发表机构
Interdisciplinary Institute, University of Aberdeen; University of Aberdeen; Indiana University; University of Manchester; Aberdeen Centre for Evaluation, University of Aberdeen; University of Auckland（阿伯丁大学跨学科研究所; 阿伯丁大学; 印第安纳大学; 曼彻斯特大学; 阿伯丁大学阿伯丁评估中心; 奥克兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍了基于LLM的INSPECT-AI工具，结合INSPECT-SR框架与RIPE-O本体，生成含140项评估的RIPE-KG，用于辅助人工评估已发表RCT的研究完整性。
AI 中文摘要
随机对照试验（RCT）的系统评价常被用作临床护理指南的证据，这类证据需符合高研究完整性标准，以防止低质量或虚假研究成果影响临床护理。然而，评估已发表RCT的研究完整性是一项复杂且需人工完成的工作，可能导致不同人工评估者的意见存在差异。本文介绍了INSPECT-AI，这是一种基于大语言模型（LLM）的交互式工具，它基于社区认可的INSPECT-SR框架以及用于记录评估过程来源的研究完整性来源与证据本体（RIPE-O），协助人工评估者完成已发表RCT的研究完整性评估。此外，我们还提出了研究完整性来源与证据知识图谱（RIPE-KG），这是由INSPECT-AI生成并使用RIPE-O描述的、针对95篇RCT出版物的140项专家研究完整性评估的初始集合。
英文摘要
Systematic reviews of Randomised Controlled Trials (RCTs) are routinely used as evidence for clinical care guidelines. Such evidence has to meet high research integrity standards to prevent low quality or false research outputs influencing the clinical care. However, assessing research integrity of published RCTs is a complex process requiring manual effort, and potentially resulting in diverse opinions of the human assessors. This paper describes INSPECT-AI, an LLM-based interactive tool that assists human reviewers with research integrity assessments of published RCTs based on the community approved INSPECT-SR framework, and the Research Integrity Provenance and Evidence ontology (RIPE-O) for documenting the provenance of the assessment process. In addition, we present the Research Integrity Provenance and Evidence knowledge graph (RIPE-KG), an initial set of 140 expert research integrity assessments of 95 RCT publications generated by INSPECT-AI and described using RIPE-O.
Comments16 pages
