This month, I presented the Clinical Trial Risk Tool at the Plotly Dash in Action webinar. I was interviewed by Plotly’s Community Manager Adam Schroeder. You can watch the relevant part of the webinar below.
The Clinical Trial Risk Tool was one of four interactive apps presented as part of the webinar. The speakers at the webinar were:
Screenshot of Matteo Trachsel’s Thermoplan dashboard which calculates the carbon footprint of coffee machine usage.
Screenshot of Agah Karakuzu’s dashboard which allows neuroscientists to assess the reproducibility of T1 values across different sites and vendors where researchers used the same research protocol. (T1 is the time it takes water molecules in the brain to return to their original state following a magnetic pulse).
If you would like to cite the tool alone, you can cite:
Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 doi: 10.12688/gatesopenres.14416.1.
A BibTeX entry for LaTeX users is
@article{Wood_2023, doi = {10.12688/gatesopenres.14416.1}, url = {https://doi.org/10.12688%2Fgatesopenres.14416.1}, year = 2023, month = {apr}, publisher = {F1000 Research Ltd}, volume = {7}, pages = {56}, author = {Thomas A Wood and Douglas McNair}, title = {Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness}, journal = {Gates Open Research} }
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.
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.
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.
