So why are we lighting fires in space, and why am I about to set fire to this piece of cotton? You'll find out in this space pod for March 23rd, 2016. This spacecraft launched at five minutes past three, coordinated universal time, and is designated Orbital ATK-6, or OA-6. Confusingly, Orbital ATK-5 is scheduled to launch after this one, around the end of May. This Cygnus spacecraft is carrying 3,513 kilograms of cargo. It will berth to the space station, where it will be unpacked and refilled with trash ready to deorbit and dispose of the trash by burning up in the atmosphere. In between unberthing and de-opening, however, a nifty experiment is going to be completed, and that's where Sapphire comes in. Sapphire, or spacecraft fire experiment one, will set fire to a piece of material roughly 100 centimetres by 40 centimetres long, creating the largest experimental fire ever in microgravity. This experiment will measure oxygen, carbon dioxide, pressure and temperature as the fire spreads, and will record the whole thing with two high-definition video cameras. Sapphire will be burning a blend of cotton and fiberglass. Fiberglass adds strength to the cotton and keeps it steady so that the cameras can get the best view of the burning. Pure cotton likes to move and crack while it burns, looking a little something like this. Sapphire allows us to conduct realistic fire experiments that are simply too risky to be performed on crewed vehicles like the International Space Station. The data we get from Sapphire will allow us to better understand how fires spread microgravity. This will lead to better procedures on how to respond to fires on board a spacecraft, making space flights safer for both our current and future astronauts. So I think it's fitting that this Cygnus spacecraft is called the SS Zurich Husband in memory of the space shuttle Columbia Commander who lost his life when Columbia disintegrated upon return to Earth. Thank you for watching this space pod and thank you to our Patreon patrons who make these space pods possible. These videos are 100% crowdfunded and would not be possible without the support of our founders of tomorrow, architects, engineers, ambassadors, dreamers and citizens of tomorrow. So thank you for your support. For more information on our crowdfunding and to look at our reward levels, head over to patreon.com slash space pod. For my next videos I'd like to cover some more of the experiments that the Cygnus spacecraft has delivered to the space station. So leave a comment below or let me know via social media if there's any experiments you want me to cover. And don't forget to subscribe to our YouTube channel at youtube.com slash T-M-R-O in case my next space pod is about the experiment that you've chosen. My name is Lisa Stodzianowski and until next time keep on discovering.


Hi, I'm Stefania and I'm going to show you a night in the life of an astronomer at Siding Spring Observatory. So tonight we are going to observe galaxies using the Hector instrument, which is an integral field spectroscopy instrument that allows us to grab data for galaxies in a specially resolved manner. It's quite a special place because you can do science while you're in contact with nature. So now we're going to go back inside and we're going to get ready for the night to do some calibration and take some data. So we are in the control room now and we have different machines that operate different parts of the telescope and the instrument. So all this side here is used by the night assistant to actually move the telescope, so to open for example the mirror, to open the shutter. We astronomers focus on these machines over here and we use this one to run Hector, so the instrument and the spectrographs related to the instrument. What we usually hope for is to have our plate ready in the afternoon and then observe seven times for half an hour and then we can go upstairs, change the plate and do everything again. This is a nice, smooth observing day like night. Okay, let's start. Okay, let's Hector. It's all tucked in like a baby. These are optical fibers and these are X-Mando. So we put one galaxy in each of these. So this is our four meter angle Australian telescope and we can do some really cool stuff like we can study the stellar content, the gas content of the galaxy. So we observed this plate last night, so we are now kind of unplugging everything because the plate is done. We'll start the night with a new configuration. Next, G4. G2. All the fibers, we kind of need to plug them by hand. Is it true? It's getting busy. This is a special place because many astronomers don't actually have that opportunity to do this kind of more hands-on job, like everything is kind of remote nowadays. Okay, four 10, not too bad. Is anyone in the dome? Okay, no one is in the dome, so we can turn the lights off. So this is going to take a while and we need to be ready at 5.30, that's when sunset is. So now we are loading the finds with the coordinates of the galaxy that we want to observe. So the telescope will know where to point. Please move the telescope at Zanit. We would like to take some dome flats, please. Hector is an instrument for a galaxy survey. So it's called the Hector Galaxy Survey where we're going to observe 15,000 galaxies over six years. So we usually observe for around 100 nights per year. So this is just one of them. Okay, so when this one finishes, so like in 25 minutes we go upstairs. Okay, I think we can go. It's just raining out. During the night we are on a tighter schedule because it's clear. So we want to go and sky and take our observations. It's cold in here. When you plug and unplug, it's when you don't feel the cold because you're more focused. We had a really smooth day, so we can take more time to do this. Okay, done. This is the galaxy that's within Examandel A using Hector. We can actually do spectroscopy of the galaxy, so we don't only have the photometry, but we can do this spectroscopy. You can see it here, right? The light and we can reconstruct some important properties like kinematics that you couldn't do using only photometry. Let's go to bed. As soon as, yeah, the last exposure is done will be, okay, let's go to bed. So it was a very successful night here at the AAT. We take beautiful data whether at the end was good. So, yeah, successful. Thank you for staying with me for a night in the life of an astronomer. And now I'm gonna go to bed. Gonna do it all over again tomorrow.


