Medical experts often use a method called network meta-analysis to compare many treatments at once. This process helps doctors decide which medicine works best for patients. However, these analyses sometimes combine two very different types of studies into one calculation.
One type is a high-quality randomized trial, while the other is a non-randomized study. When researchers mix these together without using special math to correct for the differences, it is called naive pooling. This can make the results less reliable and harder to trust when making medical decisions.
This upcoming project will look at how often this happens in published papers. The team will check if authors are following proper rules and reporting their methods clearly. They also want to see if the final rankings of treatments change when the data is handled correctly.
The goal is to help improve how journals review papers and how experts create medical guidelines. By identifying these issues, they can ensure that doctors have the most accurate information possible when choosing treatments for their patients.