PDF(Pubmed)
Abstract：
Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers. The main aim of this project was to investigate how the choice of the model and prior knowledge influence the TD values of the hard-to-find relevant papers and their rank orders. A simulation study was conducted, mimicking the screening process on a dataset containing titles, abstracts, and labels used for an already published systematic review. The results demonstrated that AL model choice, and mostly the choice of the feature extractor but not the choice of prior knowledge, significantly influenced the TD values and the rank order of the elusive relevant papers. Future research should examine the characteristics of elusive relevant papers to discover why they might take a long time to be found.
摘要：
通过主动学习（AL）采用筛选优先级的软件通过根据预测的相关性对一组无序的记录进行排名，从而显着加快了筛选过程。然而,未能找到相关论文可能会改变系统综述的结果，强调识别难以捉摸的论文的重要性。发现时间（TD）衡量需要筛选多少记录才能找到相关论文，使其成为检测此类文件的有用工具。该项目的主要目的是研究模型和先验知识的选择如何影响难以找到的相关论文的TD值及其等级顺序。进行了模拟研究，在包含标题的数据集上模仿筛选过程，摘要,以及用于已经发布的系统评价的标签。结果表明，AL模型选择，主要是特征提取器的选择，而不是先验知识的选择，显着影响TD值和难以捉摸的相关论文的排名顺序。未来的研究应该检查难以捉摸的相关论文的特征，以发现为什么它们可能需要很长时间才能被发现。
