Thanks, everybody, for coming on this very chipper, cold day to the region.
As a thought, Goliath was just on Twitter and I just took a photo of all your the back of your head and then the hashtag is catalogue of bias.
Is that right with the American spelling as opposed to the English spelling, which has a U in the eighth?
So it's catalogue of bias without all one word attached.
So if you want to cheat tweet, it'd be useful if you could use a catalogue of bias.
So thanks for coming. I'm going to talk a little bit about why we started, why did we start?
And then David's going to talk about what did we do?
And then Ian's going to come and talk a little bit about some of the integration of some of the other products,
like the James Lynn Library and the importance of bias.
About a few years ago, I wrote this piece about Reflections on Day Circuit's time at Centre for Urban Space Medicine when he died.
Unfortunately, about four weeks before he died, he sent me an email and he sent it to about four people and he said, Dear Carl, have I got it wrong?
Somebody introduced me to propensity scores and I'm wondering whether randomisation
I should have been thinking more about propensity scores in the last 20,
30 years. And all I could think is why this guy seriously and well,
and he's still passionate about bias and treatments and effect sizes and he was really interested
in that aspect and and in writing that there are about four or five papers or not papers,
maybe series of articles.
David Sackett with the first director of the centre I first met in 94, Pete, some people in this room, me and you many years before that,
and work with him and there are about four or five books or series of papers that
I think everybody should read if you're interested in evidence based medicine.
One of them is probably the books on basic clinical epidemiology, which actually has a series one, two and three I think is right.
And actually the first series is fantastic. The first book is is outstanding.
The second is a sort of series of articles called Clinician Rounds, of which is about 30 papers in that series.
And there's some great papers in that. Obviously, the editorial, what is IPM and what it isn't?
And then there's a paper on online which you can look up about being a mentor.
It was really interested in mentoring and what that meant, and he and I thought it talked about who to gravitate to and who to avoid,
and they seem to be more about who to avoid than who to gravitate to in that series.
But there's also a paper in there which is this paper bias in Analytic Research by David Sackett in 1979,
printed in Great Britain in the Journal of Chronic Disease and is a really interesting paper and in reflections and thinking about.
I've always been aware of this paper, but in reflections when I read it again,
there's a really interesting bit at the end of the paper which is comes through and he lists all these priorities and biases in 1979.
And interestingly, at the time then case control theories were the fact for journals.
So case control theories were on the rise and cohort studies were dropping and there were very few randomised controlled trials.
And if you read this paper there were sort of four priorities and you know when you look back and you think oh we should do this a few years earlier,
I think the advent of the Internet allows is it put the continued development of an annotated catalogue of bias.
Each citation should include a useful definition, a reference to example,
illustrating the magnitude and direction of its effects and a description of the appropriate preventive measures.
If any I volunteer for this task would welcome collaboration and would appreciate receiving nominations and examples of additional biases.
So there's a really interesting idea that actually this is a fantastic idea.
It's about 40 years to early, isn't it? Technology allows you to look back and think, wow, this is a fantastic idea.
We could now do that with the Internet. So that's the mission of the catalogue of bias, interestingly biases.
And when you think about it and when I was looking at this is,
is there are loads of examples of really interesting bias but do like this on the James Lindley library is
what our biases question mark and I think that's a really important issue for us always to think about.
What do we mean by bias and out there in the world? When you go out and talk about biases, they're all around.
They all affect us. But very few people really understand what are we on about and what does it mean?
And there's a good and this is Peter Award.
Here was the first triallist in aspirin, correct?
Who did the first trial in aspirin, which put biases in test and treatment.
And those influences and factors that can lead to conclusions about treatment effect that are systematically different from the truth.
And that's a really nice definition of why we are interested in biases.
Interestingly as well, when you go and start to then think about biases and this is a just two example,
all of the people that are involved in evidence based medicine or in Cochrane in some way that everybody recognises,
like Peter Godchaux, like Kenneth Schulze, like Bill Goldman,
like Ian Chalmers at some point have gone through a mission in bias where they've gone, This is interesting, I'm going to write something about it.
This is a great paper by Peter about reference bias.
When we've been teaching for over 20 years is the empirical evidence of bias that we've been teaching all the time.
So we've been teaching about this and an empirical the magnitude in the direction of the bias.
More interestingly, it's not as straightforward as the magnitude and direction.
Bias is not a straight forward something I could teach you today.
And we walk away and we have all the knowledge.
