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Just gonna mute. Great. Um Hello everyone. And you're very welcome to today's webinar um where we're going to be discussing how to prepare an abstract for the upcoming Jobs conference and for other conferences that you may be hoping to submit research to. Um We're gonna be talking about a couple of different topics today and we're gonna be looking at tips to pro provide a good abstract um specific information on preparing specific types of abstracts and there's gonna be AQ and a session at the end. Um I'd just like to introduce our two speakers. Um We have Professor Amy Higgins who is a consultant obstetrician gynecologist in the Coombe Hospital um and works as a professor for UC D and also Dr Khadija is who is a consultant obstetrician gynecologist in University Maternity Hospital, Limerick. Um Both of our presenters have a wealth of research experience and publications and we're really looking forward to learning from them and getting a chance to absorb some of their knowledge. Um So, in terms of the program, um Pro Higgins will start off talking about um some general tips and talking through specific types of abstracts and Dr Khadija will touch a little bit on basic approach to numbers and statistics when submitting research. Um If you have any questions at any point during the webinar, you can just type them into the chat. There'll be AQ and a session at the end. And if possible, I'll call on people to speak and to turn on their microphones. Just the only other things to mention. Uh Jo's date is the 29th of November 2024. So that's a Friday and of course, abstract submission will be closing in September. Um The closing date is the 25th of September, which is a Wednesday at 12 o'clock. So hopefully, this webinar will be really useful for people. Uh And you'll get all your abstracts prepared and submitted on time. Um So I'm going to hand over now to Professor O Higgins and really look forward to what you have to say. Great. Thank you very much. So, her and thanks everyone for giving up your evening to join today. Um I think we wanna keep this as interactive as possible because there's no point in you guys just sitting there listening to us kind of sprouting on about you should do this and you should do that. We really need to get some feedback from you as to what you need, what guidance you've had and, and where that might um be improved. And just to give you a little bit of background to this, the, the idea for this webinar. Um It really came from the drug submissions that we had had in previous years and that there had seemed to be a significant decline in the quality of the abstracts being submitted. And whether it's a perception that, you know, you can just throw any little bit of research into jobs and it'll be fine and you can put a line on your CV or whether it was that people weren't getting enough guidance or support it. It's hard to say, but really, really uh um commend and applaud the Jobs Committee for taking this seriously and saying, look, no, this is a real scientific um society with a real scientific conference and there needs to be minimum standards. The whole goal of drugs in the first place is to support and promote good science and opportunities to engage in clinical science among trainees in Ong and, and students, um medical students in Ireland. And to give that a platform to give you guys a platform for showcasing research and projects and so on that uh uh that you've done and it's a brilliant, like, people love going to the Jobs Conference. It's a highlight of our year to get together, to build our community, to talk. And I think uh part of submitting your abstract to the conference and part of being part of the conference is building in and feeling part of this Obstetric and Gynecology Society that we have in Ireland. It's a relatively small community. Hopefully you guys will have fulfilling careers and stay engaged with Irish obstetrics and gynecology throughout your lives. And so we need each other, we need to support each other. Um And part of that starts with helping and nurturing kind of scientific development. And so I must say when I saw the quality of some of the work that was being submitted previously, it was really disappointing, not from the point of view of the trainees, but more just a little bit saddening actually to think, well, why are trainees not getting maybe the time or the support or the guidance that you need to produce good quality abstracts? And that's really what this evening is about is not to kind of preach to you and say this is what you should do, this is what you shouldn't do but really more to engage and just say, where, how do we make this better? How do we give people more support? How do we lead and everyone has such such um busy clinical schedule, so many competing demands between training and log books and looking after your patients and exams and personal lives. And it's, it's, it is a very, very, very busy time moving house all the time trying to sort out new wifi connections and you bin pick ups every 18, like 12 or months or 24 months or whatever it is. It's, it, it's really tough. Um But somewhere in all that there needs to be some recognition that there's value in what you're doing both in terms of clinical skills that you're gaining and how that benefits. Not only your patients, now put your patients in the future that you will become consultants very soon and how you can use all this training that you have really for the benefit of your patients because that's how most people go into medicine in the first place because they care and they want to make a difference and they don't choose a 9 to 5 job, they choose night shifts and weekends and everything. Not because they're fun, but because they're difficult and because it's worth it. And part of that then I suppose is having that same quality and that same caring that you would have for your patients, for the science around it. Because unless you can make well informed decisions, unless you can produce good evidence from all the work, clinical work that you're doing, um you can't really advance medicine and even for your own training and your own careers, the more um attention you pay, I suppose just the same way as it's so important to be a careful doctor. It's also important to be a careful clinician scientist, researcher and so care and attention to detail really, I suppose is gonna be the take home message of tonight. And I really would love to hear back from you guys. Why that may not be so easy for you and what can we do about it and how we can address it because it's important on so many levels. It's important for your own career. It's important from a clinical point of view. It's important really for the future of obstetrics and gynecology in Ireland that we're training people well, that we're supporting people well, and we have that we will, you know, over the next few decades, have a wealth of expertise and have a real international community that we can be at the center of. We've amazing maternity hospitals, the biggest in Europe, we have so much industry and university collaborations and so many things that are going on out there. Um But you need good people to be part of that. So hopefully this is the first steps in getting you guys there. So I want to just go through some very, very basic stuff because really, it's the basics that are, are the really the scaffold for all of this. And as long as you have a good basic approach, your abstract will be adequate and of good quality. The details of it don't matter so much, particularly for something like jobs where the idea is inclusivity and um instead of, you know, life changing or practice changing research. So even if it's a small study and there was a negative finding, it's still important if you've done it well, if you've done it carefully and that's really what we're looking to see in the abstracts. Has