Okay, so we're nearing the point where we will be able to get the friction factor for turbulent flow and using the Darcy Weisbach equation we'll be able to figure out head loss for turbulent pipe flow. So we're looking at turbulent flow and what we're going to do we're going to use the velocity profile that Milliken came up with in 1937 and that was for the velocity profile in the overlap region. So this was the region where both laminar shear stress and turbulent shear stress were important and we need to do a transformation here of the coordinate system. Milliken was doing this for flat plate boundary layer channel flow but what we want to do is we want to transform the y-coordinate into a radial coordinate and if you think about at r equals capital R that would be so if this is our pipe and we have the pipe wall there that's the thickness so we have a little r here and out here this is a little r equals capital R and that would be the radius so looking at it in this manner if we have r minus r so on the outer wall so at r equals capital R we get y equals 0 so basically what we're doing is we're re-transforming that so that this is kind of like y coming in that way away from the pipe wall which is what it should be for pipe flow and the relationship that we will use is the one that Milliken proposed now one thing that I should say is even still to this day people are debating these profiles and the constants and the universality of the relationships that result people are arguing whether or not this relationship would apply to flat plate boundary layers or open channel flows or what the coefficients are that's all taking place because we're getting better and better data enabling us to be able to study this profile in more detail for the purposes of what we're doing here however we're not really going to worry about that because the main thing Matt Prandtl and Von Karman and everybody else who studied this were trying to do where they were trying to come up with a functional relationship with which to collapse experimental data and that's the approach we're going to take so we're going to use Milliken's profile this one here and with that we're able to calculate an average velocity I'm not going to go through the math I'll just show the final result of that the u star remember that's the friction velocity u star is tau w over rho square root okay so we have that and the constants that we're going to use here although they can be tweaked as I mentioned before we'll use these ones in which we once in Milliken originally had now with that what we can write is the average velocity divided by the friction velocity is approximately okay so that's where we get to what can we do with that well it turns out that we can actually do quite a bit and we're going to focus in on this term here and we're going to take the friction velocity from up here and we're going to rewrite that in terms of our friction factor which is what we're after the friction factor relationship came out of our early dimensional analysis of this problem a few lectures ago so rewriting the left hand side and here we have our wall shear stress this came out of the dimensional analysis and so with that we're able to continue on and rewrite this in terms of our friction factor F which is a good thing because that's what we're after so that means we have friction factor on the left hand side of the equation let's get it on the right and we'll be in business so taking a look at the right hand side we're going to work on the term that was in the natural logarithm now I'm going to play a little bit of trickery here I'm basically introducing a V and a V I should be a little careful this here is kinematic viscosity so don't get that confused with my V's hopefully or not that's a V that's a V I've introduced it into the numerator and denominator so that we can simplify this a little and what we're going to do we notice that the first term is one half of the Reynolds number based on diameter and then we have the friction factor which is what we're after and with that we can then rewrite Milliken's velocity relationship and we get something that looks like this and I've now transformed from a natural logarithm to a base 10 log so this profile is something that Ludwig Prentel came up with in 1935 and so he adjusted the constants to provide a better fit and that will be the next equation that I will write so Prentel further reduced that or recast it in the following form okay so we have this relationship here and this is good because we're getting something that has the friction factor in it has the Reynolds number it is a transcendental equation meaning that in order to solve it you have to iterate because the friction factor appears on both sides there was one other contribution that needed to be added however and that had to deal with the fact that not all pipes were smooth some pipes are rough and consequently we needed to take into account the pipe roughness effects that's what we're now going to do it's a bit of an adjustment to this equation so it turns out for turbulent pipe flow the pipe wall roughness has an impact on the friction factor and we can classify the surface roughness based on a ratio which has our friction velocity in it so in a way this is kind of like a Reynolds number when you think about it because we have a length scale and we have a velocity and we have the kinematic viscosity in the denominator but if that number is less than 5 that would be a hydraulically smooth pipe if the number ranges between 5 and 70 the number being our length scale times the friction velocity divided by the kinematic viscosity this is transitional and it results in moderate Reynolds number effects so what's happening here is as you increase Reynolds number it will have a moderate impact on our friction factor F and then finally if this number which is basically a Reynolds number for our roughness if it is greater than 70 actually let me write that over here if it is greater than 70 then we have what we refer to as being fully rough flow and here we can see the friction factor is independent of the Reynolds number so with that you can integrate that in and that basically modifies Prandtl's equation to include surface roughness and what we end up with is the famous cold brook white equation which is used for estimating the friction factor in turbulent pipe flow again it's a transcendental equation meaning that you need to iterate if you want to solve it and sometimes referred to as being the cold brook white equation very famous one in fluid mechanics now this equation you can either solve it directly by going through the trial and error iteration or you can go and look at plots of it and this is plotted in the moody diagram which we looked at earlier for laminar flow so what we're going to do we're going to go back and take a look at the moody diagram and we're going to look at what happens when you have the moody diagram and you add pipe data for increasing roughness of pipe so let's take a look at that now so what we have here is moody diagram with a laminar flow curve on it and then we have smooth pipe curve you can see it there and then we start putting curves with variable epsilon over d so surface roughness and we have the smallest surface roughnesses and then we have it increasing and as the surface roughness gets bigger and bigger or greater and greater the friction factor goes up which we would expect and then finally with very very rough pipes and and so that is the moody diagram that's a diagram that we use quite extensively within mechanical engineering in order to give us the friction factor and estimating the pressure drop within pipe flow or you could estimate other things how you could be doing sizing and determining the diameter or you could be determining the volumetric flow rate going through a given pipe flow configuration so with that we're almost at the end we come back to the Darcy Weisbach equation enabling us to come up with an expression for the turbulent head loss which is what we would use in the energy equation so if you remember back we looked at the Darcy Weisbach equation and all we do here is we put in the value of the friction factor for turbulent flow that we either get from the moody diagram or from the Colbrook white equation and we get that now there is one final relationship that I want to leave you with and this is an approximation for the friction factor that does not require the iteration due to the transcendental nature of the equation and so this is an approximation and so with this equation and you'll notice there is no friction factor on the right-hand side of the equation this would give you a starting point perhaps by which you could estimate the friction factor and then after you get it from here you could go to the Colbrook white and then you'll be closer to being converged but anyways that's another relationship that you can get the friction factor for turbulent pipe flow from so that concludes friction factor we've looked at friction factor for laminar pipe flow and we looked at it for turbulent pipe flow we came up with different relationships Colbrook white the analytic expression for laminar flow and finally the moody diagram those are all things that you can use when you're solving pipe flow problems in engineering