And there are many papers like this by Leslie Ward, who led this, which I like about all the people who've who've helped too,
that it seems that in situation when bias is like the size of such bias is unpredictable,
sometimes it's different for subjective outcomes than objective outcomes.
So although we might say it's one way, sometimes we're not sure.
And then it's just like I put this picture up because I think this has been the one bit where at this point,
and this is in 2000 and about 910, this is the Tummy Flu Collaborative at New College.
And there's Tom Jefferson, who was one of the lead. But Toby Toby's there and Chris Delma, myself and other people like Peter Doshi and Mark Jones.
So it's a real international collaboration. But at the time there's been a there was a sort of approach that there was a great approach.
That bias is quite straightforward. It's high, moderate or low.
And in that there are three or four biases you look at. And if they're present, you're low risk.
And if they're absent, you're sorry the other way. If the president you've got high risk, and if they're absent, you're low risk.
And suddenly within the the work of the of the review we did in terms of I'm a lot hold
on there are a myriad of other biases that are really important in what we're doing.
Not least reporting bias is a huge impact and that also was very influential.
And I'm thinking we have we we just made this reductionist approach.
Is that appropriate? And then there are other biases that keep pervade in everything that I've been involved in or done.
Publication bias is what this is all about. Huge project where 100,000 people supported it.
Even sent me an email the other day from about that.
Actually, bias is good because it keeps us in business. Without bias, we'd have no job.
And I thought, Yep, bias is what this whole project is about. And so that led us to the idea of the catalogue of bias building.
It's a sort of project that builds from what's come before,
from what David Sackett was talking about, to what many other people have been talking about,
to just say, let's build something that can allow us to put together a catalogue that will be useful for me,
for you, for individuals out there, for researchers.
We get asked a lot. I get asked a lot. And people in this room by Science Media Centre, could you comment on this research?
Well, actually, it contains this bias. I'm sick of writing it. Why not just point to some place?
So that's what it's about. And we think as researchers, our mission is to reduce bias.
But there's also another mission out there. We means the general public just doesn't mean we in this room.
I we as everybody must be aware of the different type of biases,
their potential impact in how this affects interpretation and use of evidence in health care decision making.
Very important concept. And I'm going to hand over to David in a minute.
But and we think this is also so important. And one area where I think it's hugely important is in this group is in the the 18th in a new generation
of people who understand bias and its impact on decision making and how we use bias to make decisions in.
Once decision making. And we think it's to equip the next generation with the critical thinking skills to make informed health care choices.
That's also another project called himself Informed Health Care Choices that's involved in and the.
OXMAN And their opportunity to teach that.
So that's why we started and this is a project where we decided to launch the website and Dave is going to show you what did we do?
So, yes, I was very passionate when we decided to take this project forward right from the word go, because as a as a teacher.
VBM And there's a lot of people on the course that I'm running this week.
One of the modules for our events based health care master's program this week,
I'm always look, I was always thinking, so why would we care about the biases?
Why are we even looking at these biases? What does it matter?
And when I first started off, I was struggling to find places other than usually going to to Ian's paper, the Schultz paper,
and saying this is one of the clear cut examples of where bias is important and how it impacts on the results.
But I often struggle to or it took a long time to dig out some other ones.
And what would have been really useful for me is what we're about to sort of show you.
Now, if I had this four or five years ago, I could really just point to my students go, Look,
this is why these biases are important and this is why they matter and this is the impact that they're having.
So like I said, I was very, very keen to get on board and really happy that I was always asked to come on board and be part of the project.
And I managed to convince the crack team at the CBM Centre that this was important and they should get involved too.
So all of these guys here and girls, we all went off for two days.
I think it is one of the first away day project. So for two nights I've had the CBN and it was great fun and we went here.
Anyone recognise this part of the world? Hmm.
Well, no, it's. It's in. It's in. It's not far. And the budget wouldn't stretch to a foreign holiday so it's.
Yes, there's Bristol with the coloured houses there.
So the death of England.
Yes, absolutely. And here we all are, day one.
I don't know if we took another picture after day three, but it probably wasn't quite as as as bright and breezy as this.
And we set off with prior to leaving, we were obviously familiar with David Circuit's paper,
which we within that paper he actually identifies 56 biases that he lists relevant to cohort studies,
relevant to case control studies, and a few that are relevant to experimental studies.
But it's quite hard to tease that out from the paper itself. So we had we had 56 in mind.
Then you go off and you do a bit of research and you realise that John is,
you need is is also interested in this area and he comes up with 235 biases and you start to just your head starts to sink a little bit.