this person got a logical scientific methodology? Is there a clear aim to find something out? Has, is there a valid conclusion and is that presented carefully and it really doesn't matter whether the findings are positive or negative or whether they're groundbreaking or not, as long as they make sense and they're founded on good science then that's what we want to see. So simple tips um that I just want you to have in mind when you're writing your abstracts, this is science and it's, you need to approach it. You guys have all done a ton of science. You've been through secondary school, you've been in labs, you may have done degrees before medicine and you've done medicine, like there's a huge amount of science there and you need to tune back into that. I was reflecting on some of the abstracts I read last year and I was just thinking that there was a disconnect between what people are producing now as clinicians when they're busy and overwhelmed with clinical work compared to what you could have asked maybe a medical student or even a secondary school, science student when your brain is in scientific mode and you're asked to do scientific writing. So this is scientific writing and it needs to be presented as such. So there needs to be a logical approach. The aim needs to be set out and the methodology needs to relate to the aim we have seen before that, there's one aim and the methodology doesn't address the aim at all. So that's what we're trying to, I suppose, encourage today just a bit of logic and put your brain into science mode and take it from there that includes using scientific language throughout, not lay language, using clear definitions and medical terminology being precise. So that means giving numbers rather than saying a group of patients were recruited from, you need to say 23 patients were recruited, there needs to be a precision in it and also a specificity which means a definition rather than just saying low risk antenatal patients were recruited. You need to say antenatal patients who did not have a medical comorbidity, a fetal growth restriction who were between the ages of this and this and be be specific with it and have a logical, a logical methodology. I can't emphasize it is so easy to say and so hard to do in practice your main um sections in your abstract will probably be depending on the type of study. Something along the lines of aimes method, results, conclusion or AES methods, result discussion. So you start with the aim, the methods need to relate to the aim, the results need to relate to the methods and the conclusion needs to relate to the findings or to the results. Um So I'm just gonna go through a few types of studies and Doctor Ishmael will do some more and then um if there's other things that we haven't covered, just please feel free to put it in, in the questions and we'll try and go through it then. So in terms of audit, I know you all do compulsory audit training through the college. It's, it's a formula, an audit has a special specific meaning and therefore, if an abstract comes in and it says we did an audit of this, but it isn't actually an audit, it's not conforming to the, to the steps of audit, then it's not an audit and automatically that reads badly and will prejudice or review or against you. So for an A um and I just want to acknowledge me for these, these are the, and the drugs Committee that these are the standards that they have set out for the presentation. Um So this comes from drugs for drugs and I'm just the vehicle um sharing it with you today. So for an audit, you need to define the problem and then you need to define the standards against which you are auditing. Your current practice, you need to present the findings, you need to make recommendations. An audit is part of an audit cycle. And so it needs to feed back. It doesn't matter if you're gonna be gone to Muar or wherever. And before you finish, you just say the plan would be to re audit in and there needs to be some acknowledgement that the work you're doing sits in a greater context. Um So for example, when you're defining a problem, you need to be specific, you need to, to, to give precise information about the cohort. So for example, compliance with antenatal corticosteroid administration in women at risk of preterm delivery is below the recommended standard. Then you need to define what that standard is and say what you, what you want to do. So you have the standards so you, you can pick nice guidelines or if there's Irish guidelines or it doesn't really matter, there just has to be some internationally accepted standard and you could say that anyone who has preterm labor between 24 and 34 weeks should be given um steroids. And in many of the guidelines, it will give you a number of what the standard is. 90% or 80% or something like this. You need to outline how many patients you saw how many patients you wanted to see and why you picked that number, if it's relevant and then you present the findings and you compare to the standards. So say you have 60% of women in my study receive antenatal corticosteroids according to the guidelines, but the compliance rate is below the recommended. Therefore, we need to take action, our action will be whatever it it is and we will reorder in X amount of time for quality improvement studies. Again, there's a formula um that you study and you just need to, to define the formula and follow it when you go to do more higher level research. And maybe some of you have done these types of studies before. There are international standards for everything. So everything in medical journals, if you're doing a systematic review, there's a formula, you have to follow like Prisma guidelines. If you're doing an RCT, there's a formula and standards that you have to follow, there's more and more and more effort now in the um medical literature to standardize quality in scientific reporting. So if your paper doesn't conform to whatever standards are reporting for say a scoping review or a systematic review or a case control study, then it will just be rejected. And so it's really important even at the earlier stages of training and even for small projects that you recognize the need and the value in conforming to reporting protocols. So for and they're easy, you just Google it and, and you'll find out what you need to say. Um And all of the information particularly to do with scientific quality is usually very readily and freely available online. Um It's deliberately made accessible for, for lots of reasons. So for quality improvement studies, you need to define the problem or why the quality needed to be improved, you need to define objectives, interventions, you need to present findings, demonstrating improvement or demonstrating no improvement that's also valid and discuss the impact of that and have it clearly structured, just simple logic and it doesn't matter if maybe you did a little bit of something and nothing really got better, um, where people just didn't engage. And so the whole thing didn't work, that's still really valid and there's still learning in that and it's just how you present that it's really valid to, to just present the numbers you have. So we didn't see a difference. Um Our, nobody took on our suggested clinical change in practice and we still have the problem. It's still really valid. It just means ok, different approach is needed. And so as long as you have the structure, we want to hear about it. If the structure and the information isn't there, then it's really difficult to say that this is a good scientific approach. So some examples here, I if you think your hospital, for example, has long gy waiting times and your problem is that you want to reduce the waiting times, that's a kind of universal problem in most Irish hospitals. What are you going to do? And so then you need to come up with a statement, you can make this up if there isn't an international thing. For