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OK. All sorted for like recordings and stuff. Yeah. All good to go. Awesome, cool. Right. Um Sorry, I can't get my camera to work. Otherwise it would have been nice to see you guys, but um it, it is what it is. Um So hello everybody. Uh Welcome to the NMR Academy. This is the first session of 10 which we'll be doing around a systematic review, meta analysis. Uh My name is NJ, I am the founder and president of NMR. We launched in 2022 and since then, we have been trying to break down some barriers into research. We've been trying to assist people to finding scope to conduct their own studies. We give them skills like you're here to learn uh networking. We do our own projects on the mentorship scheme and fundamentally the the aim of the N Mra is to break down some of the hierarchy. I think a lot of people struggle with identifying what they wanna do with research, how they want to start with their first few projects, what areas of interest they have. So we really want to assist with getting that barrier down as much as possible. Um, I don't think the Zoom audio is working because I can't hear anything. But anyway, mm, I can't hear anything. So, I don't know if that's all life, if it's worth you. Nobody said anything. So it should be, I'll take your word for it. Um, anyway, so just moving forward the, I can hear you. Ok. All good. Fantastic. Um, maybe it just canceled out my own voice. But anyway, um, to kind of go into a little bit more detail about what we're doing today. So this is the first session of the Academy Series. I've put the schedule up on here. It's also available on our website and it will automatically come up again on the meal or you can, we'll keep posting about it on our social media. So hopefully no one falls through the cracks and take a screenshot of this if it helps. Um But yeah, so today is obviously the first session which is introduction then and coming up with a research question, then we're gonna move on to protocol search strategy, screening risk of bias data extraction, this meta analysis. And then finally the write up. So very much a a whistle stop sort of every single section. We're gonna go through all of what it takes to conduct a, a study. Um And really make sure that you've, by the end of it, understood every single step. Um If you are sticking around with us to do the mentorship scheme then it's kind of a double whammy because we, the mentorship scheme starts in June. So by the time you start the program with us, you will have already had the first four sessions. So you can immediately implement those concepts with us and conduct your own study. And then as the weeks go on, you'll have picked up the rest of the skills and data extraction, synthesis and so forth and you can apply those with us as well when you get to that level of your, your study. So it should all come together quite nicely with an opportunity to apply your skills and also to learn them from scratch as well. Um Naturally, that's um something that that's ongoing. So do feel free to revisit these uh the recordings will be made live afterwards. And um you know, I think this is one of those skills that coming back to that quite regularly will be of assistance. And um yeah, so that's kind of a very brief overview of what we do with N Mra, what we're trying to achieve with the Academy for now, uh later down the line, there will be more things that we get on with. So we're doing Biostat and then we'll also cover other statistical uh techniques. We'll also cover other other research designs. So if you don't want to do systematic review meta analysis, maybe you prefer to do a case study or a editor or something else, we will get to that in, in due course. But first we wanna nail down exactly how to do a systematic review meta analysis. So to get off, uh just to start off with, I think the first thing we think about before we just start to refine a research question is to look at, well, what really is is a good research question? What, what should that mean in the context of a systematic meta analysis? So immediately I think you wanna start to understand, well, what does the system review me aim to achieve? What kind of research questions can you answer with it? And where do you, where does that leave you? So what I immediately think of when I want to look at a system analysis is what kind of questions are actually good to answer? So and the way I start with that is I say, well, when we do a cys meta analysis, the process of achieving that kind of gives away where it has its most utility. So if I do a study where by the process of it, I'm going to collect all the relevant literature in a, in a, in an area, I'm going to then accumulate all the things that they found and synthesize a study that puts together all that information. Essentially the best kind of research questions are gonna be those where there's been an attempt to solve this. People have tried to figure out where they wanna go with answering that question. But potentially individual studies are not able to provide all of the correct answers or maybe individual an uh studies are conflicting. So as an example, you may find that there's a particular drug where there are multiple conflicting results. And actually, it's not very clear what, what's the best way to proceed with such a disease or such a such an an outcome. In that case, there is probably a lot of scope through me analysis because we can then combine individual trial data and we can put that all together and say on aggregate with all the patients included in all the studies. This is the overall result that we find and with that, we can then make a more conclusive result which may help to answer that question, which each individual study has tried to identify what the best drug is, but it's not really succeeded very well. So that's where I think there's benefit in terms of what