Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews

Paige B Martin · Didi Surian · Rabia Bashir · Florence T. Bourgeois · Adam G. Dunn

24 citations0/0 files storedJAMIA Open

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Meta-analysis and systematic reviews Research Data Management Practices Academic Publishing and Open Access

Abstract

OBJECTIVES: Systematic reviews of clinical trials could be updated faster by automatically monitoring relevant trials as they are registered, completed, and reported. Our aim was to provide a public interface to a database of curated links between systematic reviews and trial registrations. MATERIALS AND METHODS: We developed the server-side system components in Python, connected them to a PostgreSQL database, and implemented the web-based user interface using Javascript, HTML, and CSS. All code is available on GitHub under an open source MIT license and registered users can access and download all available data. RESULTS: system is a web-based interface to a database that collates and augments information from multiple sources including bibliographic databases, the ClinicalTrials.gov registry, and the actions of registered users. Users interact with the system by browsing, searching, or adding systematic reviews, verifying links to trials included in the review, and adding or voting on trials that they would expect to include in an update of the systematic review. The system can trigger the actions of software agents that add or vote on included and relevant trials, in response to user interactions or by scheduling updates from external resources. DISCUSSION AND CONCLUSION: We designed a publicly-accessible resource to help systematic reviewers make decisions about systematic review updates. Where previous approaches have sought to reactively filter published reports of trials for inclusion in systematic reviews, our approach is to proactively monitor for relevant trials as they are registered and completed.

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References

2004Empirical Evidence for Selective Reporting of Outcomes in Randomized Trials 2010Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up?2010The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews 2013Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias — An Updated Review 2010Dissemination and publication of research findings: an updated review of related biases 2015Using text mining for study identification in systematic reviews: a systematic review of current approaches 2017Living systematic review: 1. Introduction—the why, what, when, and how 2014Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap

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2020How to decide whether a systematic review is stable and not in need of updating: Analysis of Cochrane reviews 2020Is it time for computable evidence synthesis?2019The risk of conclusion change in systematic review updates can be estimated by learning from a database of published examples

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