example, waiting times, we wanted to increase waiting times from eight weeks to six weeks and we did ex intervention or we phoned triage people before we had them to clinic or whatever. And then you present the findings during the course of this, our average waiting time for a consultation decreased and if you want to dig down and get more into the nitty gritty of the statistics, we'll cover a little bit of that later. But it's just the basic information has to be there. O otherwise the study isn't a valid study for case reports. May, they're quite a common, um, type of study that's presented to drugs are submitted for drugs and it doesn't matter if it's not that unusual or you think it was unusual and then you search the literature and you see actually lots of people have reported this. It's, it's still ok. Once you, once you report it, well, you need to go back into kind of medical student mode for this, your presenting a case as you would in a final medical exam to an audience who doesn't know the full story. And so the detail is really important. You need everything you would need in a, in a medical um presentation. So you need, for example, the demographic stuff, you need their presenting complaint, you need the medical history and surgical history. And even if they're not non contributory or she didn't have any previous surgery, then you need to know that because often when you're reading a case presentation, if you're presenting it, you can say, oh, well, the only interesting thing here was that we find this really unusual tumor at the time of Cesarean section. But you forget to say there was no previous surgery. But if you and and it, because it wasn't relevant and you, you, you knew it wasn't relevant. But if you're reading that you, you think God, well, what were her risk factors? Um, why is she different? Why did this different thing happen to her that didn't happen to other people? And so the relevant negatives become really important in a case report and they're often skipped and it really diminishes the quality. So you want to know medications, allergies, family history, social history, everything that you would give. If you were a good medical student, then you can talk about the history of the case and then you put it in context in terms and for some um and there's loads of good um resources as I say online. So just Google it like the B MJ has a whole journal on B MJ case reports for example, and they have a formula, a template that you fill in um about your case. And what really then makes it excellent as opposed to just good is broadening your case into a wider clinical context. What are the learning points from this case? Why is it really interesting or why do people want to know it better or why do you want to share it with you? Um So just to summarize the really take home messages, I suppose that I want to get across to everyone is get into science mode in your head. Don't just sit there and write. I did a bit of this and a bit of that. And this is what I found and there you go, you need, there has to be logic in it. This is a scientific meeting. And if you can't see that scientific logic in what you're reading, it's not going to be a good abstract. If the scientific logic is there and it reads like a good piece of science, regardless of the content, then, then it's worth presenting and the ways you can force that to happen or encourage that to happen, as I say is close attention to the flow that the, that the methodology reflects the aims, that the results reflect the methodology. Um And that the lu conclusions reflect the results that things have numbers and values or just rather than saying there was a significant, we find a significant increase, I need to know. Well, I find a 30% increase from this to this and the P value was X and now you say, OK, now I believe them, they, they, they've done that properly. There has to be a logic binder. Um And if this is difficult, if you think, well, I've done a good abstract, I think I've done a good abstract, but I don't know. You, please please ask for support and feedback. The whole point of being a trainee is that you have supervision that's like and I II get that very often, you guys may be made to feel that you don't have as much senior support as you want or that you just are left to do things on your own. Please don't feel like that from a research point of view. The job of the consultants and trainers is to review things with you. Um And if you genuinely have nobody who will review something for you, then that actually needs to be fed back via your trainer. But there are people there that you can ask. Hopefully there are people there in your hospitals that you can ask. Look, this is my abstract. Could you please have a look at it for me? Are there any tips, any advice, anything you want to say? And it's really helpful and even um between yourselves, you can do that. It's obviously ideal to do with someone who has um a good bit of experience because the feedback you're gonna get will hopefully be helpful. But there's no reason you can't just do it with each other. Look, I've read, what do you think of my ab drugs abstract? You're just if you have a quiet moment on call or whatever, do you mind having a look at this and help each other? Because this is adult earning, you are all well capable of figuring this out um yourselves on how to make a good abstract. And I know it takes a little bit of time, but it really makes a big, big difference in the long run. And once you've written one or two good quality abstracts, then it just becomes kind of very easy or very natural to, to get the detail you need. So it's just similar to the way it takes, it's slow and it's work we to learn, let's say a Cesarean section. But there comes a point in your career where your hands are just doing it without thinking. And it's the same with this type of thing. If you can start off early with a rigorous kind of scientific methodology, then as you do more and more, this will just flow, it will become so obvious to you that you needed ap value there and it will be automatic. And hopefully, then that's how we build good science and a healthy scientific community in Ireland. So that's really all I wanted to say for today. Thank you all so much for joining. Like it's really great that everyone has given up their evenings for this and is willing to engage to really, really appreciate it and I'll hand over to Dr Ishmael. Thank you, Amy. Um See, OK. So again, thank you everyone um for being here and um taking the time of your not really uh job time, isn't it? So, um i it's just to, to hopefully, this will be helpful for you uh in, in writing your abstract for our jobs uh meeting and then further to that other meetings um that you will be going to in the future. OK. So I'm just gonna cover about the original research and then I'll cover some basic statistical reporting which would be expected in an abstract. So just in general, I think uh pros have covered this bit when it comes to an abstract, it needs to be very clear and simple. And when people read it, they know what you're talking about, they have an idea of what your research is about. OK? Whether it is um a research, a case report, um anything at all. AQ I project an audit, we're covering the same thing really. Um But specific to clinical research, I suppose you need to have a clinical background in the background section. Um And then um a clearly stated hypothesis in the objective session uh section and then uh describing your methods of how you test your hypothesis in the method section and then leading to it, then we'll be uh reporting the findings of your study in the results section at the end. Then in the conclusion, the summary of your study, whether uh what is your hypothesis and whether that was accepted or rejected. So any negative or positive findings is still findings. OK? And then reveals how the study changes the understanding of what