we try and achieve and how we, we do that. Um Right. Moving on. Um This is, I hope someone will get the fallout joke. This is fall out in Vegas. Um I cannot hear, I knew what happened. Uh My, I'll just, I'll just turn on my audio for Zoom as well. So you can just directly hear me talking from like here from this end and then I don't know, you tell me fine. OK. Um Well, technically she's aside, we are live again. So yeah. Um this is a follow up joke. I hope someone will get this. But if you don't, no problem. Um Basically what this tells you is this idea of the he hegelian dialectics is an old philosophy joke. But essentially it is the same thing in the same way that Caesar from Fallen U Vegas combines himself and the NCR, you're trying to combine very opposite or conflicting results, you put them together and your synthesis will be something that is novel. It's something that you wouldn't have expected from looking at individual studies, but it gives you a new perspective and a clearer answer to your question. And it's fundamentally a combined result that you wouldn't have individually from looking at studies on their own. Um So yeah, now the practicality of ideation is something that's not very clear. I think this is something that needs a lot of work. Um Because paradoxically you, you find yourself in a, in a setting where you don't really have an idea of what you're looking for. Because by definition, right, you're looking into an area where there are some studies, people have tried to analyze this question individually. So maybe there's a, a four or five or seven or whatever number of studies there are. But the challenge is all of these studies give you different results or maybe they don't agree or maybe they've got different variables they're looking at, there will be things that they are able to do or, you know, unable to do individually and you then have to kind of come in and think of, well, what would be helpful if I put all this stuff together, where would I see benefit if I was to combine all of these past studies, which you will initially have the challenge of actually going and finding because they might be in different places, they might be on different, different types of databases, some might not even be fully published. You might be looking at like a preprint or something. Um You have to really go out of your way to identify all of these different pieces and identify what you want to do with them. And that's not an easy thing to do. There are no obvious answers. So this is an area where I think there's not much practical help. Um II have been looking into literature around this for a while in ID I NMR, but also just for my own previous study and a lot of existing literature basically tells you to use stuff like po or finer or any other mechanism by which to, you know, structure a research question. But that in itself won't necessarily help you to identify whether your research question is any good because there are lots of studies that you could put into those frameworks but may not be very conducive to a good research outcome. So I will go into how to do like using po and finer and all the other stuff in a bit. But before that, I want to just briefly cover what you should look for and how you do that. So the I kind of put this in the, in the last um the last bullet point I said, what can be very beneficial is to think about how your research, your results should look. So that's kind of your hypothesis of what you're expecting. And then from there, you can go into what you actually see within the literature because those are not necessarily gonna be the same thing. Um You may hypothesize that drug A is better than drug B, but you might find the literature actually tells you something completely different and that in itself that should set off some alarm bells because that's not what you're thinking you should see. And then the question is why. So naturally you have to do the first step of any, any good analysis of where you have a question is a literature review. Um A as you can kind of point, it's, it's this inverted triangle. You go, you start broad and then once you find something where you think there's, there's value, you go further and further down. So like we've said here, you wanna look at what the topic is, why it's important, what kind of research has already been done? Um Specific area you're interested in. People make this a very personal process of if I'm interested in stroke medicine. I will only do a study on stroke medicine. I think that's, there's nothing wrong with that. But I would like to point out that in general there is a very broad um remit of what you can do with systematic reme analysis. I think the skills are very interchangeable. So, you know, if I wanted, I'm, my background for context is cardiovascular science. That's why I'm doing my phd in, I'm specializing in, in heart failure. And you know, it's looking at modalities of risk factor analysis. But if I was to be offered, someone wants to do a plastic surgery project, the skills are identical, it doesn't change anything. Um What would be more challenging for someone like me, however, is, I don't know that much about plastic surgery. So this literature would probably take longer because I'd have to help identify what's important, what li what literature exists because I wouldn't know that. But the process is fundamentally identical. So one thing that's also worth thinking about is current ideas and current kind of research questions that's very important. Um When I look at a study, I always tell the groups in the mentorship scheme to think of it in terms of like a hook. So once you've done one research study, um for example, you've done a study on a, a certain drug, there will be within that paper hooks or things that you can attach yourself onto which naturally lend themselves to new questions. So for example, if you're looking at a drug, you