The Clinical Trial Risk Tool is a browser-based tool which uses Natural Language Processing (NLP) to analyse clinical trial protocols. We are pleased to announce the publication of a technical paper on the tool.
A BibTex citation is as follows:
@article{wood2023clinical,
title={Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness},
author={Wood, Thomas A and McNair, Douglas},
journal={Gates Open Research},
volume={7},
number={56},
pages={56},
year={2023},
publisher={F1000 Research Limited}
}
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Introduction Conducting a clinical trial risk assessment is now a regulatory expectation and a cornerstone of quality management in clinical research. A risk assessment is a systematic process for identifying and evaluating events that could affect the achievement of a trial’s objectives [1]. In practice, this means examining the protocol, procedures, and trial environment to spot hazards to patient safety, data integrity or compliance.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Clinical trials are essential for advancing medical science, yet they are inherently complex and involve a wide range of risks. As a result, effective risk management in clinical trials is crucial to ensuring their successful completion. Among the various approaches to managing these risks, clinical trials Key Risk Indicators (KRIs) have become essential tools. KRIs are precise, measurable metrics that serve as early alerts for potential risk exposures in clinical studies.
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Designing a high-quality clinical trial protocol is critical for the success of any study. A protocol is the blueprint that outlines every aspect of a trial. In an ideal world, a flawless protocol would require no revisions and include only essential elements. In reality, however, the average protocol undergoes 2–3 amendments and often contains excessive data collection and overly complex entry criteria.