we already have currently in our clinical um um uh information that we have currently. So I'm just gonna go through each part of it uh based on um original research. OK. So in the background, you need about three sentences on this roughly and this will describe what is known and why the study is needed. So this will include your theoretical and clinical background that we you need to describe and then the and state the problem or the gap in knowledge that you are going to address in the study and then a little bit about the context of your work. So this will be your background and then in the objective, this should be very simple. This should be just a one liner stating precisely what is the objective of your study. So uh an example will be to determine whether whatever to evaluate, to correlate to prospectively measure or anything at all. It just, it should be just a one liner in an abstract and then the study design and the methods to cover what did we do? So what is it that you have done? So uh you need to describe the type of your study. So when it comes to clinical research, it ca can either be a prospective or a retrospective study. OK. And then the study design can be either in experimental or intervention or it could be just an observational study. So you need to cover all of this, including the setting of where the research is being done and then the duration of the study. So this can even be covered in a sentence really. OK. So I know it's, it's a lot to cover but then you can actually really make it really compact. So just an example, this was from my own actually. Um second one, no, the first one only. So I just did a, a study of my um research bef uh back a few years ago. And what I did was just say this prospective cohort study examined 1000 and 5% from consecutively delivered singleton pregnancies in a tertiary center from January to March 2016. So that covers, what is the type of study? Is it a retrospective study or is it a prospective study? What kind of study it is? So, it's a cohort study. How many did we examine? Aha 1005 1000 and five. Yeah. And then where in a tertiary center? So you don't really have to state where exactly. So it's a lot of research. Um journal articles don't really want you to state exactly where uh so there's no name of the hospital. You don't have to say that you can just say in a tertiary center and when from when from January to March. So again, the, the, the second example I just copied from some, some other papers that this is a randomized controlled trial that compares called milking first, a delayed tramping. So that's what they did in preterm infants. So that's the, the um participants. And when was it recruited from June 2017 to September 2022. And where from 19 university and private medical centers in four countries. So that kind of covers um all of that. So what kind of study, prospective, retrospective, the study, design, the setting and the duration of the study? So that also includes the participants. So uh if you have a bit more to add on, you can um just like pro Higgins mentioned before your participants, what are the characteristics of your participants to just say 30 woman? So you can mention if there's a specific thing that you look at, say, for example, you're looking at woman above the age of 30 or BM I A woman with BM I above 40. Things like that that you need to uh describe in um in the study design in the methods section. OK. So um any eligibility criteria or any exclusion criteria mentioned that as well and then you need to put a little bit about what did you do? So um what is the intervention and then what are the outcomes that you measure? OK. Um The statistical methods is also important to mention that what did, what did you do? So what kind of statistical test was used? Whether you know students is that me? No, I could hear some noise, sorry. And what kind of statistical uh software that was used? For example S PSS. So this is an example when I uh again of one of my abstract where I include outcomes that was measured. So outcome including SG a low birth weight, all of that were compared with what? So this is when I mentioned some statistical um uh methods that was used logistic we just uh regression adjusting for uh this, this this and then what was I looking at? I was looking at the odd ratios um and the 95% confidence intervals. So then we go to the result section which is the most important part of the abstract. Again, it needs to be clear and concise it summarize what did you find in your study? So you need to clearly state the answer to your research question, whether it's a primary outcome. And then the of course, the primary, you need to mention that and whether you need to mention the secondary outcome, it depends on how much information that is needed and what you want on the um abstract. OK. And then we'll go through some statistics afterwards. Um some of the numbers um afterwards, I'll, I'll go into more details later then to the conclusion, the conclusion will be, what does this mean? And if so, so what, so are you, are you uh are your results significant or nonsignificant that doesn't really matter much, but it's a result. What is it with that results that would change um your clinical practice maybe? OK. But again, whatever you do and in, in research setting, it is uh even if it's um you feel like it's a good study, there is a significant association with things it is again, just an association. It's really hard to say for that. This is the cost of. So for example, if you say um obesity is the cause of high BP, but it may not be just the cost, just one, it could be potentially a different factors that could lead to that. So whenever you're describing your conclusion, you need to uh directly supported by your results. OK? But then you need to also take into account the limitation of your study and whether that will be uh a benefit and can cause um changes to your clinical practice, but do avoid speculation or over generalization. OK. So words that you should use would be something like the results could um the results may potentially. And um so it's, it's not saying um it's, it's because it's not one plus one equals two anyway. OK. So it needs to be, you have to um be very careful in what you say. Um uh But when it comes to associating things now, that's uh that part. So I'm just gonna cover a little bit about basic statistical reporting. Um It's not my strong point. But anyway, we'll, I'll try my best. Um So if you look at a study, we're looking at a sample, we're taking a sample from a population and then we have participation, participants from a sample. So we're assuming it represents the population as a whole. So again, it's not the whole population. We're only taking a sample. So whatever we have, the result we have is implying that it covers the whole population. That is why again, I said earlier that you need to be careful with when you with your conclusion. So just this basic stuff, I feel like it's very basic but it's probably good to just know this these basic things. When you look at data, there are the numerical data and then the categorical data and the numerical data can be divided into the discrete data and continuous data. OK. Discrete data will be the whole numbers and continuous where there is the decimal place. And then uh categorical can be nominal with no order like gender or ordinal where there's ca ordered categories. OK? Um So say level of prematurity could be very severe, severe things like that. And then looking at all your variables, then you describe your um you do your analysis, there are two types of analysis, a uni variate analysis where you just analyze one variable, which is something you will do very commonly. Um And then the bivariate analysis where you compare two variables, whether it's a two numerical, two CG or it could be one numerical and one categorical variables. So this would be very common. So I would say this