I'm I'm delighted to welcome you to the to the last session in the organ transplant seminar for the winter quarter but I do want to remind you that we have a fairly complete spring quarter coming up and that there's one addition at the beginning of the spring quarter. Currently the spring quarter starts on Wednesday April 17 but in fact we're gonna start a week early on on Wednesday April 10. We have a visitor from Israel Dr. Sperling who's gonna talk about organ transplantation in Israel which has been a sort of a controversial area because some of the buying and selling of organs have worked through Israel. I don't know that that Dr. Sperling is going to specifically address that but I hope that that will be part of the conversation. So that's April 10 and it's not on the schedule. So we'll meet here at the usual time. Today today I am today I'm delighted to welcome Mark Russo back to the University of Chicago. Mark prepared a paper organ allocation and lung transplant with David Meltzer and Robert Gibbons and Mark will be delivering the paper. Mark is now a cardiovascular surgeon who specializes in complex and re-operative aortic valve surgery at the Newark Beth Israel Medical Center in New Jersey. Mark received his MD from Albert Einstein, a master's degree from Dartmouth and completed his residency in fellowship at Columbia University. Mark Russo is an experienced heart and lung transplant surgeon who's participated in more than 400 organ transplants. He's authored 130 published manuscripts and textbook chapters focused on improving healthcare quality and clinical outcomes for patients with cardiovascular disease. It's a it's a delight to welcome Mark back and I'm looking forward to this wonderful paper. Mark, thank you. I'd like to thank you for the opportunity to present this work. I almost didn't make it my flight actually got canceled and I was able to jump on another airline so but it's yeah it wasn't because of weather it was it sounds like it was a plane that I didn't want to be on but again I'd like to thank everyone so this is some work that I've done with with David Meltzer and Robert Gibbons. I'm gonna go through a variety of things related to the allocation of organs in lung transplantation specifically focused on the issue of medical urgency and geography and how those impact the allocation of organs. So disclosures I don't have any conflicts. Our research has been funded by the thoracic surgery foundation for research and education and also by a startup grant from this CTSA ITM core subsidy and I'd also like to thank Quan-Hur, Liz Johnson and Orly Merleau for their help in developing the manuscript related to to this work. So briefly lung transplantation is a therapy for patients with with end-stage lung disease. These are people with cystic fibrosis, IPF, sarcoidosis, pulmonary hypertension and COPD. It's long transplantation has been shown to improve survival and quality of life and appropriately selected patients. However there is an issue which is true in basically all areas of transplantation where there's a critical scarcity of organs available for transplantation. That is demand is greater than supply. There's less than 2,000 lung transplants performed in the U.S. annually and there's somewhere around four to five hundred patients that die every year while waiting. I'm gonna assert that given the critical scarcity of organs the primary objective of organ allocation should be to provide maximal net benefit to society from the available organs. So how are organs allocated? First organs are matched based on ABO blood type, size, height and weight, whether the patient needs right left or bilateral transplantation and then this issue of medical urgency and geography. So for a minute I'm going to focus on what medical urgency means and and and how geography impacts organ matching and allocation. So first medical urgency. Before 2000 before before 2000 May 2005 priority for lung transplantation was based on waiting time. That is the longer the patients were on the waiting list the higher their priority. As you can imagine that might create a selection bias. Critically ill patients who are rapidly decompensating were unlikely to survive to transplant and stable patients who are well enough to wait were the ones who actually got the organs. Again you can imagine that that could create a number of issues in terms of how the organs are allocated. In 2005 the lung allocation score was implemented. So this was implemented for a few reasons. First to reduce the number of deaths on the lung transplant waiting list to increase transplant benefit for lung recipients and to ensure an equitable and efficient allocation of organs to active transplant candidates based on medical urgency rather than on waiting time. So medical urgency as defined by the LAS score was it was developed based on a multivariate regression model. First to predict the risk of dying during the following year on the waiting list versus the likelihood of surviving during the following year after transplant. The risk of dying is weighted more heavily than than the likelihood of or then than survival. I'm not going to spend a lot of time on this but it's something to keep in mind potentially for discussion later on. So the scores can range from zero to 100. Rarely do patients have a score less than 30 but score between 30 and 50 to put this in perspective for people who don't see lung transplant patients. A score of 30 to 50 weightless survival can be measured in months to years. From 50 to 74 survival can be measured in weeks to months and greater than 75 it's usually days to weeks. So what are the factors that impact LAS? So waiting which urgency parameters include age, oxygen requirements, body mass index, diabetes, six minute walk test, functional status and then some measures of pulmonary function. Post transplant survival is based on age, need for mechanical ventilation, functional status, what their diagnosis is and again some pulmonary functional status. It's worth pointing out also that the way the score was constructed it can be changed over time based on what a regression what the model that they used to develop the score predicts but this was the initial what the initial score those were the important factors in the initial score. So now on to geography. Under the current system all patients are prioritized by LAS but before LAS or patients our organs are allocated first based on geography regardless of LAS. The primary unit of organ allocation is the local geographic unit known as the donor service area and the U.S. and Puerto Rico there's 58 of these this is what the map looks like. So initially again organs are offered to a subset of appropriately matched patients based on blood type and size within the donor's DSA so within one of those 58 areas. Therefore it's possible that a less ill candidate within the DSA could receive an organ over a more severely ill candidate outside of that DSA and further it's possible that you know you can imagine based on those borders which are somewhat arbitrary and how they're drawn that a less ill candidate may actually be located further from the donor hospital than the more severe candidate but it's determined based on again geography and borders. So if there's no appropriate candidates available in the local DSA then the organ can be allocated to the next largest geographic unit which is the region there's 11 regions this is what the regions look like. So I'm going to present a real life scenario. A 27 year old sister fibrosis patient was hospitalized in the ICU while waiting for a transplant. His LAS score was 91 that gave him one of the highest LAS scores in the country and again that suggests that his survival is measured in days to weeks. An appropriately matched organ was available 20 miles away from where the patient was hospitalized but it was outside of his DSA area. So this is what the waiting list looked like. The yellow line that's our 27 year old he's number five for priority. If you look at if you look at the LAS here this patient has a score of 91. Patients who are ranked higher in priority have scores of 40 and 42. Again those are patients whose survival probably can be measured in months to years. These are patients of particular interest actually a patient of 31. The reason I highlighted 2 and 3 is because 1 and 4 the patients those were the organ was declined for reasons that that I don't know but if you expand the list further you can see that the area in yellow those are all patients with higher LAS scores than the two patients who are in the positions 2 and 3. So that's just on one page. There's actually I think it turned out that there was 27 people who had a higher LAS score in the region than the patients the local patients who were ranked 2 and 3. So what happened? So again just to reiterate this currently lungs are offered first to the subset of patients in the local DSA and it only is offered out regionally if there's no acceptable candidate. And this so in this case number two on the list was a 69 year old which creates we can have an entire discussion about the ethics of transplanted 69 year olds but a 69 year old local candidate with an LAS score of 40. Number three was a 71 year old with a LAS score of 31. Number five was our 27 year old regional candidate with an LAS in 91 located 20 miles away from the donor hospital but outside of that specific DSA area. So given the critical illness of the 27 of the 27 year old regional candidate request was made for compassionate release of the organs that is the transplanting center for that candidate went to the other center and said would you release would you please not take this organ so that we can give it to our 27 year old that request was denied. The organ went to the local one of the local candidates I don't know if it was the 69 year old with the LAS of 40 or 71 year old with an LAS of 31. Three days later the 27 year old candidate was placed on ECMO five days later he died. So there is a couple things that clearly I'm placing value on people's lives saying that I don't think that I was unclear and my suggestion that the 27 year old probably has greater benefit from the transplant than the 69 and 71 year old with lower LAS scores but maybe that's not true. So first do patients with higher LAS scores receive a greater benefit from lung transplantation. So this is a study that we did with looking at the UNIS database which is a national database for lung transplantation. The study population include all lung transplant candidates greater than 12 years old between May 2004 which is when LAS was implemented in May 2005 I'm sorry May 4th 2005 through May 4th 2009 there were 6000 people approximately in that pool and follow up data was available through November. So we stratified the transplant candidates based on their LAS at the time of listing into three categories LAS less than 50, 50 to 74 and greater than 75. So basically what we did was try to calculate the net benefit of net survival benefit of transplantation by calculating what the actuarial survival for a patient was based on given LAS score if they remained on the list versus what their anticipated survival was or the anticipated graph survival if they were