might find that actually people who are elderly, uh you know, get a different benefit to that drug compared to people who are younger. And then you might, you might attach that question to, is that due to how the drug is metabolized, is that due to other comorbid factors that are associated with that disease and that drug, you know, there may be other things going on that you didn't initially think about. And once you go and do that additional analysis, you will help to identify other questions and enhance your current understanding of that knowledge. So by doing this process, by actively reading the literature and identifying what it's what the ideas are, what the key results are and where that literature is guiding you, you will start to pick up on questions that are not completely answered or maybe the the answers are not what you're expecting or they're not what other literatures show you in the past. And those recurring questions not coming up adequately should set off alarm bells. There should be something I as a researcher, you'll, you, it's, it's like a muscle, you'll develop this habit of saying hang on. That doesn't sound right. What's going on here. And the more you do it, the more literature you read, you will become more confident in identifying something where you might find a spark or, or something interesting to listen to um this is something II teach every year. I think it's kind of vital. Um If you're gonna think about where to go and what's useful to cover within your study. I think if you're gonna hypothesize what impact drugs or anything will have, then you need to understand cause and effect and you need to be able to map out where cause and effect will link with your study. So this is something called a dag uh a DAG directed acrylic graph. It's used very commonly in epidemiology, but I think it can be used anywhere cause and effect doesn't change regardless of what field you're in. So please feel free to use this in anything and everything that you feel is appropriate. But all this really does is it shows you a chain of causality. So in this example, what we've done here is we're looking at the association between screen time and obesity. So model A is the most simple model. It doesn't really tell you much. All it tells you is that we think that screen time is gonna cause more obesity, but it's not that simple, right? Because screen time and obesity don't have a direct link. You know, if I, if someone was to just sit there on their computer all day, that in itself isn't going to trigger adipose tissue to multiply or you know, to kind of it, it's not going to directly lead to obesity because that doesn't make sense there is no biological mechanism or any way in which that would be possible. So that's where you, as a researcher think to yourself, what else is going on here? What would be potentially a mechanism by which this would work? That's where model B comes in. Uh Model B. What we're suggesting is if you have more screen time, that will cause you to have a varied level of physical activity, which could then lead to obesity. And those relationships are are important because in this case, physical activity is the mechanism by which this happens. So it's a mediator. It is the reason for which screen time will influence obesity. If you have a lot of screen time, it's likelier that you will not have as much physical activity. And therefore your risk of obesity will go up and obviously vice versa is also true. If you have very little screen time, you potentially have more time to be active and you therefore would have reduced risk of obesity. Um So that model works both ways and that's why you can be confident that physical activity will mediate this result because it goes both ways and you can see a clear mech mechanism. Um model C is we're adding something new here. What we've suggested is, well, let's let's think about where education might have a role. So education is your parental education as a child is linked with obesity, but again, it doesn't directly cause obesity. It can potentially cause it as, as an offshoot. Um And what we're proposing here is that, that happens through screen time because if your parents are less educated, they, they will, you know, they might not push you to do nonscreen based things, they might not push you to be active or not push you to be in school as much things like that. So we can then say, well, that's gonna lead to that, But the relationship is not causative necessarily. And, and you can picture that by going the other way round. If a kid has more screen time, it doesn't mean necessarily that their parent is less educated, that relationship doesn't always go the other way and it doesn't make sense. So it's where you've got to be very cautious of where, where, what does that relationship look like? And that's why you'd wanna study this because if you looked at this variable in your analysis, you could then identify what exactly the relationship looks like. Whether there's a AAA kind of a drug response, it's not really a drug but AAA response between how much education parents have and how much impact that has. There, there's things that can be done there. And the final one, what we've added here, end outcomes and kind of causative uh so consequential outcomes. So what we're saying here is that, well, both screen time and obesity are linked with more self harm, but they're both linked in different ways So obviously, screen time is gonna lead to obesity anyway, because that's the central causal chain, but also they both feed into causing obesity through different mechanisms. Um Screen time potentially through what would you you think of in terms of screen time being linked with low self esteem and you know, kind of comparing yourself to other people on social media and so forth. But then obesity does the same thing we're also thinking of, you know, obesity, you might be, you