But luckily I think for us he managed to whittle down those to 35 into four concrete sort of areas.
So there was a lot of overlap between those 235 and actually 40 was was what he ended up presenting.
So we took those on board as well. And then we obviously were familiar with our through our own work about the Cochrane as well,
which some of these biases weren't captured either in the newspaper or in the paper, obviously because Cochrane wasn't around then.
So some important biases, particularly in relation to clinical trials.
So armed with that, we took our we took our project folder and our project tracker and we ended up with roughly around about 100 biases.
We turned up at Bristol with about 100 biases to start off with, which we then set about in, in, in the, in the hotel bunker room.
And here we are in action.
We split into three teams, uh, folk here and then, and Carl and this is Douglas Badenoch and Ruth Davis is in the room as well as CBN manager were in,
we're in the control room I think, doing something useful, but it felt like we were doing all the hard work.
But I'm sure Ruth was definitely. But Ruth was definitely keeping us to task and keeping us at the time.
And so we spent three and so for some reason I always end up this colour seems to follow me around.
So I was in the pink group too and we had 30 biases per group and we came up with a web
page template that basically had to cover these four areas that we were looking at.
So what's the definition of the biases,
the bias that we're looking at and where we're going to go look for and what we're going to what we going to write?
We then had to find some examples of where these biases that we were we were we were we were tasked with were present in the literature.
So some real concrete examples of yes, his, his, his a bad example, a good example or a bad example,
whichever you're looking of it, of this particular bias in action key was this as well.
So like David said, can you can you can you show evidence that it matters?
And sometimes when it does matter and sometimes when it doesn't actually matter and put that in one place.
And then also, as David also iterated, the catalogue must tell us how we should avoid this going forward.
So that was what we were armed and tasked with, with completing. And we basically had a search strategy that consisted of four areas looking at the
papers within our own catalogue folder that I've already mentioned from the 56,
the 235 and the 40 papers. What's in there that's already answering some of these questions?
But also we didn't want to miss any current up to date. Systematic reviews was key.
So we also looked in PubMed crews for any systematic reviews or anything that would would cover this area that would be key to it to look at clearly.
We also did a little bit of Googling work just to see what came out top for these different types of biases.
And then we were keen also to link with the James Allen Library to see what their what they had on
on each of these different biases and any links that we could then bring into to our Web pages.
And this is, if you like, the rough template that we start to complete.
So we just start to complete all these for different biases. A good example of allocation bias here.
This is the, if you like, the web template that we were going to use.
So definition a background, something along the biases that this this bias currently relates to which we are,
which when you go into the catalogue online, you'll be able to link off to the similar biases that each bias is related to and snowball from there.
A nice little definition of the background of the bias.
Again, a concrete example where we could find one information on the impact with links to show you this is Watson.
And often, hey, we've got our friend Schultz and Grimes, which is the which is the original paper we just showed you earlier.
And then some nice preventative steps about how you can deal and handle with this bias
in your research or in how people doing research should handle this type of bias.
Okay. And then a bit of a some finger pointing to some other linked resources that we're going to help you with and provide for you,
as well as the sources that we've used to to provide for this this page.
So that said, I'd now like to just show you the actual catalogue, which is ready to ready to roll out.
We hope so. Here we have it. Okay. So this is the home page for the catalogue.
And I think it looks really, really cool. And I think we've done a really great job.
And Douglas from Douglas Barton up from innovations, done a lot of the work, done all the work behind that,
the back end of it, taking those sort of A4 sheets that you saw and turning into something like this,
which is which is pretty impressive and really cool to the front page, we'll just have an example of, of a bias,
probably the most recent one that's gone in, but also will have some, some links to some of the blogs that we've done, the latest blogs.
And in fact, I think today I sent Carl this and he got it up today just to sort of say my take,
pretty much what I've just said to you at the start of my talk,
how for me, when I first started in IBM, this type of thing would have been really, really useful.
And what we're hoping is that for other teachers, particularly teachers VBM,
who want to use examples and go somewhere, this will be the place to go for for stuff on biases.
And then you'll see if you just click on the top rows, we then get into our biases.
Currently, there's 25 biases and these will be updated regularly.
Like I said, we've got at least another 40 or 50 in the background and they're constantly being checked by the
team to make sure that they're correct for content and correct and ready to go out on the pages.
And we just offering those two different areas on the website where you can start to select the biases you're interested in dropdown menus.
And this will become populated as we as we go through it,
you'll see some sort of if you're familiar with some of the areas of bias and different types of terminology,
but you'll also see some sort of strange ones like this hot stuff bias.