transplanted. So waiting list outcomes include patients who are live on the waiting list patients who are transplanted dead on the waiting list are lost to follow up. So the actuarial wait list survival candidates were followed from the date of listing to death which was the outcome of interest or if they were transplanted they were censored or lost to follow up they were censored. Post transplant actuarial graph survival was calculated recipients were followed from the data transplant to graft loss which was the outcome of interest. If they were lost to follow if they were censored it's worth noting that almost none of these patients are lost to follow up because if they're lost to follow up they're usually dead. So the net benefit by LAS so if you look at the low what we call the low priority stratum which is LAS less than 50 you can see that the waiting list survival and post transplant survival basically overlap. Suggest the way I would interpret this is the area between these two curves is the net benefit. Basically there's no net benefit and if you further stratify this by LAS ranges of 10 points actually patients with LAS less than 40 the not the waiting list survival is is superior to the post transplant survival suggesting patients actually have a negative net benefit. You can see in the intermediate term or in the immediate priority stratum LAS between 50 and 74 there's a pretty good distance between this is this is the post transplant survival curve this is the waiting list survival there's a pretty significant benefit here same thing with the high priority score. So what do we conclude from that we conclude that recipients less with LAS less than 50 receive little or no net benefit survival benefit from transplantation and it's as I mentioned if you further stratify those patients with LAS less than 40 may actually do worse with transplant than without recipients and that's actually something to keep in mind when we get into who actually gets the organs. So recipients with LAS less than 50 appear to receive a net survival benefit from transplantation. So the next question is who actually gets the organs. This is a breakdown of how the organs were allocated based on LAS you can see that 60 percent of the patients who are transplanted have an LAS less than 40 and another 23 percent have an LAS between 40 and 50 so basically more than two-thirds of the patients who are transplanted have an LAS less than 50 which you know based on the survival curve suggests that we may be getting marginal benefit. There is issues of quality of life which we can talk about later it might not be all about survival but nevertheless there's a significant minority of patients who get transplanted with an LAS greater than 50. So looking at this differently LAS score less than 50 most of the patients with an LAS score less than 50 are receiving lungs locally and most of the patients with LAS scores greater than 75 are receiving lungs regionally or nationally. So that means that the patient the organs actually have to be offered out into a greater area for the higher priority patients to receive the organs. So another study that we did basically using the same data set we looked at the distribution of patients who are listed for transplant and the patients who are transplanted by LAS and you do see in fairness that most of the so this is all the patients who are listed with an LAS less than 40 these are the ones who are actually transplanted so low priority candidates those are patients who have an LAS less than 40 and between 40 and 49 comprise the vast majority of both candidates so people who are listed about 88 percent and recipients almost 90 percent. So subsequently we looked at what the natural history of survival on the waiting list is so and the reason that this is important is okay so most of the patients who are listed have lower scores and most of the patients who are transplanted have lower scores and that's probably not a problem if people with higher priority scores aren't dying on the waiting list but but we asked the question essentially is where they dying so again same data set so this is this is a curve looking at patients who are a lot live on the waiting list percentage of patients alive on the waiting list over time so the alternative is either that they were that they died or they were transplanted so these are patients still waiting so if you look at most of the most of the outcomes determined in the first 60 days you see that very few of the patients with a score of greater than 90 are still on the waiting list even at 60 days and almost none I think great so patients who have a score greater than 40 less less than 6% that's not right that's not right that should say greater than 90 less than 6% are still alive on the waiting list at one year but of the patients who are so who was actually transplanted you see that again focusing on the 60-day mark because that's where a lot of this with the curves of the steepest you can see that at one or actually focus on one year at one year the highest number of patients transplanted was again patients in the lowest LAS group so 40 basically less than 660 and the lowest was was the higher priority candidate so the what we're really interested in who's dying you can see that at 60 days 40% of the patients with an LAS greater than 90 were dead and that it's a step wise decrease as you go across LAS so again there's a fair number of patients who are dying with higher LAS scores where we're allocating organs to lower LAS patients so based on this we asked the final question that I'll present here is the allocation local allocation of lung donors resulting in lower rates of transplantation among higher priority lung transplant candidates so that is is it actually the way organ the question that we're trying to get at is it actually the way organs are allocated or there's some other and is that is it related to geography or are there other factors that we aren't understanding here so to determine that and this is a very simple study in preparation for a more sophisticated analysis of this was to determine the frequency with which when a patient with a blood group and size when a blood group size match candidate with a higher LAS score in the same region existed when organs were allocated to a local candidate so that is you have a donor they match based on ABO blood type and size was that organ allocated to somebody with a lower LAS score locally when a higher when a more urgent patient existed regionally so use we use the UNOS data we got special permission from UNOS to we actually got daily LAS scores for each candidate yes yes so we got a special data feed from UNOS where they provided LAS score for each candidate every day I'm sorry it would have been offered that's fair so this basic so what we were able to find was there was five so we looked at 2009 data we we limited the analysis to double lung patients and that's because it becomes too complicated to look at right versus left versus double so there was 580 double lung transplants that occurred in 2009 the data that UNOS provided us actually had five thousand I'm sorry five million observations because again it's LAS for each day so it was the day and the LAS score that was basically observation it's not patients so so we try that we divide an event as again when an appropriate ABO matched height matched double lung candidate existed in the same region as as a donor but the organs were allocated to a local candidate with lower LAS is that more clearly stated because I know that I stumbled over it for the first time so the primary outcome measure was was what we called the event so any event in the secondary outcome measures basically we looked at the LAS point differential so that is what was the LAS score of the local candidate who actually received the organ versus the regional candidate who did not receive the organ and then we looked at the number of transplants that were impacted and most importantly the number of people who subsequently died after they were bypassed for an organ so Delta LAS again is the differential between the patient the local patient who was transplanted in the regional patient who was bypassed so so any difference there was this occurred about 3400 times a difference of greater than 10 it occurred about 800 times a difference of greater than 25 it occurred 250 times and a difference of greater than 50 occurred 63 times so that means for each transplant so each one of those 580 transplants there was an average of six regional patients who had a higher LAS score so greater urgency than the patient who was actually transplanted there was about one and a half with a 10 point differential there was half on average half a patient for a 25 point differential and about one in 10 transplants had had a differential greater than 50 points so how many transplants to this impact so no point of for any point differential it was 480 so that that equates to about 83% of all transplants there was somebody who had higher urgency who was not transplanted the differential of greater than 10 points it was about 55% of the transplants 20 25 point differential was about a third of the transplants in a greater than 50 point differential was about 8% of the transplants so going so this is what I would use the most the most important even though it wasn't the primary measure I think this is the really you know this is sort of the upshot so how many of those patients subsequently went on to die so any point differential 15% of the patients went on to die if there was a greater than a 10 point differential 27% of those patients went on to die if there was if there was without being transplanted if there was a greater than 25 point differential then about one third went on to die and if there was greater than 50 point differential about half of those patients subsequently died and I don't for this and there's two simplifying assumptions here and I'd point out one of them that to irrespective of your question one problem is we don't know why the patients weren't matched so it's possible that it's possible that those patients would not have been transplanted anyway so these are these are sort of an overestimate but I'll also so to answer your question in terms of what someone have died