might be, you may be a victim of bullying. You may be low self esteem. Again, it's a similar thing but achieved through two different ways. So those associations can also be quite interesting to look at because all three of them are on the same causal pathway. So the more you do this and the more you can think of kind of direct associations, this will guide you in terms of what to include in your study, what variables are important and what things that you should really be including to be confident that your study is on the right lines. Otherwise you're gonna get a little bit lost. Um Just a little bit. I wanted to kind of cover PCO anyway. Uh II did say that we do we back to it and also something I like a little bit, it's an offshoot of PO but it's, it's a little bit more uh in depth p it, it's, it has, has its utility kind of a gene example we put in there. So people you compare gabapentin with placebo and your outcome is does it improve or pain or in this case, do you decrease pain syndromes? But same thing po has a very clear perspective because you see you're doing intervention and comparator. But as you can imagine, not all studies are gonna do that, particularly not studies that are technique uh kind of non you, they, they might not have two definitive outcomes. And this really lends itself quite nicely to trials or cohort studies something where there is a very obvious split between what's going on in one group versus the other and that's not normally something we do. Um So yeah, the other side of it is spider spider is, is, is an off it, it was developed to add on to PICO. Um Well, is obviously for comparative studies. Spider can do a lot more which is why it's more useful. Um prespecify what type of study you're gonna have. So you've got uh so P obviously is for population, which is the same thing as S for sample P I is phenomenal of interest, which is essentially what you're going to measure. That's not even on po which is quite useful. Um I guess you could say that it's combined within I NC obviously, those are your intervention you're comparable, but that just tells you what you're gonna meas what you're going to measure in a definitive uh two arm study. Spider doesn't have the limitation, it could be anything. Um So this is this example, like off the internet, we're looking at of young patients who are going to antenatal education. You couldn't do that with po because there's no comparative or no no intervention. So this uh and also this study by default would be qualitative which is new. You, you couldn't do that with po again because you have to do armed studies in which means only or maybe cohort studies. Um So Spider is much more versatile. Um One thing I really like about it is it accounts for essentially any type of evaluation. So here we said experiences but that's very vague, right? Experiences. You can measure the survey, you could interview people, you could do you know all manner of things that would give you that qualitative data, you could potentially get them to fill out like forms and then grade stuff out of 10 and then obviously do an analysis of, well, they said it was six out of 10 before the class. Now it's eight out of 10. That's great that tells you there's a a marked improvement of of 20% and that's quite useful for qualitative data, sorry, quantitative data to add on to qualitative data, those are called mixed method studies. Um So I think Spider is probably the more useful way of doing it. But my strong recommendation is think of your research question first and go through your bags, make sure that you've covered all this stuff in some depth. And you've thought through the and then put it into spider because once you've put it into spider, it will help you to cross verify that everything's covered. And your, your question is appropriate, which is why this, this today we're calling it refining a research question because what I'm trying to teach you today particularly is that there are these multiple steps of what you can do before that your study is gonna be well thought through, there's gonna be definitely be enough literature out there and there's gonna be a relevant question that's worth answering. Um So process of it, but I will always, I will leave you with a few kind of I, I'll, I'll give you an example of what, what I did. And I'd also like to kind of go through a little bit more practical tips and tricks and things just to show you where you can avoid some pitfalls. So, um if we, so for example, if we go to this dag, we've started to come up with a question. So we're looking at the, in uh what does screen time have to do with obesity? We have identified that physical activity is probably linked in um we, because it's gonna be the causative mechanism we've identified that we think that parental education being low or high may influence both screen time and therefore obesity. And we think that when both screen time and obesity are high, that should also prompt more an increase in self harm risk. So what that accumulation will mean is that we wanna study all of these variables, but we wanna study what happens when things change. So what I would recommend is you do a systematic review and analyzing how screen time ties into obesity. Um So the research question, your primary one should be quite simple. You should be what is the association between screen time and risk of obesity in Children? Because that's targeting level B, we're gonna do level B first, the kind of initial causative chain cos if that chain doesn't work, model C and D fall apart, there's no point to study them. So we're gonna cover model D first and then we can do additional studies looking at uh have studies looked at parental education. Are there studies that have compared self harm rates? Are there studies that have described in, in, in what level they're physical activity and any changes in that? And those would be very good