 Hi everyone. I hope you're having a wonderful day. My name is Alexander Fonseca. Currently, I'm doing a PhD program at Michigan State University under supervision of Dr. Anna DeConor. The title of this overall episode presentation is an interactive forest plot for visualizing risk of bias assessment. Well, the line for this presentation we are going to start with some basic concepts like forest plot, risk of bias visualization and assessment, motivation, materials and methods, results, and finally, conclusion. To start, we are going to introduce the concept of forest plot. We know that forest plot is the most common way to summarize and combine the results of multiple source evaluation. It's pretty useful in systematic reviews and meta-analysis. The main goal of this kind of graph is to look for patterns. Another important component of this work is the risk of bias assessment and visualization. We know that the risk of bias assessment is a critical step in the process of systematic review. And the deputation of these results are pretty important in order to establish the internal and external validity of the studies included. There are multiple ways to visualize the results of that assessment. But this one, that I'll pick, is the most common way, which is called a traffic light plot. So you can see here that the color indicates the level of the judgment of the risk of bias assessment. For example, the green ones indicate that the risk of bias is low. Mainly the motivation for this work was to start around six or seven months ago. So we start developing a website to show the results from a systematic review. We are looking in our forward package that allows you to visualize the forest plot as well as the risk bias assessment. We found this really good package like forest risk warnings. But our idea is we thought that could be a good idea to combine them in order to create an interactive forest plot in which the risk of bias assessment will be included. So the materials are resolved for this website are presented in the next slide. Well, this is a calling view of the website that was built as a powerful leaving systematic review project. You can see at the left side of the screen that there are multiple tabs, but we are interested in the forest plot of this website. Well, once there, you can see that there is a forest plot, a default forest plot that changed according to the selection of the final user. And in these two drop down menus, the user can select the peck size. So for example, let me select problem ratio, modify the table exposure to indirect measure of exposure. And so you can see a new forest plot. So let me go back to the default forest plot of the ratio and the indirect measure exposure. This is the default forest plot. Well, the idea with this forest plot is that it contains three novel functionalities. The first one is that the user can hover over the point estimate square on any study in the forest plot. And x is the spire of the point estimate and the confidence interval for the measure of association and the outcome as well. So you can see the same in any of the squares here. Second user can click here on the point estimate square on any study and the risk of bias judgment is displayed in an interactive table alongside the forest plot, where our color system indicates the level of bias that was considered by the review team. It is possible to visualize multiple associations at once by clicking in multiple point estimate squares. Finally, in this reason bias table, the user can click on a row and above a window appears to contain the justification of the reason bias for each domain evaluated. So you can see here to confirm the evaluation, missing data, assessment and so on for any other row in the table. Well, for the development of that novel functionalities, we mainly use three package ggplot and gggraph from a table and shine as well as the main platform to develop the website. As conclusion or ideas for diffusion, we wait to implement our package. We know that we have to improve some functionalities in order to make that launch. And we are thinking also into develop a web app to make it accessible to people without a short background in our room. Finally, we are thinking that we are completely open to work with other groups that are working in the same idea visualization the results of this matter review. So yeah, we are more than happy to collaborate with other with other groups that are working the same way. Finally, thank you for your time. And I would like to thanks to the national board for this. Thank you.