ArticleCochrane evidence synthesis and methods2023
Machine learning for accelerating screening in evidence reviews.
Article in Cochrane evidence synthesis and methods, 2023. 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 policies in neurology journals: a cross-sectional analysis.Frontiers in neurology · 2026Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence reviews are important for informing decision-making and primary research, but they can be time-consuming and costly. With the advent of artificial intelligence, including machine learning, there is an opportunity to accelerate the review process at many stages, with study screening identified as a prime candidate for assistance. Despite the availability of a large number of tools promising to assist with study screening, these are not consistently used in practice and there is skepticism about their application. Single-arm evaluations suggest the potential for tools to reduce screening burden. However, their integration into practice may need further investigation through evaluations of outcomes such as overall resource use and impact on review findings and recommendations. Because the literature lacks comparative studies, it is not currently possible to determine their relative accuracy. In this commentary, we outline the published research and discuss options for incorporating tools into the review workflow, considering the needs and requirements of different types of review.
Indexed as
Identifiers
What Socratic 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 Socratic graph.