secondary questions to add on to a systematic review that helps to put all of this together cos by the end of it, then you'll have a systematic review that's looked at whether screen time changes your risk of obesity, whether physical activity is linked in that causal chain. And what evidence there is for that in all the literature and potentially, then additional analysis will have covered whether studies have looked at parental education and self harm being linked in there as well. Um I've put kind of in bracket subject data ability purely because data availability decides what you can analyze. It's part it's covered within the remit of your literature review. As you find more studies, you'll be able to see more and more of them have covered the same things or maybe they've, some of them have covered some variables, some of them haven't if the data is too thin or not measured in the same way between studies that can obviously cause you difficulty. But these are things to think of whilst you do your da uh data analysis and to some extent, you can preempt them by good literature review because if you've seen enough of the literature, you'll start to pick up if there's any common metrics by which things are measured and whatnot. But sometimes until you get all the papers, you, it's not that apparently for some people um this is stuck on spider and PICO steel. Um um that doesn't work. Which slide are you on? I'm on putting it all together cause the zoom, the zoom is on the same one as well. OK. Let me just um let me change to the next slide anyway. And then you guys tell me, OK, perfect. Thank you. Let me give you the paper. Now, um I can uh can people let us know if you can see the news light, please. Um Yeah, um I'll start talking about it in a second if you're happy. All good. Um This the slide is OK for me now. So um II think sorry guys, can you please try refreshing? Um because uh for the both of us, we can both see the new slide currently. OK? Like if, if, if it's working, I'm gonna just crack on, but yeah, if someone's struggling, hopefully it will be fine in the recording. Um Anyway, just to proceed back on the topic. Um So this review, we, I was working on last year uh published in January, so fairly recent, but just something that came up when I was doing the slides. Um This is uh I think quite a good example because it, it's fairly simple, but there's a reason why we chose this topic. Um not just because I'm in cardiac surgery, but anyway, um so this is looking at basically how methods of harvesting the internal thoracic surgery for coronary artery bypass grafting. Um So the two main methods we use is skeletonisation versus pedication. Um Essentially, we just wanna see I, is there any difference when we do this uh between what outcomes we expect? In terms of, we're looking at mainly we looked for follow up mortality, graft, failure, re uh revascularization, operative issues, stroke M I the usual things you can look for in cardiac surgery. Um I've put a diagram here of what that means because if you're not cardiac surgery oriented skeletonisation, literally, you, you take off every bit of fascia and you purely just extract the artery pedication. You leave on like fascia, external uh veins, nervous bundles. You don't take, you just leave that and take the whole thing with you and then you can, when you graft, you put everything on. Um So there are reasons to do both and II think this is where the, the concept of why we do the study makes a lot more sense. So in, in our hypothesis, uh when, when writing up the study, but also just from clinical experience, there are reasons why both the good has benefits because you can get longer grafts out because you're gonna cut out everything. So you get a really good pure artery. Um It's more, it's been demonstrated at less risk of sternal wounds because I guess you're transplanting less tissue. And on top of that, it, it's, it's just more AAA cleaner quote un operation. Um But also there's reasons for it to be worse. Standardization is more challenging to do. So people have said inexperience uh or you know, operating like a learning curve or impact outcomes, you're more likely to damage structures when you're cutting out more stuff and you're also more likely to have graft patency failure. So naturally, there's a hype, there's a kind of a hypothetical reason that both might be good and equally, then you think to yourself, how do they compare against each other. We didn't know looked into literature and help current liter, current results are mixed. Some studies say they're better. Some say they're worse first. That's massively controversial because imagine you wanna tell your patient uh it could be the same, it could be different. I don't know. And that doesn't, you know, that that's a terrible idea. Don't do that. So logically, the reason to conduct a systemic meta analysis is to resolve that controversy and help identify whether there is a picture that can be found by analyzing all the studies. So in this review, we looked at 28 studies in total uh with nearly 13,000 patients, but all in all, you won't find all of them have the same stuff. So this is uh mortality e eight studies are 28 reported that which is about 3030 32%. So it's not very commonly reported and now ignore the bottom, the the effects model. Just look at the, the just look at the I RRS for all of these studies, right? So you've got some of them that are saying pedicled is better, some are saying sat is better, some of them have these enormous um 95% confidence intervals where anything could be true between that value and as a surgeon, this is a mess because you don't know what to expect. Um You can't be confident of one way or the other. That mortality is better or worse in either way. Um Sadly, our meta analysis didn't give us a clear idea either because the I RR is 1.14 and then the confidence interval is huge, 0.59 to 2.2 