December 2, 2022
We had the pleasure of sitting down with Central Michigan Medical student, Hassan Kobeissi to learn more about his accomplishments using Nested Knowledge and how it has helped his medical school journey. He also shares some words of advice to other medical students who are looking to research to advance their careers.
Hassan Kobeissi is a third year medical student at the University of Central Michigan. He is looking to specialize in an interventional field like neurosurgery interventional radiology. To date much of his research work focuses on mechanical thrombectomy for stroke and aneurysm coiling.
Hassan began using Nested Knowledge in 2020. When he first started he had no idea what a systematic review was and how to go about completing one. Fortunately, Nested Knowledge software was “logical” and provided a streamlined method of conducting a systematic review.
Two years later, Hassan has completed many nests within Nested Knowledge and has been listed as an author in over 10 publications and counting. In his interview, he explains how Nested Knowledge has expanded his medical career and provided impactful collaborations. Scroll below to learn more about how Nested Knowledge has expanded his research and career.
Hassan Kobeissi – My time at the University of Michigan, I was in two labs. One was the A lab that was a neurosurgical lab that focused on glioblastoma and that was bench work. So I was sitting there pipetting and injecting mice. It was your typical bench work. When you think of lab research, that’s what I was doing. Then I was in another lab, that was the General Surgery lab and for those medically minded. I was in the [ECMO] lab where we would use the ECMO machine on sheep to try and make it better for humans essentially improve it. It was pretty neat. I didn’t really think of my research before medical school, but I was not a fan of bench work because to me, I didn’t see the clinical impact.
So, bringing it to present day, a lot of my work focuses on mechanical thrombectomy for stroke and aneurysm coiling and to me it’s nice because this is actual clinical stuff that makes a difference. So we’re looking at actual outcomes in patients interventions and patients. We are seeing what certain risk factors are and how they might contribute to patients doing better or worse…So I can see the actual impacts of my work much more clearly.
Hassan Kobeissi – When I was first introduced to Nested Knowledge, I had zero publications, I did not even know what a systematic review was. I didn’t know what a Meta-analysis was…but I was motivated and NK provided the tools for me to learn through and grow.
My first project was looking at thrombectomy without thrombolysis and back then, it might as well have been Greek to me. But I saw the process of how you first come up with a topic and then you think about what do you want to do. So you come up with your very focused topic and then you run the search.
So we ran the search within Nested Knowledge, and the search return called 300 papers. Then it was easy for me to see that the next step is to figure out what papers we want to keep. So I observed how the exclusion criteria was formed and I actually did the screening for that study. Even as my first study, it was very intuitive for me. So, I would go and see the titles and the abstracts of the studies that were returned from the search and I would know the study is a secondary analysis. It’s not original data. So we have to exclude it.
So the ease of the workflow made it very easy for me to understand what was happening because if I was going into an Excel sheet and manually returning results from PubMed it would have not been feasible, frankly, for me to even participate in that project.
You know, the the secret lies in having a very systematic, don’t mind the pun, a systematic methodology to producing the work. For me, what facilitated that was Nested Knowledge because the software walks you through each step. From inception of the search to the screening, to the extraction. Everything is in one software. So it’s a very logical flow.
So you come up with a topic, you run the search and then right there you have the the search results and you get to work on the screening. Next step would be the tagging and then next up is the extraction. So the flow is really what impacts the efficiency of the projects. Because if you’re all over the place it’s hard to produce large volumes of work because of the inefficiency that causes. So you know, the workflow is really the number one thing…the ease of the workflow made it very easy for me to understand what was happening.
Try it: View an interactive figure from one of Hassan's recent publications using Nested Knowledge Basilar Artery - thrombectomy vs. thrombolysis. Check out the key insights from this nest by scrolling down.
Hassan Kobeissi – Instead of having to manually pull up an Excel sheet, which I’ve never even done, you have just the outline of the workflow which is fluid. You run the search, you configure your criteria for exclusion. If you are on a team, you can delegate the screening to two people. Now you can take the screening and be the adjudicator. Then you can see that the screening is completed and you know what the next step is gonna be…I would say these are the things that make NK great.
Hassan Kobeissi – I would say my favorite feature in Nested Knowledge is the extraction page. I really like the intuitive nature of the extraction page.
It’s all laid up for you on one screen. It’s a very nice workflow. You say here is the data I need and there it is on the left side and you can go through the paper and find a number here…It’s way easier to avoid mistakes then having to go in a spreadsheet and look all over the place.
Now, in terms of what I think is my favorite feature within Nested Knowledge that will push research forward, it is definitely the the tagging feature because that opens up a whole new world of transparency where interested readers can go into the nest and they can say, yep, here’s the variable I’m interested in, how did each study report this, and what was each study’s outcome…It’s a perfect feature for data transparency and visualization.
Hassan Kobeissi – So, I would first identify a topic you’re interested in. That’s number one thing. And then at that point, there’s a bunch of different ways you could go about it. But the way that I think the most success could be had, is simply reach out to people and you tell them, hey, I really like your work! I would love to join with you on a project. I have and that has never not worked out for me, and people are generally very receptive to having more people join the team.
Like I said, There’s always work to go around. I think that medical students or researchers should show initiative. Then, willingness to work and learn. There’s definitely a place for you. Don’t be shy about reaching out and expressing your interest, and even if you want to reach out to me, go for it. Like I said, we’re always looking for people.
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