And trust me, when I click on it, it doesn't go where you might want it to go. It goes to a nice definition of of what hot stuff bias is.
And this actually came from David Circuit's paper.
So there are a lot there are a lot of quite obscure and obsolete terminology that David used in that paper for certain types of biases.
So you thought, what's hot stuff bias? What's that got to do anything when you read it?
I think this makes clear and perfect sense, if not now more than ever, when you start to read what he's talking about around Hot Stuff bias.
So there were some biases that we we realised that he went searching.
Yeah. Be careful your Google search for this.
But you go off and you start and just nothing's going to come through, certainly not on PubMed or any sort of any sort of regular database.
But that that type of bias is very, very prominent now, and it's a very one that needs to be at the forefront, I think.
So we included things like this because we thought actually that they were making sense of that.
And they certainly make sense now, even if the terminology isn't quite what we would call that now,
I'm not sure what that kind of what the name for that bias would be. Now, if it's not hot stuff bias.
But we can we can we can debate that as well. I think. So there are some some some on there from the catalogue that certainly you wouldn't see now.
But we've put them in anyway because we think. Some of them are actually quite relevant in terms of impact.
Again, some statements here, we just sort of said, funny enough,
hot stuff's not been formally investigated and those looked at the impact on effect size for hot stuff fires.
But are you making a judgement of your own, your experiences and observations of how you think that that that bias is impacting at a time?
And then we've got a blog, so we're going to keep a blog going.
I've started the first one, and I'm hopefully relying on I'm going to rely on the 18 to to be filling in and putting blogs in.
But we'll also be inviting blogs in from from people from the outside as well.
So external to keep that to keep that running. All things bias and all things bias related.
Okay. And I just wanted to link actually from if you go back to one of the pages I talked about the James Lind Library,
and here's where I think I'll bring in Ian because you'll see where we can and where we could find a link we as best we could.
We said, okay, there's also some we'll get information on this bias on the James Lynn Library.
And we put the definition in that James Lynn gave. And then when you click on that, you go off and you've got a really nice definition with a lot,
a lot more content than what we've been output in our catalogue. But that wasn't the point of our catalogue.
All catalogues are one stop shop and then you can go off and start reading some more important biases there.
So I think on that note, we invite all right, if you wouldn't mind, just give me a few words on the background, too, to the James Linda.
That's great. Thank you. Okay.
Well, first of all, I'm really chuffed to have been invited to come to this launch.
It's just great. I mean, for for someone who, as I have been trying to point out, that you don't have to be a statistician to understand about biases.
And biases are actually more fundamental than statistics.
It's just very, very good news indeed. So I want to congratulate you on having taken this initiative really fantastic.
And you can imagine that I'm particularly chuffed that you've seen that the James led library is relevant to this enterprise.
That's great. I talked to Dave about this paper and I asked him what's happened to and Kavita.
Now, I heard I was very pleased actually to hear that Dave David had actually looked to see if he could find out what Joe and care better is.
So far, neither of us know. Dave didn't know, so I didn't know.
So I think that one of the first jobs of the catalogue of bias is to do a worldwide search for the current whereabouts
of Joe and Keir better because she deserves some credit actually for having produced the first catalogue.
And by the way, the way it is spelt is actually the European ways, not the English way.
It's the same in French as well. So it's important.
I eventually persuaded Dave to write a commentary on this paper and that involved
finding out a little bit about the meeting for which he had prepared it.
And there was a great deal of discussion about case control studies, how valid they were.
There were some people who wouldn't give them the time of day.
Other people who realised that they were the only way of going about addressing some questions.
And we got the person who convened the meeting, someone called Michael Ibrahim, an epidemiologist,
to write an appendix to Dave's article in that giving a background to the meeting.
I was interested because they've when he came to Oxford made a.
Made a somewhat bad impression on Martin Vesey, who is the professor of public health.
Martin felt that because of this paper,
Dave didn't have any time for the sort of work the cohort studies and the case control studies, which Martin was doing.
And Martin, they never really got on. It has to be said during the five years that he was here.
And I thought that, you know, something might have come out of Dave writing about this exercise.
And so I got him to write about it's a very nice piece that he's written.
It doesn't really uncover much about that, but it's there for people to see.
So what I thought I'd do is let me see it.
Yes. I'll just give you a I'm going to give you a very few examples from the James Lynn
Library to illustrate how it might be useful to people using the catalogue of bias.