which could mean either way we will go into how to analyze this stuff in more depth later. I'm not gonna bore you with that anyway right now. But we are still what, what this shows me is, there's no difference. They're probably about the same because that interval, that confidence were massive. So it could be, that one is better. It could be the other is better. In that case, we just say, well, they're about the same or statistically, you know, it, it, it's impossible to differentiate the two at the moment. There's no statistical difference. So, improvement on previous literature, at least it's yes, I say no, it's, yeah, they're probably the same which for a surgeon could be relieving because you might say, well, ok, if they're no different, I'm gonna do what I'm more confident in because I've done this 1000 times. I'm pro you know, it's not worse and I'm good at this. So I'm not gonna make a, a mistake if I force myself to do something I'm not good at. So that's a good result. S same again here, right? You have five out of 28 studies reported this, which is a tiny percentage, but this is common, not every study will compare every single thing. But again, you have some favoring pedication, some studies that the world are huge, they have no way to tell. Our study showed that penalization is better. You know, if, if you look at the, the I RR on the right for random effects model, we we, we are pretty confident that that's better. So that, that's the result because what we've identified is that compared to having studies that are a little bit all over the place, we can definitively say that yeah, for graft failure pedication is better. It has it's less likely to happen by quite a lot. 1.87 means the ratio was 87% which is a really good result and you know, almost, almost double, so really, really strong. Um And that's, that's evidence that wasn't there before. So that's why you should do meta analysis because it shows you an outcome for all of these patients combined that we didn't know from individually. Um So yeah, that's a very brief overview of what a what a man, what a meta analysis can achieve. Why you should think about doing one and real, a kind of uh an example based quest uh question of what, what works and why you should study it. But sometimes your, your idea sounded really clever, but it doesn't actually work anymore. And there are three main scenarios that you will come across. We will cover this extensively. Um Almost everyone in mentorship program will face some one of these at some point, but we'll get through that together. And um throughout the, the course of the next nine lectures, you will learn every single bit of these anyway. So I'm not gonna go into too much depth, but I will kind of give you a perspective of one of our previous papers and what we can do to make sure that we're standing out because that's, that's fundamentally the crux of this. If you think of all of these questions, your paper is either, you know, if it's been done before, that tells me that the literature is a little bit saturated or unclear. If it's not got enough literature yet, then the opposite, your topic is probably too new or just too niche. And if your topic is not relevant or unimportant, that is probably just a, an indication of the field you're in because that literature is still being developed. Maybe people are still identifying where there is more scope or, you know, maybe again, you've gone into a too niche of an area. So in, in situations like that, um I this is something that happened to us. Um, Adele, you will appreciate that. I put up one of our papers. Um Yeah, so essentially we already knew that there, there, there's been studies on this stuff before. Um, diabetes me mellitus and Parkinson's is not completely a novel association. People have tried to study this before. And a couple of years ago, there was already a, a system on this. But then you have to think, well, how do we make ourselves different? Sure that our question is actually still adding to literature. And this is where those hooks come in. You start to think of, well, what's a hook that will help you generate new information that will be valuable? I've kind of highlighted some of the new stuff that we did I in the big circles. So first of all, our study focused on type two diabetes, which is novel. Um Secondly, we added more studies and we had about 15 million more patients. So ours is 32 million, this is 17 million. So we, we've added a heap of new data which we can use to identify patterns and trends that's very valuable because a clinician can then point to us being the most significant kind of um literature on the area. And the new things that we've also done is we have stratified patients by age sex, um exposure duration, we've also added on a bit more data for essentially looking at the associations between how much um the progression of Parkinson's was affected by diabetes. Again, I've highlighted all that stuff here. Um So that is a, again, more to do with kind of the long term trend of diabetes goes in, in. That's quite new. No one's really looked at that before. And if you're a doctor who's gonna be treating patients who have diabetes and are quite elderly at risk of diabetes. Um, you know, it will be uh good to identify why this is the case and potentially identifying where you can contribute to that literature slash where you want, you wanna guide, telling your patients. So those kind of things are very advantageous. And what we've done here is essentially, we, we knew what the literature looked like, but because we could structure it and we could do good literature review practice and we could kind of steer ourselves into a bit more of a novel area. We've added a lot of value by constructing a paper that is inherently more useful or to this particular context. And we've added on to what previsit has done by no means. Does this invalidate what that