will be in a lot of abstracts where you look at um categoric variable. Let's say you're looking at woman age less than 35 versus age more than 35 or you have ABM I of less than 30 versus BM I more than 30 something like that. Um um so you usually will report on the frequency and percentage. Ok? So of say of say 100 patient 30 were um you know SG A so you can have the percentage as well to that. Ok? So the way you write it as well, you need to be very careful with that, you need to mention numbers and um the percentage because there are abstracts where we could see that it was just reported with just percentage. What are the numbers even though say you've mentioned the numbers at the very beginning in your method section. Sometimes it's worth putting that number anyway in your uh result section now. So um the other thing is the confidence intervals. So this is um to look at the true value and whether that includes the true values. So there is a range where this is when con confidence intervals are used. OK? And you will want to know what is the confidence intervals of your value being in the true value? Um So uh this will be calculated to you by um a statistics um you know statistics. So it will it will be done for you. You don't need to know how to calculate it, but you need to um present the result with it with numerical. Uh So within univa lances again, if it's a numerical variable, so you look at centrality. OK. And the spread of the numbers. So when you look at centrality, it's the mean or medium depending on whether it is normally distributed or not. If it's normally distributed, you use mean and then you also report the extended deviation. If they, it is not um normally distributed, you need to report on median instead of mean and use I uh the inter qu uh quartile range. OK. So just an example here I have, it says, OK, uh 32 years old. So the mean maternal age, 32 with SD of 5.5. So that is how you report things. Um So let's say this one BM I 25.7 SD 5.3. When it comes to bivariate analysis of two category of variable, then usually is the chi square. Um And you look say, for example, this is one of my studies again. Um So I looked at smokers um with normal and abnormal uh placental insertion and you can say there's 22 versus 127. So 22.7% versus 14.8%. And then obviously, the P values will be calculated for you um in your statistical analysis and you need to um put that in your report. So when you compare one numerical and one categorical variable, then it's slightly different and you use the T test or the Z test. Um um And this is just again, you don't need to know too much details of the, how you calculate the T test or the Z test. But um the P value then is important but you need to report the T but um the T as well as the P value. And when you look at two numerical variable, then it's looking at relationship and whether there is um rel relationship, whether there's a correlation between two data, so whether it's a weak um weak relationship. So say for example, for uh the positive correlation where you see the dots on the X axis and the Y axis go up, that's a positive correlation going down. That's a negative correlation. If it's scattered everywhere, that means there's no correlation. So with our value, this is just roughly what it is from very low probability low um correlation to a very high correlation. And then the next one will be your odd odd odds ratio which measures uh the association between an exposure to an out outcome and then you have the adjusted odd ratio with adjusted for other factors. So again, uh you don't need to know the full details of it, but this is how you need to mention it in your uh results section. If you do calculate the odd ratio, um what it, what is it? And then you have the 95% confidence intervals and also your P value. Now the P value. So I think this is everyone probably knows about this. Uh but it describes how likely the findings is due to chance. Um but it should not be replaced by a qualitative co comments about it being significant or not. So sometimes you just say, oh this uh X is th th say drug X reducing the BP by uh 30 millimeters of mercury. And this one is only by five and it's significant without giving um any um statistical numbers and the fee value. So you do need to um when you're looking at your report, uh when you, you, you're stating your findings and your results, you do need to have that. So, um and when you present your P values, you also need to have the like for example, a 95% confidence intervals together with the P value. And when you report your P value, use the exact number of the P values and not just saying it's less than 0.05 or more than 0.05. And if the P value is really uh you know, small, then you report it as less than 0.001. Now, I hope that's not too much. But um what I'm, I suppose the conclusion of this um when it comes to the findings and the method section, you do need to mention a little bit about numbers. Um but each, each study will be very different. Uh Some studies will be just very basic looking at frequency tables and numbers and percentage, which is, um, which will apply to a lot of things like your audits, your quality improvement and all this. Um, but it's worth looking at some of the basic statistics to, just to know, um, what you're doing, say if someone is already doing some sort of research, you should, you probably would know, uh, a little bit of, um, uh, basic statistics. But I know this is something that we don't visit every day in our career. So you kind of know about it a little bit and then you forgot after a while and then you go back into it. So it's worth before even putting up your abstract um in looking at all the basic statistics, just the basic little things that needs to be um presented in your abstract. Um And then I just have this link here. I don't know whether are we sharing the slides may so you can have a look at that. It's just a very um simple document um on um audits and quality improvement that is also relevant to research. OK? It's just in introduction to stati statistics. And I think it's, it's worth looking at um prior to writing your abstract. Um That's all for me. I hope I didn't go over time. Thank you so much uh Both for, for your, your uh wonderful talks and uh plenty to learn there and good to get a nice overview of the different aspects of preparing research. And so we really do appreciate your time, I suppose at this stage. Now, um, there'll be a chance for AQ and A. Um, so what I'm going to ask people to do is if you can just type your questions into the chat box and send them on in. And what I'll do is if possible, I'll try and add to you as a speaker if you want to verbally ask your question. Um, or if you prefer, I can ask it for you. Um, just while we're waiting for people to, um, send in questions here, I have received a couple on whatsapp here, so I'm just going to read some of those out. Um, so this one might be more for you. Press O Higgins. Um It's a question about when it comes to writing abstracts and what tips do you have for being precise as possible in our descriptions while still adhering to a word limit or a word count limit? How can you approach that possibly might have lost, um, Pro Higgins there? Um, w would you be able to take that question? Doctor Schu? Um, just in terms of the writing abstract? Um, do you have any tips for aiming to be precise but also uh, adhering to a word count as well? Sorry. Do you mind just repeating that question? I, my screen froze for a little while there. Oh, yeah, of course. Yeah. Yeah, I'll go ahead and repeat that So the question was when it comes to writing abstracts, what tips do you have for being as precise as possible in descriptions while adhering to a word count limit? Yeah, that, that's a good question. My general advice is just write the abstract as best you can and don't pay attention to the word count at the beginning. Um It, it, it has to