Scoping reviews, a type of literature review, require intensive human effort
to screen large numbers of scholarly sources for their relevance to the review
objectives. This manuscript introduces GPTscreenR, a package for the R
statistical programming language that uses the GPT-4 Large Language Model (LLM)
to automatically screen sources. The package makes use of the chain-of-thought
technique with the goal of maximising performance on complex screening tasks.
In validation against consensus human reviewer decisions, GPTscreenR performed
similarly to an alternative zero-shot technique, with a sensitivity of 71%,
specificity of 89%, and overall accuracy of 84%. Neither method achieved
perfect accuracy nor human levels of intraobserver agreement. GPTscreenR
demonstrates the potential for LLMs to support scholarly work and provides a
user-friendly software framework that can be integrated into existing review
processes.

使用 GPT-4 大型语言模型（LLM）的 GPTscreenR 软件包及链式思维技术，能够自动筛选大量学术资源并在效能和性能方面表现良好，提供了一个可集成到现有审查流程中的用户友好的软件框架。