previous study did? I think that uh you know, a doctor would still find a lot of benefit from that, but we can be confident that what we've done has a specific niche that it targets and that's very valuable. Um So again, we will cover all this in, in great depth down the line. But today is just a kind of a rough overview of things to think about where you might go with your study, how you might be confident that you actually can't stumble upon something that's useful to do. And um yeah, thank you very much for listening. I will take any and all questions and um Yeah, thank you for coming. Hang on a second. I wanna see if there's any questions. Um Yeah, there are a few questions. Yeah, I'm just seeing. Ok. Uh, first question, other than sri of the other research questions, there are a number of things you can do. Um, there's quite a few things so you could do. Um, it, it depends on what you're trying to achieve. Um, I'll talk about some clinical ones first and then I'll go on to others. So let's say you wanna study a drug, that's quite an interventional thing. You're gonna give something to um you know, maybe 100 patients and see how it goes. That would require you through a clinical trial. That's a AAA very specific type of research study. Um If you want to do something that is interventional, then you will do uh sorry, it's not interventional and you're just gonna monitor patients. You would then give them something along the lines of, you could do a cohort study, you can do a cross sectional study. These are all observational. Um You can also do the kind of epidemiological studies. So those essentially will look at huge amounts of data. You can be like a million people and you're just gonna follow how many people get diabetes in the population. For example, it, you, you, you wouldn't be able to tell much about that particular population, but you'll know how many of them have it, how it's going and that kind of thing. So those are kind of your basic, um, you very basic, I guess studies. Um, you can obviously do lab studies, the, the and, you know, things like that for, if you're more scientifically oriented, basic science, I should say, um, if you just want to put out like your own opinions on things you can obviously write to the editors if you'd like to do. I think that's pretty much it, to be honest. Um, at this stage, I, we'll, we'll call it that, but there are other things you can do. Um How did you get the idea to get the link between type two diabetes and Parkinson's, that's already been there for a while. Um, there have been studies looking at this for at least 78 years now. Um But yeah, it was just an interest in the group in that mentorship scheme. Someone wanted to study it. And we just thought, well, you know, you've identified a paper that's relevant and then we just went down that literature review, just keep going down the pyramid, which is why I said you start really broad and then you go as more and more specific, as long as something makes sense to do in terms of the ideas sound, it's very easy to then just ideate it more and more and more and get, find the relevant papers, find the hooks to your question. It all ties in quite nicely. But the initial process as you're right. It's, it's something where you've just got to keep reading literature, finding something that's worth doing. That's a personal interest is because I think that motivates people to actually go and study a particular area more. Uh mm What else? How many database should be used with finding articles on? There's no golden number on this? Um I think the question is just how broad do you wanna be. Um So, you know, it depends on where you're going. So, for example, I do a lot of global health work. We would look at, you know, wh o in, in the cosmetic, we'd look at lilac, we'd look at a bunch of, we look at c which is mostly like Brazil and kind of South American papers, you know, so there you can do very niche uh database as well, but your basic ones, obviously MEDLINE and base um those kind of ones. Those are your very well standard ones. So I'd say cover like three or four good ones, but then if you're gonna go for something more niche, then pick out something extra. Um Right. Do you believe it would be good to read the, do you do the Cochrane handbook was before? I mean, you could, the Cochrane handbook is like 100 pages probably more. So I think I wouldn't read the whole thing just as like a book. I'd probably refer to it as and when you need it otherwise it's gonna take forever because, and also I think the reason we've designed NMA like this is that there's teaching and then there's immediate application Cochran handbook is, it's like a textbook, almost. It, it, it is very in depth. So do you, do you want everything to II, I'm not sure that that's necessarily the best use of your time, but I mean, it's very informative and, and there are some journals that will expect you to follow that, that kind of style. So there's no harm in being well versed at all. Is there a program or the same? Yeah, I mean, we, we, we, we do this every year. Um You can, you're more than welcome to join. Um Right. Yeah. Um Are there any more questions? Anything else you'd like me to go through? I'm more than happy to, to sit and go do do that? Um I understand some people had issues with their um how on the program you, you apply? Um We had an application w which ended yesterday. Um But we'll, if we have like a lot of requests to reopen it, we could keep it open for a few more days potentially. Uh But um nash, there is another question. Um Yeah, there's a couple more questions now. Sorry. Um I've just seen these questions. Yeah. How did you get into research yourself? I was in high school. I basically, I had uh we, we had an internship in high school. Otherwise they didn't let you come back for your 12. Um So I emailed about 100 different academics i in the nearby area. And