Publication announced

Publication announced

A technical paper on the Clinical Trial Risk Tool has been published in Gates Open Research!

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.

  • Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness [version 1; peer review: 1 approved with reservations]. Gates Open Res 2023, 7:56 doi.org/10.12688/gatesopenres.14416.1

Open access natural language processing paper

Read Gates Open Research paper

Our publication is open access. Click to read online or download as PDF.

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}
}

Clinical trial team structure and best practices

Clinical trial team structure and best practices

Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of a clinical trial is strongly dependent on the structure and coordination of the teams managing it. Given the high stakes and significant impact of every decision made during the trial, it is essential for each team member to collaborate efficiently in order to meet strict deadlines, comply with regulations, and ensure reliable results.

How to read and extract value from a clinical trial protocol

How to read and extract value from a clinical trial protocol

Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Clinical trial protocols are detailed master-plans of a study – often 100–200 pages long – outlining objectives, design, procedures, eligibility and analysis. Reading them cover-to-cover can be daunting and time-consuming. Yet careful review is essential. Protocols are the “backbone” of good research, ensuring trials are safe for participants and scientifically valid [1]. Fortunately, there are systematic strategies to speed up review and keep it objective.

How accurate is the Clinical Trial Risk Tool?

How accurate is the Clinical Trial Risk Tool?

Introduction People have asked us often, how was the Clinical Trial Risk Tool trained? Does it just throw documents into ChatGPT? Or conversely, is it just an expert system, where we have painstakingly crafted keyword matching rules to look for important snippets of information in unstructured documents? Most of the tool is built using machine learning techniques. We either hand-annotated training data, or took training data from public sources. How We Trained the Models inside the Clinical Trial Risk Tool The different models inside the Clinical Trial Risk tool have been trained on real data, mostly taken from clinical trial repositories such as clinicaltrials.