So it has these three silos, if you like, of records and articles,
general articles about what constitutes fair tests that you in general need to have a comparison of some sort.
If you want to make an inference about an effect of of an intervention,
then the bias is and you see, we don't have 235 or whatever it was and certainly not even 100.
I think there are seven there. So I think that to some from there got something on the play off chance and
then we got something which is to remind people that at the end of the day,
all of this is an attempt to try and make things better for patients and the public in general and work, which relates to that.
And so, for example, the quality of reporting and key papers in the quality of reporting research go into that.
But before you start to bias, I felt it was quite important to actually say when you don't need to worry about
bias because there are certain treatments that have such slam bang effects,
that bias is unlikely to be a cause of these effects.
This is a paper that Paul Glacier and some of us wrote in that was published in the BMJ.
And we've got some examples of treatments with dramatic effects and the sort of ways in which that drama shows itself.
But I just took one from 1801, which is Astley Cooper treating conductive deafness by doing temp anatomy.
It's sort of hurts to look at this spear being stuck down the area.
But basically that's that was an example of a dramatic treatment.
People who hadn't heard that, hadn't been able to hear for some time suddenly could hear.
So you don't need to worry about bias there.
You may need to worry about it and different sorts of deafness, but for certain types of deafness, that was a dramatic effect.
But two biases which I've chosen to illustrate allocation, bias and observer bias.
Observer bias is sometimes called measurement bias and other sorts of terms as well.
And this one I thought I'd start off with was done in 1809 during the Peninsular War.
In fact, in Portugal, where there was a a base hospital, seemed to be quite busy soldiers, often with fevers of one sort and the other.
And there were three surgeons there. One of them was Alexander Hamilton, who described himself actually as the most beautiful man in existence,
which is the name of the biography of this man, who was a bit of a bounder in all sorts of ways.
But he reports in his M.D. thesis in Latin for University of Edinburgh that it had been so arranged that
366 soldiers had were admitted alternately in such a manner that each of us had one third of the whole.
The sick were indiscriminately received. No selection were attended as nearly as possible with the same care and accommodated with the same comforts.
Neither Mr. Anderson nor I ever once employed the Lancet bloodletting.
He lost two eye, four cases mortality of 2.5%, whilst out of the other third, the other surgeon used bloodletting.
35 patients died. So ten. A higher mortality rate using the what was then a very widely used form of treatment.
Here's another one, which is methodologically a bit more interesting in a way.
Thomas Graham Balfour was a doctor. He was in charge of an orphanage, military orphanage in Chelsea.
And he went on to become the president, the Royal Statistical Society.
So he was, in that sense, a fairly exceptional medical doctor.
And there was the idea that giving Belladonna to children during a course of a scarlet fever epidemic would protect them from getting scarlet fever.
So what he did was he. There were 151 more boys of whom I had tolerably satisfactory evidence that they had not had Scarlet Tina.
In other words, he wanted to make sure they were eligible for prevention of scarlet fever.
I divided them in two sections, taking them alternately from the list.
And this is the key statement to prevent the invitation of selection.
In other words, to prevent bias. I don't know an earlier quotation which puts it quite as clearly as that.
What he was trying to do by the beginning of the 20th century alternation was being used very widely,
and it had become called different things in different languages.
And basically alternation was the predecessor of random allocation.
Random allocation. Really, it's only advantage in practical terms is that it's easier to conceal what the allocation method is.
But that doesn't mean to say that if you use around the numbers, the allocation schedule is necessarily concealed.
It might be stuck up on the wall so that when people the next patient eligible comes in, you look at the list on the wall and you decide,
actually, no, I don't think I want to put the patient in is going to be allocated to that.
So you can have because of lack of concealment of a random allocation schedule, you can introduce bias.
Random allocation does not protect you against bias without concealment of the allocation schedule.
Now, reducing observer bias, well, this was a comparison of so-called tractors,
metal tractors with placebo tractors made of wood for treating rheumatism.
This is from 1800. This is Gil Ray's cartoon making fun of this very fashionable treatment.
These are this is what they looked like. And as you'll see, the inventor, Perkins, had patented them.
And the idea was you put this little bit of metal spike against someone's nose and it is there or some part of them.
And you drew out of them some magnetic thing that was causing the problem leading to their symptoms.
Well, this man, Hagar, did a very nice job.
He said, right, well, let's make some out of wood to look like metal, and therefore which will not act in the way claim for them.
And he wrote up this lovely pamphlet of the imagination as a cause and a cure of disorders of the body,
exemplified by fictitious tractors and epidemic ball convulsions.