subsequently it's interesting if you look at and I'm not going to present this data but if you look at patients who most of the patients who are transplanted had scores lower than than certainly lower than 50 and even lower than 40 when I say most I mean like 90% and so then the question becomes well would those patients have progressed in their disease process and then subsequently died and the answer appears to be no that most patients LAS score doesn't change significantly over time whatever they're listed at that tends to be what it is and that's not to say that's true for all patients but I have data which I don't have readily available but that shows that that most patients score stays within about a five point range so they stay within the stratum that they're in very few patients flip in and out one other thing I will point out though is this sort of so there's a hundred and eighty five patients who I'm basically claiming died because they were bypassed there's you could make a counter argument that that's actually an underestimate because we're only talking about 580 double lung transplants we're not talking about the other 1500 single lungs and there's also a question of people who get double lungs there's clearly some diseases where the patients need double lungs but there's other diseases where they may not and we could get greater benefit if we split those lungs and gave them to two different different recipients all very complicated questions to address here so in conclusion my conclusion is that the current allocation system results in a high frequency of organs allocated to lower priority candidates even when an appropriately matched higher priority candidate exists regionally so based on the data I presented I would I would summarize this by saying that most donor lungs are allocated to low priority candidates that is patients with an LAS score of less than 50 low priority candidates appear to receive little or no net survival benefit whether they have an improvement in quality life I can't estimate based on the data available to me but the third point is that high priority candidates continue to die at reasonably high rates on the waiting list while this is occurring so what's the current state of affairs and transplantation if you go back almost 15 years the Institute of Medicine panel concluded that broader organ sharing or broader sharing of organs led to an overall increase in the rate at which the most severely ill patients were transplanted and a concomitant decrease in excess transplantation of less severely ill patients without increasing pre-transplant mortality so this is a group that David and Robert sat on I think that they were the first two authors on this IOM paper the their analysis focused on liver transplant but the questions are the same in liver transplant the the allocation is slightly different than in lungs going on to 2000 after this report was issued the Department of Health and Human Services issued what the infamous final rule which was intended to assure that the allocation of scarce organs will be based on common medical criteria not accidents of geography in 2005 the OPTN long allocation subcommittee recommended changes to the long allocation policies to minimize the effects of geography on waitlist outcome in an effort to reduce mortality despite the above organ lung organ allocation remains a locally based system so opposing points of view following the announcement of the final rule there was a bitter debate in the transplant community about what should happen concerns were expressed that implementation of the final rule would increase the cost of transplant so that is by distributing organs by a wider geography would require we would incur greater costs force the closure of small transplant centers adversely affect access to transplantation for minorities and low income patients and discourage organ donation and result in fewer saved lives based on the IOM report there's no evidence to support any of these things and 15 years later there's really no evidence to support any of these things and I would also argue that transplantation is so expensive that adding a few thousand dollars for a flight to to procure an organ if the organ goes to somebody who gets a significant survival benefit versus somebody who gets minimal benefit is probably pays for itself although I can't prove that right now so what future studies are we doing so basically what we're interested in this is really the upshot of this and unfortunately we don't have final data to present but using the same analysis that was used for the IOM study which was a mixed effects regression model we want to test our central hypothesis that organ sharing over broader geography would result in in better organ allocation as measured by higher rates of allocation to higher priority patients and prune survival on the waiting list among long candidates and increase net benefit of long transplantation based on this data I'll just throw out a few questions which represent largely limitations of the data that I have first all this is based on the fact that we are you know how to how do we define priority and that the LAS actually appropriately defines it which I actually think it's a pretty it's it's at least a step in the right direction it's a pretty significant advance actually my opinion but what would be what should be the guiding principle in organ allocation one of the first things I said that the guiding principle should be that we get net we get maximum net benefit from from a societal perspective from all the organs that are available there's people who would argue that that is not necessary that that shouldn't necessarily be the guiding principle is survival the most appropriate outcome measure so one of the things that I said repeatedly was that in the lower priority groups survival is not improved but it may be the quality of life is improved so if patients are on home oxygen and transplant allows them not to be on oxygen or if they can't get out of a chair and and now they can have some quality of life even if it doesn't improve their survival than that those those quality of life measures are important unfortunately there are no good quality of life measures in this population which is definitely a shortcoming for the field and I think that's something that that should be addressed but should we be transplanting patients of with LAS is less than 50 if we assume that that survival is the most important outcome then maybe we shouldn't but but again there's probably other markers that that need to be assessed before we draw any firm conclusions about that and finally do we really have a shortage of lungs relative to needy candidates based on the data that you know based on the studies that I've done I could actually make a pretty strong argument I think that it's not that we have a shortage we're just transplanting the wrong people and that's why high priority patients are dying I won't present my solution but I'll stop there thanks for your talk I'm actually a supporter of regional sharing of organs but when I listen to you talk I'm actually struck by it seems like the solution for this is maybe geographic sharing of organs but it may also be sort of more policing of the list because it seems I mean if it's true that people who once they're on the list don't really have a change in their score then some people are being listed too late and other people are being listed probably too early and I don't know if the geographic sharing would alleviate that or if there could be some additional list management then yeah well I mean there's people who are certainly more expert in this topic and you know if you want to weigh in at any point then please do but there's clearly access to care issues and also the natural history of the disease isn't necessarily such that people aren't a gradual decline where they you know could be picked up at any point along the way they patients may have a sudden decline and then plateau or they may just have a sudden decline and never make it to the list or they may have that gradual decline and that's probably also a function of their disease process I'm talking about these patients like they're all the same and one of the limitations is that COPD is not the same as IPF which is not the same as pulmonary hypertension and the problem that we have is that we don't have enough patients in each one of those subgroups to make generalizations about that specific subgroup so I'm again bunching them all together as if they're all the same and they are clearly not the scores may be comparable but the disease states are not as Marcus stated and the progression of the disease state in each individual disease is very very different IPF patients tending to progress more rapidly but they all start at the same level I don't have any contention with what you've said at all it was my contention as part of the group that actually form the LAS that we should be doing geography and that was that was the third rail of discussion and I think that the discussion today should be centered on not so much the regions as on concentric circles yeah so if I went on that I would actually yeah two things about that in fairness to the group so you know everybody if you just hear this for the first time you think well you know this committee had the opportunity to fix this these laws are governed by states so there are governors who have said we are not going to allow our organs to go outside of our state boundaries until our citizens have the opportunity to have a shot at them essentially and so this is way beyond this is this is a much larger issue and this is issue that Donna Shalala attempted to address 15 years ago and still remains a pretty significant point of contention because you raised the point I would and again there's people who know more about this than I do but my understanding of organ allocation in hearts is that patients are first organs are first offered to their status one a one b and status two the first offered locally and then if there's no local status one a then it's offered to a 500 mile radius and then if there's no one a then it's offered to 500 mile radius of status B and and so on and so again the idea is concentric circles rather than boundaries