eventually one of them was willing to, to work with me. They taught me how to do uh the dags and all this, all, all this stuff about conceptualizing the study. But then they were, I was fortunate enough to then work with them to find that first study complete it all the way through. We published a few years ago. And then since then, because I knew all the skills, I started to become more and more independent. So coming up with my own studies, um started working with more people like my friends and colleagues at, at uh in university and then slowly but surely just developed N MRA because I thought let's go, let's go our own way. Let's go independently. We, we can just, we can do this else. So it, it, it was a tough way to fi figure out things. But um I'm very happy that I did it my own way. So, yeah, and that's what we wanna teach you guys as well. I don't necessarily want you guys to be stuck doing the same research again and again, or, you know, not feeling like you're able to break through into research, not learning the right skills, that stuff's very important and that's why I exists. Um How much time should you want to spend in literature? You there's no such thing. Um, there, there, I couldn't tell you it was like, oh, yeah, spend 43 hours. Exactly. The, the, there's no definitive time period, I'd say, do your dags know the literature in and out and don't overcomplicate it because you're right. You know, sometimes it can feel like there's way too much stuff. I'd say if the literature is not precisely like there ii, if the liter is not. Exactly right. Um And like, I guess what I'm trying to say is if in your dag, you're going off on random tangents, you're adding a bunch of stuff that will get confusing. So keep things simple because you can always go and add extra topics and things later. You can, you know, if you've got a good primary question, it is easier to add secondary questions later than it is to have too much stuff and then cut down loads later because I think starting a project is what slows it down the most that initial momentum of getting a good question going, starting doing the work. If you fall flat on that stage, you'll probably never get the momentum back to actually get everyone motivated and keep working. So it's important to keep things fresh and to make sure that you're progressing fairly steadily at the start. So, yeah, I'm not saying less is more, but I'm saying there is a, a healthy balance to be obtained. Um Is there a website or any platform where you can get ideas for a review. Um Not particularly you just read the, read the literature, find something you want to study yourself. Um dag oh Dags, there were my slides, I'll show you again in a second. Um Yeah, one second. Um So, so these are tags, this stuff. So hang on. Can you see my screen? Yeah, these diagrams that talk about cause and effect. These are the dags. So those obviously help you conceptualize where different variables are, they can conceptualize cause and effect between things. And I think once you've done this to a a as appropriate level of depth, this should guide you on what level of what variables you want to include in your study and it should guide you as to what is important to analyze within your systematic review. Um I hope this makes sense. Um I'm happy to answer any more questions. I I'm aware that there's a lot going on on the screen. Um Can you receive phd as a, as a doctor? Um It depends on what you wanna do. So I'm in the UK. Um There are specialized programs here where you can take time off um off of being a full time doctor and then you can do maybe a part time phd alongside or you can just stop working as a doctor altogether and do your phd for academic purposes. Those in, in, in that way. Yes, you can obtain funding to do that. Um If you're in a, uh if you're in a different country, um such as like outside of the UK, it will depend on country structures, things, but it can be done. There are ways to, ways to put your academic career ahead of your doctoral career for a bit. Um And then obviously you can transition back or you can juggle both phd and you've got more research experience and you're already a doctor. I think you have a lot of options open because clinical trial is easier to obtain. And if you have an a a grounding in both, um and obviously, if you can get a little of funding, you'll probably be fast tracked up towards promotions and being more accepted in the in the academic world. So it can be done. But the initial first step is always the hardest. I'm sorry if I can add just because um people are here. Uh Yes, we have, we have an entire series uh that is going to target each aspect of uh systematic reviews and meta. But also um I the there is do not send a mean um or patronizing comments in this group chat. Um This is an entirely free opportunity for you to learn from Niraj. Niraj has an incredible amount of experience. He's a uh amazing person and researcher with an, with a incredible background. Um We are not here uh to be criticized freely uh after we have spent this entire time creating a resource like this. So further mm comments that have been sent today will not be accepted. Going ahead. So I just wanted to share this. Um But um yes, we did have some problems with feedback forms, et cetera. You don't have to fill them in as they come, you can fill them in afterwards. That's completely fine. Uh Do you now have any more questions for us? Um OK, then everyone. Thank you very much for coming toda today and we look forward to see you later. Uh Please do check our social media and do get in contact with us if you're interested in in the mentorship scheme. If you receive a good amount of interest, we uh might reopen it um for a few more days. So do get in touch with us if you have any questions as well. Um So yes, again, we look forward to continuing. This is going to be a great series. Ok. Thank you very much guys.