And he was unable to find any difference between patients treated with the wooden ones and the metal ones.
That's an example of observer bias, the observers being the patients, of course, being controlled by a placebo controlled trial in 1800.
This is an example of a comparison of of which involved both control of allocation bias and observer bias.
It was a comparison of activated and ordinary horse serum for treating diphtheria 1918.
It was done by this rather serious looking person, Adolph Bingle, and he worked in this place, which looks a bit like Wormwood Scrubs, I would say.
But presumably it was the the state of the art hospital at the time, and this is how he puts it.
He proceeded cautiously. I began in 1912 to treat alternate adult patients with antitoxin serum and with ordinary serum.
That's an activated serum, exactly.
In the temporal sequence in which they were admitted to the ward to make the trial as objective as possible.
I haven't relied on my own judgement alone,
but have sought the views of assistant physicians of the diphtheria ward without informing them about the nature of the serum I detest,
namely the ordinary horse serum. That judgement was thus completely without prejudice.
So again, I'm keen to see my observations checked independently and most warmly.
Recommend this blind method for the purpose. So it's a nice example of a trial which is controlling allocation bias and observer bias.
The last thing on to do with Chance, I know this isn't part of bias,
but I thought that for those of you who weren't aware that meta analysis was used a long time ago and in this particular case,
1928, here are three hospitals in which alternate allocation trials had been used to test serum versus no serum.
And outlined in red are the combined meta analysed results from the three hospitals and which are three trials that were done in 1928.
The word meta analysis wasn't introduced until 1976 by by Jean Glass, an educational psychologist.
That's my last slide.
It's basically to show the people who have been involved in the James Lane library, and I haven't named all of the more than 100 authors of articles.
Jeff Aronson is one of them. I'm trying to see whether any others here.
I don't think there are any others, but basically there are a lot of authors of some fantastic articles, including yours, of course.
Yes. So there we go. I guess we started.
One of the things I'm always a great believer is don't wait until it's a finished product.
Make it happen. Get it out there. Get some people that are interested in that and build and build.
And then in about five years time, we'll have an amazing product.
And I believe we've got a lot to do because if we look at each one of these, we could do a better job and probably find better examples.
We could find better. Some of them think about the impact and think about preventive measures.
Jeff and I were talking today about stage two, stage three, and stage four.
Thinking about the impact of these. Both have had a big impact on the papers.
We see how they interact. What's the true impact?
How do we talk about this in terms of reducing the impact in research of what should we be doing, the researchers, to prevent that?
So we think there's a lot to do here. So it's one of these projects where we want to just keep going and going and going.
So it's not just building the new viruses, it's also coming back and revisiting the ones we've written and thinking, how do we improve that?
I'm particularly interested in people who are interested in want to be involved.
And so we've got on the contact page a forum where you say, I'm really interested in a bias.
Here's why. I'm going to tell you a bit of background about why I'm interested in that.
So if you're really interested, we'd love to hear from you.
And I think one of the things when I click on the BIOS is what I think we're really interested as well.
And one of the key is Reef and I and the listener, Elizabeth Spencer,
are trying to get a bit more money because we can afford to take about ten people away at the moment, and at some point we may grow that number.
But I think there's a bit in the biases as we go down. The background is is here we want to really grow in in this section.
There's the bit here. The the no, the one with the James Lynn Library that's below them, which we haven't filled in.
It's got teaching resources.
And we want to put some with each bias and ability to pull off the couple of flying that make it when you teach in regular.
And I think that will be a huge benefit when we do that.
And so some of the mission is when you wrote about this, it's not just to say, oh, we're onto the next one,
it's been okay, what would what would we do to teach it how to help a teacher out there in the world.
So that's the global aspect of having and that's where it fits with not just teachers and postgraduate health professionals.
I think this is everybody is with schoolchildren in particular.
So I think that's the mission is to is for people to get involved will keep going back to their bias of thinking,
I'm going to fight to improve it, so I'm going to finish it in.
Thanks to David, thanks to input, thanks to everybody. Come in.
But particularly there are a number of people in this room who we drive to Bristol.
I can tell you they like the beer at night and the meal and we had them hammered for about 14 hours of writing.
And at the time, I'm not quite sure they thought this was a good idea.
But I think now when you see the finished product, I think it's been a really fantastic journey and we're hoping to continue it.
So on that very basis, thank you very much. Everybody has come and those who've helped that America participated in developing the catapult.
Thank you very much. And.