ArticlePloS one2024
Usability and agreement of the SWIFT-ActiveScreener systematic review support tool: Preliminary evaluation for use in clinical research.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial intelligence as team member versus manual screening to conduct systematic reviews in medical sciences.iScience · 2025Article
- Digital Tools to Support the Systematic Review Process: An Introduction.Journal of evaluation in clinical practice · 2025Review
- Ketamine in the effective management of chronic pain, depression, and posttraumatic stress disorder for Veterans: A meta-analysis and systematic review.Frontiers in psychiatry · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systematic reviews (SRs) employ standardized methodological processes for synthesizing empirical evidence to answer specific research questions. These processes include rigorous screening phases to determine eligibility of articles against strict inclusion and exclusion criteria. Despite these processes, SRs are a significant undertaking, and this type of research often necessitates extensive human resource requirements, especially when the scope of the review is large. Given the substantial resources and time commitment required, we investigated a way in which the screening process might be accelerated while maintaining high fidelity and adherence to SR processes. More recently, researchers have turned to artificial intelligence-based (AI) software to expedite the screening process. This paper evaluated the agreement and usability of a novel machine learning program, Sciome SWIFT-ActiveScreener (ActiveScreener), in a large SR of mental health outcomes following treatment for PTSD. ActiveScreener exceeded the expected 95% agreement of the program with screeners to predict inclusion or exclusion of relevant articles at the title/abstract assessment phase of the review and was reported to be user friendly by both novice and seasoned screeners. ActiveScreener, when used appropriately, may be a useful tool when performing SR in a clinical context.
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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.