you could do the same thing along transplant you could stratify patients by LAS you know I keep my by custom I've been I've been stratifying by groups of 25 so you could do the same thing LAS greater than 50 you go locally I'm sorry greater than 75 you go locally than a 500 mile radius and so forth again the problem becomes that their state laws that prevent this from happening it can't be concentric circles because people have actually actually initiated laws that would prevent that the only thing that I would add to your concerns is the cutoff at 50 for transplant just because for discussion sake 40 makes more sense because 40 includes the 80 percent of the highest scores or 20 percent of the highest scores rather and so it's a skewed it's a skewed distribution it's not an even or a Gaussian distribution from 0 to 100 it's very skewed and again to make this point because I don't want to walk out of here and think that we're trying you know 80 percent of the people that were transplanting aren't getting any benefit again one of the problems here is that a patient with an LAS score 40 who has pulmonary hypertension might be different than a patient with COPD who has an LAS score 40 or a patient with IPF who has an LAS score 40 but again the numbers aren't large enough at this point to be able to make any conclusions based on those specific subgroups so so that piece of it I don't want anyone to walk out of here thinking we you know we only need to do 20% of the transplants that we're doing may not be suitable for a patient who would be at higher score so you know that doesn't you know your analysis does not include why the organ was turned down for that particular high score patient because if you transplant that organ on that high risk patient probably the patient won't benefit because the patient may not survive because the organ is not really going to be able to support that patient so that can be used better in a lower score patient because you will have a survival so that is very important one of the take-home messages is there is lung donors and there are lung donors which are not equal in all terms so you got to know there may be there may be other things which are going not real more kind of a judgmental in selecting the matching the donor for the recipient the second thing I think you did already talk about what is the geographical region is approximately or is it the borders that's the issue I think and again we can move forward with the question it's gonna let me see if I can pull up I mean please keep asking questions but again to look at the geography and this is what the regions look like so again it's sort of somewhat arbitrary of how the regions are drawn up it's clearly not concentric no there is so once the organ gets there's in liver yeah so it's local that is closest to the donor hospital within a service area so all of the purple part of northern Illinois here would be one donor service area this little thing up here is considered Wisconsin that's rockford and which is another one of the things that's interesting you know it's interesting that I chose this topic to explore because the center I'm at now we're actually the second largest heart transplant program in the country and the reason that is is because there's essentially nobody else doing heart transplant in the state of New Jersey so if an organ comes up we have to turn it down before anybody else gets shot at it and so I trained at Columbia which is in Manhattan and not far from where I work right now and they've actually transferred patients to us because the waiting time is shorter and so I frequently take the opportunity to make fun of them for that when we transplant the patient within a few days when their patient would have waited months but it's but it's not I mean but it's an also an unfortunate you know it's unfortunate that we it's fortunate for us we get to benefit from that but it's an unfortunate state of affairs it's not it's not good policy Ross well New York well New York has separate has to there's New York City and everything else first of all it's really it's good to have you back you want to stay second your scenario that you presented at the beginning is pretty interesting not just because figuring out who's sick is not sick but that another center wouldn't say yes to having a really sick kid transplant you have no idea how interesting that is and I I don't want to say what the details of that scenario are but it involves me the center it didn't happen it happened somewhere else but but people it's people who who we know so I think the new each other right in the bad part about that is so you can complain about the inefficiency of this system but there was a very obvious scenario where somebody clearly would have benefited from being transplanted shouldn't even be an issue when you get contacted about it and they don't get transplanted that's a that's a bad problem yeah I mean but those are the those are the rules and people will exploit the rules I mean again just to be clear it's no one in this room but but it's it's people we know and the people knew each other and they should have been able to come to a reasonable resolution here and it's particularly interesting because you know we can have a debate about whether people who are 70 years old should even be transplanted the the median survival post transplant for patients in their 70s is less than two and a half years the 27 year old cystic fibrosis patient he probably has the best opportunity for long-term survival of any of our candidates so there's a huge inequity in terms of what the potential benefit to that patient was and what the I don't know what the outcome of that patient although I could probably find out what the realized benefit from that specific organ was it's it's a really interesting scenario and but it that happens all the time could you could you go back and talk a little bit more about the two notions that you had in one of the earliest slides you know what does priority mean it seems to me that that's related to the one that followed what principle are we following so would you explain in general what that question well priority means and then what your own priority so that's what's confusing about this with the way alias is presented we would say the guiding principle is alias score and that's how we allocate organs but clearly what Trump's that is is geography which is why we keep going back to geography so priority is determined again based on what you're into and at any point because you are on the on the committee that did this and so you're much more expert in the sign but the alias score was developed based on a multivariate regression analysis of the UNOS database to determine factors that were predicted of both survival on the waiting list in the absence of transplant and survival post transplant so in an attempt to determine who would get the best the greatest net benefit that's what that score represents now one of the because one of the issues here is that you would think well the sick patients you might think quickly the sickest patients are gonna get the greatest benefit but they might be so sick that they will have a poor outcome post transplant or you know in the absence of or with transplant so that's one of the issues and so I'm sorry I mean to so one of the issues is alias would suggest that the patients with a score of 90 have the highest benefit but again their survival in the absence of transplant is weighted twice what their post transplant expected post transplant survival is that might not be the right ratio and so if you go back to my analysis where I showed you the areas between the two curves and suggested that well maybe it's you know who is getting the greatest net benefit by my analysis is actually patients with this l.a. score of about 60 or 70 who get the greatest net benefit because the patients with 80 and 90 are so far you are so severely ill that many of them just die immediately after transplant so they never achieved that benefit whereas the patients on the lower end of the spectrum many of them would live for years in the absence of transplant are you saying then that the priority means the number of survival years it's a predicted number of so percentage of survival of the coming year was how it's so it's coming year one year that's how it's calculated if we looked at it at one two and three years of survival and they were approximately the same but more controllable so that's why we chose it. Yeah well the problem is that I mean each one of these steps so first of all we're talking about one year survival clearly when you do a lung transplant it's a $250,000 operation we're hoping to get more than one year of survival out of the patients there's there's a number of limitations is it absolute survival or is it qualities or quality of life or some other measure there's so many it's very difficult and the other problem with one of the problems with lung transplant is you know and again there's people who are more expert on me who can comment on this I would say that the current era of lung transplant didn't occur until until the early 2000s so we don't have a lot of long-term follow we don't have huge numbers of patients to look at liver transplant kidneys they have huge numbers of patients that they can look at it's difficult in this area because there's a smaller number of organs that are available if you're asking me what my opinion is the guiding principle should be that the organ should be allocated based on the greatest net benefit to society do I know how to make that happen I don't I mean I think it would be having long-term survive being having having good data looking at longer-term survival say five-year three or five-year survival and determining it based on again the net benefit of transplant versus not transplant but again it's that's much easier to say than it is to really hash out thank you thank you for such a intriguing and you know talk but you know one of the so the other side of the equation you kind of just discuss this briefly is the whole outcome after transplant and you know we know that patients that have the higher LAS scores actually do poorly after transplant and as a lung transplant in the lung transplant community we kind of wonder