have logic in it. The abstract actually doesn't need to um to include all the results. It just needs to include the most pertinent results. For the conclusion, you have the the poster or your, the rest of your presentation or the papers or whatever for including results. And actually now more and more, there's a trend to including supplementary material along with papers where these, there's pages and pages of results online and that people can just access if they want to. The the concept of the abstract is a short communication piece. So the background in particular can be cut if you're having trouble at words. So I would write it. Well, I would write it extremely logically as we've been kind of emphasizing and precisely the background need only be a sentence or so it's just to set it in context. We wanted to do this study because we saw something and you can give, you can elaborate on that and really give it flavor and you do the actual presentation. But for the abstract, one sentence is OK, for most things unless it's really, really pertinent to what you did later on, your aim again, should be able to be given in one line and your methodology. Although we say it's, it's kind of critically important which it is for an abstract, it just needs to be a simple description. Our cohort was this, our intervention, was this or what we looked at was this. And you don't need necessarily for the abstract to describe all your analysis because you're gonna be implying that three year results. So you don't have to say we did it in S PSS or Excel or we did, we had ethics or all those things that you will say in your presentation, you can leave them out of your abstract. Then for your results, your your abstract should probably be depending again on the context about 50 if not 60 plus percent results. Um And that should be as precise as possible. It needs to give the numbers for your cohort. It needs to give the percentages along with the nominator denominator for every percentage that you have. It needs to give P values where those are relevant. And then your conclusion, you should put a little bit of effort in because this is what's going to grab the reviewer's attention more than anything is well, did they find anything interesting from this? Um And it ideally, it shouldn't be too wishy washy. You want to have something a little bit precise even if it's we didn't find a lot in this and more work needs to be done that that's still relevant. But as um, Doctor Mill is saying, like we don't want over generalis um, comments or statements, but again, just a concise bit of, um, a, a concise conclusion. So my advice would be, as I say, just write it, write it really well and then cut out which, what seems superfluous to the actual nitty gritty of the science and go from there. Ok. That's wonderful. Thanks so much, Doctor Higgins. And, and I just add that sometimes people use um tables. I know um I think it is allowed, isn't it in, in the abstract for jocks? Yeah. So if there's too much that you want to say, especially when it comes to original research, there's a lot that you want to put in there and then it will be too much in your findings uh area that you can kind of mention the primary outcomes. And then maybe if you want to elaborate on the secondary outcomes just to put it in um in the table. But you know, if it's just, if it's really restricted you, you have to pick up what is it that you want to report on? So the primary uh outcome will be the most important part to mention. Yeah, I would agree. And just one other point on that it, it can be a bit of fun. Sometimes I'm not recommending this as a scientist or as a clinician. But more just to have a little bit of fun with abstract writing, you can put it into any of the like a I large language models like C GPT or anything like that and say, can you make this 400 word abstract into a 200 word abstract? They actually are designed quite well for that type of thing, reading under handing, interpreting and revising language. Um So, II please don't ask Jack U PD to write your jobs abstracts. But if, if you want to explore that or if it's frustrating you, it can be fun and there, there can be learning in that. So that's one other thing to say. That's great. Um So thank you both for those comprehensive answers and we have two questions in the chat there. I'm just going to answer Christina hers one and I'm going to see if I can invite you to the stage in case she wants to verbally ask her a question. But Christina was just asking, what's the word count for the abstract? There's actually 2100 characters. Um So not exactly a word count, but um that's the character limit that the online submission portal will stick to OK, 2100 characters. Um And just to add to that, that would be for jobs for every different conference. They have like different limits, but generally it would in like 2100 characters. It could give you about like 300 words or something like that. So that would be in general for, for a lot of different uh conference as well. That to me um So um I've sent them but I'm not sure if it's gone through. So if you don't mind, I'll, I'll go ahead and read out your question there. So um is asking uh how do abstracts on qualitative studies with limited numbers or limited statistical? Uh h how do you do uh abstracts when you're doing a qualitative study with limited numbers? Um and limited statistical analysis available. Um For example, a quality study through interviews about c cause of attrition among trainees in obstetrics and gynecology. And so I wonder which of you would like to, I don't really have much experience with qualitative study, would you um you y yeah. Yeah, it, the problem with qualitative studies exactly as you point out really clearly there you use, there's a ton of information and it's how do you condense it? So it depends on what your core message is. So there, for example, excuse me, if you're talking about causes of re you may have found lots of multiple competing causes or you may just have had one or two key things. And so you just wanna present as much as you can the key findings, you don't need to go into huge detail with, with the research. If you've done it as a kind of a thematic analysis, then it's fine to say because what you're trying to say is I did something interesting and this is what I find. If you just say I did interviews, we interviewed this number of people, we analyzed it using this thematic approach or some other approach. The key themes identified were XY and Z and your conclusion will be there is significant or you know, um a problem with the tr or there isn't a problem with the tr and key factors identified in influencing this were either in big themes or in more specifics. And yes, you, you lose a lot of the um detail or a lot of the interesting findings in qualitative research by summarizing it in abstracts. And I suppose if you read the qualitative research in the literature, like the actual papers are so different to the abstract, but you just wanna tell people your main point, what you did and what was the main point? What did you look at? And, and, and that's it. And then you have to save the exciting the juicy stuff for the actual presentation. That's great. Thank you really, really helpful um to, to read that. And so keep on sending uh questions in. If, if anyone has any more, I have uh two more that were sent to me. Uh two or three more, I should say there's also some coming in. So um how do you guys approach choosing a title for your um abstract. Do you think a title has an important role in capturing the attention of um reviewers and potential attendees? Oh, yes, I do think that title is very important because the first thing people is gonna read will be your title. Um So it needs to be um interesting in that people wants to read your, your, your abstract instead of making it like a boring statement. So you kind of have to, how would you say this kind