Curtin Institute for Computation, Curtin University, Perth, Australia.
School of Population Health, Curtin University, Perth, Australia.
Syst Rev. 2021 Apr 1;10(1):93. doi: 10.1186/s13643-021-01635-3.
Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening.
Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples.
Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers.
In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.
系统评价和荟萃分析提供了最高级别的证据，以帮助制定政策和实践，但它们严格的性质与大量的时间和经济需求有关。筛选标题和摘要的部分是审查过程中最耗时的部分，分析师需要手动审查数千篇文章，平均需要 33 天。旨在简化筛选过程的新技术提供了初步的有希望的结果，但目前的方法存在局限性，这些工具的广泛使用也存在障碍。在本文中，我们介绍并报告了 Research Screener 的初步效用证据，这是一种用于促进摘要筛选的半自动化机器学习工具。
进行了三组分析（模拟、交互和敏感性），通过模拟和真实示例提供了工具效用的证据。
Research Screener 在九项系统评价和两项范围评价中实现了 60%至 96%的工作量节省。来自真实世界交互分析的结果表明，与手动筛选相比，节省了 12.53 天，相当于节省了 2444 美元的资金。保守地说，我们的结果表明，扫描系统搜索中识别的总文章池的 50%的分析师很可能已经识别出了 100%的合格论文。
鉴于这些发现，Research Screener 能够减轻希望进行全面系统评价的研究人员的负担，而不会降低他们努力实现的科学严谨性。
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