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Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs

arXiv AI/CL/LG（论文）T2
21:1708/07, 09:17 PMPaperRead the original ↗Export MD ↓

Score

72
Information gain8 ×0.3

提出新工具和知识图谱数据集，为该领域提供了可复用的资源和增量信息。

Content quality7 ×0.2

方法描述详实，但缺乏对工具效能的实证评估和对比实验。

Practical value8 ×0.2

可直接应用于临床证据审查场景，有助于降低人工成本并提高评审一致性。

Timeliness9 ×0.15

结合当下LLM应用热点，及时回应了循证医学中对透明度和可溯源性的需求。

Source reliability8 ×0.15

来自arXiv预印本，未经同行评审，但作者团队具备相关学术背景且框架基于社区认可的规范。

Base 79.5 × T2 Source weight ×0.9 = 72

Curation line 70Selected

Why selected

The paper proposes an innovative tool and knowledge graph with practical value, yet requires more validation and peer review.

AI commentary

「This study offers an AI-assisted solution for medical evidence review, valuable for researchers in medical informatics, clinical research methodology, and LLM applications. Although currently a preprint, the RIPE-KG dataset could serve as a foundation for future research.」

Quick overview

This paper introduces INSPECT-AI, an LLM-based interactive tool that assists human reviewers in assessing the research integrity of published randomised controlled trials (RCTs) according to the INSPECT-SR framework, and the RIPE-O ontology for documenting the provenance of the assessment process. The authors also present RIPE-KG, a knowledge graph containing 140 expert research integrity assessments of 95 RCT publications, generated using INSPECT-AI and described with RIPE-O. This work aims to improve the reliability and transparency of evidence used in systematic reviews and clinical guidelines.

  • INSPECT-AI combines LLMs with provenance knowledge graphs to assist human reviewers in RCT research integrity assessment.
  • The RIPE-O ontology is used to document provenance of the assessment process, enhancing transparency.
  • RIPE-KG contains 140 expert assessments covering 95 RCT publications.
  • It aims to reduce manual review burden and improve consistency and evidence quality.

Full text

This is an AI-generated Chinese digest; use "View original" for the source text.

Systematic reviews are essential for clinical guidelines, but assessing research integrity of RCT publications is labor-intensive and subjective. INSPECT-AI uses large language models to assist reviewers in evaluating papers based on the INSPECT-SR framework, and the RIPE-O ontology to document reasoning, decisions, and revisions. RIPE-KG, the initial dataset, contains 140 expert-involved assessments across 95 RCT publications. The authors emphasize that the system is designed not to replace human review but to enhance traceability and consistency, providing more reliable infrastructure for evidence-based medicine.

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