of make it really um juicy in a way. Um I don't know how to say it there, but you know that you make it very interesting and people wants to read it. So it is very important to try and think um of a good title for your abstract. Maybe if you want to add anything on that. Yeah, I would agree that it's nice when there's a good title. I would also say that if you're limited by time it, it needs to be descriptive and there are certain journals out there that will specify that the title must include, say the methodology like this was an observational study or this was a cohort study or this was an RCT. Um So obviously jos you have a bit more free re um but there are certain public pub publishing for it that will restrict you with your title. Um So it depends on, it depends on your audiences, the bottom line. Like if you're writing in a newspaper or magazine, you want a snappy, you know, attention grabbing title. If you're writing for some very formal journal, they may just say they want you to describe the study and the title and nothing more. So, it's a, that's a nice thing about drugs. You have a bit of free range. So whatever suits you, if you can only think of something descriptive and you don't wanna come up with something else that's absolutely valid. So you a little bit of a chance to kind of personalize your, your approach and that's, there should be a little bit of as much as I we're going on about scientific rigor. There should be a little bit of individuality or fun in it as well. Um In the way you express it, if you want just one tip about writing abstract, I think even if you just look at type um abstracts of um general articles just even to have a gist of what is expected of an abstract, I think reading it um reading different abstract that is related to what you're going to submit, I think will give you an idea of what is it that you need to put in your abstract as well? And it's a good learning as well for you when you're reading papers of how you're going to be writing it. So, of course, for jokes, it's the um just the abstract. But for those of you who are thinking of writing more um uh writing a journal article based on your study. It's always worth reading things first keep reading and just to see what are the ways of um writing that's there that you can um uh you can adapt to or, you know, use an example for writing your own just to add there as well. Um For drops abstracts, there is a character limit for the title of 150 characters. Um Please do write them in capital letters and that does ease us kind of compiling it for the abstract book. Yeah, that's great. So thank you so much. And so um keep sending in uh chat questions if you have any more. Um I just have two more that are sent to me. Um But uh if you have any more questions for the consultants, this is your last time chance to send them in. Um So one of the other questions I received is um you know, of course, rejection can be part of the submission process um when you're approaching conferences and journals. Um So how do you approach uh rejection? Do you have any advice on how to improve rejected abstracts? And how many revisions do you make to an abstract before you submit it? Do you wanna answer that? Um With the rejection? Is this for, for me to answer me? Well, um I have to say uh when I was looking at the ECT last year, I did have, you know, the ones that should be rejected as an example. Um an audit um with all the methods were not quite complete but it's there. But then, oh the, the report, the, the the findings of the audit will be um um what, how would you say this, the findings will be reported in the poster? But that's not an abstract is it, it's not really an abstract because it just you trying to just submit before the time and then not finishing your audit because you should have analyzed everything before you submit an abstract. So I find that very frustrating to read because it's like someone who's just wanting something in there and not really into committing to doing the job properly and um thinking that with jobs you can just get away with it. But I think we need to really focus on uh as mentioned by Higgins. This is a scientific meeting. Um And this is what we have here um in Ireland. So I think it's uh we need to kind of up our standards that uh with a lot of submissions now. So those rejection will unfortunately be there if, if, if that is the quality that we're seeing, this is why we have the session today just to kind of improve that too. Um So um with the ones that I um reviewed, I did give some feedback. So I think we probably would do that again. Maybe. Yes, I II think we, we will do that. If there is any rejection, there's ways of to improve your abstract, which will be included in, in um in the feedback. Yes, that's absolutely correct. Uh So I think starting from last year, we asked our abstract judges to give the feedback and then there is a small group of us that compiles them and then kind of like send it out. Now, last year, we could only send it out to those who has rejected because we receive a few 100s of abstracts. So it's really down to the jobs committee really to come together and kind of put this together and we are a, a small community as well. So I suppose it's, if you, if you do get um your abstract rejected, II understand it can be very um sometimes discouraging and I've had my own abstracts rejected before as well. And I suppose to take away from that really is that the idea of all this, that the whole scientific pursuit is that when you get rejected is to find out, well, why was it rejected? And how can you improve it? Really? And, and that's what we are hoping to achieve with jobs as well. We want to make jobs more than just a line on your CV. We want it to be an opportunity for you to actually get feedback and improve and kind of like professionally develop yourself, your, your, your scientific career itself. So if, if you get rejected, you know, hopefully that with this webinar, it will reduce that uh rejection. We are aiming to accept as many high quality, good quality abstracts as possible. That is scientifically sound. Um But if, if you do get it, you know, we, we are more than happy, we were trying to give feedback so that people can actually improve um the abstract in the future. That's great. Yeah, and I'll just say one more point on on that, which is ee exactly like the previous speakers have said. Um it, there's obviously reasons behind it, but having an obstructor rejected, having anything rejected is just a normal part of clinical life. In fact, every single researcher out there who has any kind of credibility will have had much, much more rejected than accepted. It's so rare for a paper to be published or accepted on a first submission. It's almost all you apply to some place they say no, then somebody else says no, then somebody else says no, then somebody else says yes. So there could be three or four rejections preceding every publication, same with grants and high level kind of scientific funding. Every single researcher will have a huge proportion of um grant refusals and failed applications before they get one single grant. It's, it's just the way life goes. Like rejection is a time for learning and for growth. Like you look at people like the sports guys, you know, how many races do they lose before they win a gold medal. Michael Jordan said, like I lost many more matches. I've missed many more baskets than I've ever scored or won. So, um, it's really your, your attitude, I suppose you say. Ok, well, they didn't like that that day. Wa was there something I could improve or was it absolutely brilliant? And it just wasn't to their taste that often happens as well. Um, or, um, are they just really bad in jobs and I'm brain? But no, there's always something, there's always something you, you can learn for it and, and occasionally, and often, particularly maybe not so much for jobs, but with other um