what is the best gauge of when we should say no to a patient who's too sick and so you're you know your study argues you know a score between 50 to 70 but is that you know the question is is that you know that that would be another another potential issue to address well and again when you think about the complexity of that because now we're assuming that LAS meet so we took a multivariate regression that's supposed to predict net benefit and then we're going and determine and trying to assess what the actual net benefit is within that so the LAS has no I mean it has no innate meaning it's a it's a number so we're sort of taking the net benefit of the net benefit in a sense which makes no sense I mean I no sense but I think the real issue is again trying to come up with a regression model first so what are the so what's the outcome measure what's the balance between survival in the absence of transplant and post-transplant survival and then what and then determining what the net benefit from that is so there's there's issues with how the scores calculated making sure that you have the appropriate data to even do that which I'm not sure that we do because again we have to focus on one year because that's where we have enough patients to really be able to look at one year survival if we looked at three-year survival then you know for for we would basically eliminate you know three or four years of data so that would give us six or seven thousand fewer patients in our sample it's it's a complex issue there I don't think there's any and that's why I say I think the LAS is a great step board it's clearly not perfect to the credit of the people that developed it they it's sort of a living equation and that it can be it should be reassessed over time and and further improved and that may mean that the balance between post-transplant and weighting less survival is adjusted the factors in the model are are changed over time as we as we get more predictive models the model you know for anyone who's just a statistician here the the area under the curve for a model in predicting lung transplant survival at one year is probably about 0.65 and that's whether you know you can you can just use regression analysis or you could use more complex computational systems and it still comes out to about 0.65 which is not a great model quite honestly. I'm sorry let me say one thing and the other thing that we have to assume is that patients who aren't transplanted that's them being censored is a is not does not bias us I mean patients may not be transplanted may be delisted and presumably those patients die although I don't know that for sure but we count them as censored rather than as a death on the weighting list and that certainly could skew our weighting our estimates of weighting less survival. So I realize you were trying to avoid this in some ways but but in your scenario you you you pose this this the fascinating issue of this compassionate release right which from the description it it was intended to to be that safety valve in some ways right on the scenarios that you are pointing to that are limiting um transplants to occur right and it and it should work so it seems to me there are two other factors in this it's not just geography right you you pointed to politics state politics which I think is a very real issue and secondarily it seems like there's a cultural issue in terms of relationships between transplant centers that's important so my question with regard to that is are there scenarios where the compassionate release actually works? Oh sure sure absolutely okay I can tell you scenarios where patients had you know without going into detail positive cross matches multiple positive cross matches which means that they there's very few organs that they could accept one comes up it was destined for somebody else and a call goes and people would release that organ this is not representative this should not be viewed this is a an absolutely extreme example that I happen to know know of because I know the people involved so then the question becomes well who should police this in some level it was the experts policing themselves and even they couldn't come to a reasonable resolution as far as I'm concerned and these are people who are experienced transplant surgeons who know you know who know what the progression here is there should have been no doubt what was gonna what was a likely what was a likely scenario you're pointing to another research direction that you know complements in some ways what you're in you know attempting to do here which is to look at how it is that those transplant centers where compassionate release works judge values it's on it's on a one by one basis so you're you're at the whim of the you know there's there's one transplant team that has the upper hand and you're at their whim but but I I imagine that those providers when they're in communication in centers that work well with one another right that they have certain values and beliefs that they cling to as being important in addition to the medical criteria that you're outlining right and that centers where that relationship doesn't exist so that maybe there's a secondary avenue to be pursued here which which would complement what you're attempting to do with with geography mathematics yeah I mean what you could have a safety network if there's clear inequity but then someone has to decide on what that safety net is which is probably just as complicated as solving the issue from the beginning you know and again these are people that knew each other they knew you know they were fully educated in what the likely progression was you know there is expert at this you know I don't know who else you bump it up to because these people had a relationship they were 20 miles apart they knew each other well and you know these are the sorts of things that happen I mean if you want to talk about the ethics of transplantation and medical care in general people have perverse incentives and they're not they're I mean although I maybe shouldn't say this while I'm being recorded not all physicians have the their patient is not necessarily their number one priority people get paid to do operations they have certain incentives to achieve volume measures they think that you know I'm sure that the I don't know this but I'm sure that the conversation went well that guy has such a high score that something's going to come along for him and he'll be fine well he wasn't and then the issues become well the organ should be located which should be allocated locally and there's some benefit to that but again one there's arbitrary lines drawn there and and as far as how things are are allocated people would argue that you know there's a system in place and that's how the system works and they were just following the rules all things that I don't necessarily agree with and I don't think that you do either but but somebody somebody thought that was appropriate although very although relatively and frequently patients with la scores of less than 40 less than 10% dying a year they do that and essentially what you're arguing is that you have to be the advocate for your patient you you are your patient's advocate and that's what this person may claim that they were that they were doing yes I want to follow up on your last point that probably most of the speakers have talked a little bit about the perverse incentives although none as as directly as you have and I think for those of us who aren't working in transplant I'm wondering to the degree that you feel comfortable if you can talk a little bit more about what you think the prime drivers are you mentioned for example like small transplant centers your survival or states having an interest what do you think really are the prime drivers and as well as under the current system which regions benefit which regions are the ones that they're losing well my institution benefits in a huge way like I said because we're I mean there's another transplant center but we do eight transplants to every one that they do I mean money is a huge incentive you know a transplant pays about $250,000 to the institution and that's sort of on average so in metropolitan areas it could be more than that that's a lot of money and if you want to put that in perspective a cabbage probably pays about $30,000 so you have to do you have to do five six seven cabbages for every every transplant you do money is a huge I didn't say this but someone recently said to me that I think this is an overstatement but that's their view that transplant is essentially a license to print money the patients keep coming back they need biopsies they need immune suppression management relative to all of the things it um it is uh you know it makes a lot of money and so valued program it's very prestigious centers want to say that that's why there's a whole bunch of centers out there that do four and five transplants a year because you know there's institutions want to say that they do transplant and they have no intention of growing that volume but they just want to say that they do transplant or trans catheter valves or you know whatever the high you know robotic surgery whatever sort of the high end marketable things are no one ever asked well how many most people don't ask how many do you do they just know that they do it and that's that's good enough so yeah so there's volume issues you have to do a certain number to be uh to be cms certified uh i think it's what is it 12 or 15 that you probably need to 10 um so i can tell you that this is a center that wasn't necessarily meeting their numbers every year and they needed to do that i mean that one transplant difference could be it could be the difference between their transplant program surviving and not surviving um and yeah so outcomes i mean it's a low las patient even though they have advanced age you would expect them to do reasonably well in the short term there's a number of of competing incentives and and they shouldn't be over i mean they can't be overlooked can't be factored in in the context of this but but particularly in transplant um prestige and and money have a lot of i think impact on the decisions that people make that all being said i do think that the overall driving force is