-Monitoring of Mental Health in the General Population: Scoping Review of Existing Approaches to Self-Report Measurement.JMIR mental health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveSemi-automated tools used during the preliminary screening of articles in systematic reviews can start with a small set of seed articles and actively learn from human decisions to prioritise more relevant articles for subsequent screening. However, given that these tools are vulnerable to biases and lack clear stopping criteria, their performance in large-scale systematic reviews remains uncertain, especially in reviews covering broad subject areas that require a substantial number of representative seed articles. This article presents a hybrid approach that uses text-mining techniques combined with a semi-automated tool to effectively reduce, screen, and validate a large cohort of articles (N = 90,871).
resultA preliminary evaluation using simulations indicated that this approach has the potential to craft a comprehensive collection of seed articles that covers broad subject areas for semi-automated tools in a large-scale systematic review. The strengths and limitations of using a semi-automated tool alone in such a context are discussed. Our approach increases the efficiency of automated tools by providing a larger and more focused selection of articles to start with, optimising the learning process for those tools and reducing biases. Additionally, our approach could increase the transparency and reusability of keywords for future review updates.
Indexed as
Identifiers
What OpenQuestion holds
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.