submissions and publications you will have in the future, sometimes it just doesn't suit the journal. There's nothing wrong. You've done a really good job. It's just that particular journal or those particular reviewers. So you need to just get a balance between not taking it too personally and yet having the maturity to grow or learn from it. Yes, I've had loads of rejections too for my papers from journals. Um But then each one will actually get you to improve it further in some sense. Yes, of course. As uh Higgins mentioned, sometimes it's just the journal you've done the best you can. Um And maybe it's not covering the topic that you uh your paper is on and things like that. But every single rejection in that sense would be to, you know, there is room for improvement in a way. But for jokes, I think it's more the quality that um cost that could cause the rejection in jokes. But in general, yes, that you probably have done a great job, but then it's trying to improve it and I know it, it can be very frustrating and a lot of time spent by revising it again, but it's always worth it because it will improve it further and, and you can learn a lot from, from all those those mistakes as well for future writing. Yeah. Yeah, projects definitely part of the research research cycle, but um good to get all of your, your um input on that. Um I'm just going to ask one final question. Um And then I'm going to hand over to me for closing remarks, just a follow question about um um feedback. Uh you know, how do you approach um getting feedback from mentors um particularly in a case where it might be tricky to track down a consultant or to get concrete feedback. Do you guys have any advice on how to approach um obtaining feedback or any different avenues that could be, could be uh used? Yeah, that can be a bit tricky. Um um I would say sometimes what you can do is depending on who your um supervisor or um trainer is that to approach it in different ways um that you bumped into that someone and just ask one simple questions maybe to try and get that on the spot. Sometimes that works with, with very busy consultants. Um um So, or obviously, the best way is to arrange a formal meeting, but sometimes that's what you need is to try and get um on the spot. Um uh It is very difficult if you don't get the supervision that you deserve. Um But again, you can learn from your colleagues as well, so you can even approach other consultants that is more approachable, who is willing to look at it for you. Um Or even your colleagues, your own colleagues just to read through and see whether there's any feedback uh that they can give to improve your your abstract. Even to just, you know, ii suppose when it comes to say if it's a clinical research, you do need supervision from the beginning of how you are going to do your research, the different steps in each part of your research. Um So, so say for jobs is only just maybe a simple abstract that you need to look to be looked at so that you can approach other people for it or even your colleagues. Mhm And there should be responsible consultants for all that like people shouldn't really be in situations where they're doing clinical research without the awareness or oversight of a consultant behind that. Um So if it's not your trainer directly, this is the kind of hierarchy of people to ask is the consultant who is overseeing the research in whatever shape or form to say you are doing and ordered in surgical site infection, who was the lead consultant for that project, then your trainer and if both those people are not available or, and you're finding it difficult to engage with them, then as the others say, sometimes there's other the consultants who have an interest in that or who have linked or linked to that project or um other kind of senior colleagues or senior SP OS or tutor or research people that you can say, can you have a look at this abstract? But there should be consultant involvement and if there isn't, then that's not ideal and that should actually be fed back to, to your trainer. OK. That's wonderful. Thank you. Thank you both so much. So I'm just gonna hand over to me for closing remarks and would you like to take me? Yeah. So um thank you so much to everyone. I think at one point we have um nearly about 60 plus people attending this webinar. So um everyone has, you know, lots of things to do precious time as well. So thank you so much for making this uh webinar such a great success and especially like, really, really thank you to Pro Amy Higgins and Dr Katie Jsm for such an insightful and informative session on how we should kind of improve our abstracts. Like seriously the guidance that you gave the tips they, they're just so invaluable, especially now that we are approaching the deadline for abstract submission for uh the job scientific meeting. And really, you know, we we're really, really fortunate to have consultant trainers like both yourself who are so deeply invested in the growth and development of trainees. Like II think everyone would echo my sentiments that, you know, your, your commitment to try, you know, trying to improve our skills and supporting our research is really something that we really, really appreciate um to all the audience today, all the attendees and trainees. Um I just really encourage you to take the valuable lessons from this webinar and really apply them to working on your abstract submissions. Remember to focus on the clarity, the conciseness and ensuring that your abstract answers the key questions as to why you did it, you know, what was done? Why did you do it, what was found and why does it matter? Um We'll be sending out like a summary of the, of our talk today to, to everyone that's on our mailing list. Um Just so that's another resource that you can use to try to improve your abstract. So another reminder is that um you know, if you, if you're not working on an abstract, yeah, you've been worried about it. Well, you know, the job scientific meeting is a great start to actually starting this process. We're very open to kind of advancing your, your development. So we're really excited to see what you can bring to and like I say, we accept all high quality so that we could get. And so please remember that the abstract submission is has opened and will is ongoing and it will remain so until the 25th of September 12 p.m. And we really endeavor not to have any extensions at all. So please work on your start working on your abstracts, engage with consultant trainers as soon as possible and not to leave it until last minute. I know that's can be an impossibility for some of us. Um And really, this is a chance for you to share your research, your work with, you know, all your peers, colleagues and receive valuable feedback. That's something I think jobs is different from others because if you, if you submit to others um conference, you rarely would get like such a personalized feedback. And that's something that we we are aiming to do with jobs so that you then bring that to an international platform and, and be better and, and get a a better kind of um outcome. We also really want to hear from all of you on how we can further support like jobs stuff and support your research development. So please take a few moments after this to fill in the feedback form that is automatically sent out to you at the end of this webinar. Um Your input is really, really valuable to us and it really helps us to tailor kind of future sessions on how we can help foster a more supportive research community because not all of us as, as lucky to have um kind of trainers that are as involved in this, as much as uh we all should. So once again, really, thank you very, very much to Professor Neil Higgins and Ismael for your time and expertise. Thank you everyone for joining today's seminar, uh webinar. And I really look forward to meeting most of you at the Drug Center meeting on November 29 2024. Thanks.