actually for patient k yeah i'm sorry i uh and no but let me say now but i i think i think that i think that you know transplant physicians are among or maybe the most committed of all i mean you know it's really it's there's a life i mean it's it's a life commitment it's not just sort of a job that you show up for um so i don't want to say that but but there are these other issues when we were setting up the las there was a lot of discussion about how we would destroy the collegiality of physicians and surgeons and lung transplantation and we have in some ways um by allowing borders to get in the way when there was a waiting time everybody understood it even if it was unfair uh except to patients with clpd um but everybody understood it there were no questions about it there was collegiality there was sharing or um compassionate reasons but when you start putting borders and rules and lines on it then um um the mustering of forces to over overcome those rules is much more difficult just given that that what you described it strikes me this may be one of those cases where when you put the rules in place people's psychology changes to well am i keeping the rules rather than um you know am i doing the right thing and you know i think about chris castles essay night's naves or ponds uh talking about the the way that the system approaches physicians changes their psychology about what they do so if the system says well physicians physicians are going to be perverse so we got to put some rules around them to control them well then they start thinking in terms of just keeping the rules um and we see that with billing you know that when in 98 or whenever it was when pennsylvania was uh find 40 million dollars for up billing they put all these rules in place and now everybody up bills uh because the rules allow it to let's ask one of the questions that you kind of posed on your last slide if it looks like at least from a mortality standpoint there's no benefits to transplanting in la s below 40 has there been any medical research or discussion about whether there should be a cutoff at the lower ends of when we should even offer a transplant uh i don't know if they're they would you get into the like phishing laws 40 to 80 nothing above 80 nothing below yeah different issues different issues there's a substantial difference in survival with scores above 80 than there are below 80 in the immediate going so they get benefit so that's post transplant survival so so their patients post transplant survival is directly proportional to their la s that is the uh i guess i should say indirectly proportional so the lower the la s the better their survival which you know if we transplanted you or i were transplanted we we should do well would we get any benefit from that probably not we i'm sure we wouldn't in fact but but the fact is if you transplant a healthy person they're gonna do better so again that's why it goes back to the issue of of net benefit and that's really always the question the problem in these situations is we know a lot more about what happens after we do something than if we choose not to do something and so it's always difficult to to to assess what the true net benefit of anything is because patients who get something are generally followed there it's easy to find their endpoint patients who don't they sort of go away and and there's a number of confounding factors that may impact what happens to them after the fact it just seems interesting that to me it looks like a low-hanging fruit example where you have this misallocation of a scarce resource for no net benefits and you also as you alluded to have a perverse incentive for people to transplant and 60 or 80 percent of transplants are going to people who potentially don't need it from the mortality standpoint and yet no one's addressed that problem they're talking about well again it's multifactorial so it's not that there's someone sitting above all this that has you know has the magical power to change this there's it's it's influenced by many things and also again just to be clear our my endpoint survival may not be the most important one for COPD patients they probably i think we all know that they don't really get a great survival benefit but their quality of life should be better how do we measure their quality life that's a very difficult question to answer so again i don't want anyone to walk away saying that we're transplanting the wrong people but i will agree with you that i think it's pretty clear that our system is imperfect it could be fixed with some minor changes but i have to tell you i'm not optimistic that it will be changed anytime soon as an aside i got a call yesterday from somebody who's on the chairman of the so sts is the main thoracic surgeon society i got a call from the person who chairs the media and public relations subcommittee which i'm actually on to apologize because my paper because one of these papers is actually is being is in print this month and it was up for sort of a press release and and and for them to promote and they chose not to because they thought it was too negative and they agreed that it was obvious that there's a problem in the allocation system and they don't want people walking around thinking that the that the lung allocation system is is perverse which i totally understand and agree with i thought it was actually very nice of him to to call me and apologize and unnecessary but but it is interesting when i give this talk to a group of lung transplant surgeons people get up and there's the number of people who agree with the number of people who disagree is usually about 10 to 1 but i still don't think it's going to change thank you


A digital age classroom, what's it like? The use of new technology in the classroom is a complex topic with many variables. The aim of this essay is to highlight some of the advantages and weaknesses of the use of technology in the classroom and how to find the middle ground. Then looking at how to implement technology in a way that creates a conducive learning environment for students, with a specific focus on the ESA approach. Finding technology as a supplement to teaching and creating a positive learning environment for students is the goal. Advantages Technology in and of itself is not a bad thing, much like anything, if used inappropriately, it can be counterproductive to the learning environment. However, it also holds infinite possibilities to help supplement learning. The ability to have the answer to nearly any question is at the student's fingertips with the implementation of the Internet. This is a great resource for self-study and learning outside of class. For in-class use, there are sites such as Secretive and Kahoot. These sites allow you to have a simultaneous opportunity to ask questions while the lesson is going, even anonymously if so chosen. This allows students to not be worried about being embarrassed for asking questions and also provides everyone with a physical transcript of questions and answers for later review. Kahoot is a site that allows you to create fun games and activities, which are ideal for the engaged phase of the ESA structure. Lastly, the unending resource of relevant and updated authentic materials online is a huge benefit of technology in the classroom. Weaknesses All things require a balance to a degree, and technology is no exception. There are a lot of positive attributes, although plenty of negatives as well. One of the dangers of the use of technology in the classroom is the temptation of distraction. If a web-based resource is being utilized, the temptation to check social media or email is also present. The majority of people are comfortable with technology, but teaching English to foreign language learners may take place in regions where technology is not as developed, or lacking altogether. This also can present a difficulty if a lesson is based around technology. Arguably the biggest weakness, unreliability. Technology fails, often actually. If a lesson is based around a PowerPoint for example, and that PowerPoint does not open, there needs to be a backup lesson plan available. ESA and Technology Technology in the classroom can be a wonderful supplement to learning, and specifically in the ESA structure. The engaged stage is ideal for the implementation of technology, with the use of modern resources such as YouTube, social media sites, blogs, etc. The options are endless for finding interesting and relevant materials to capture students' attention. The study phase can also use technology, but in this phase, it can be more structured and use learning activities such as a gap fill exercise utilizing the secretive platform. This will keep students engaged and allow them to use different modalities in learning. As for the activate stage, technology plays more of a resource role. For finding pictures or facts about a given topic for a group project. The so what? Technology is a part of the younger generation's lives. They will use it whether or not it is incorporated into the classes. Utilizing technology especially for the engaged portion of the ESA structure has the potential to create an enjoyable and appealing environment for the students, an environment where they feel safe to learn and make mistakes. The ideal situation is to be able to exploit the advantages of technology in the classroom and try to circumvent the weaknesses. Finding a happy medium is the goal. Implementing technology in the classroom, not as a replacement of teaching, but as a supplement, has the opportunity to greatly increase the efficacy of learning.  Speak with an ITTT advisor today to put together your personal plan for teaching English abroad. Send us an email or call us toll free at 1-800-490-0531 to speak with an ITTT advisor today.


IBM basically said, listen, it's going to be the fourth quarter now that you're going to see some sort of good news when the main frame is really in full swing. Therefore, what dominated the discussion was 21 straight quarters of revenue decline. The strategic imperatives are doing fine, but you need better than fine. In the end, I think a lot of people are just saying wait till next year and wait till next year environment. Obviously, there's so many other stocks worth buying.