The Power and Peril of TriNetX in Rhinology

TriNetX has quickly become one of the most popular research tools in rhinology. At the 2026 American Rhinologic Society spring meeting, more than one in ten abstracts used the platform, no doubt drawn by its huge sample sizes and built-in statistics. However, our concern is that many of these studies suffer from unrecognized biases and potentially misleading results. In this article, we tell the narrative of our experience using TriNetX, highlighting how easy it can be to produce incorrect results. Common biases that often arise with large databases are presented, as well as examples relevant to rhinology. We argue that researchers need to ensure that their team has expertise in subject matter, study design and potential biases, and the platform's logic and limitations.