[dr. richard gorman] good morning. whenever i hear that introduction, i am remindedof something that someone once said about me, which is—they were quoting my wife,who said that on my good days, i sound somewhat like a renaissance man, but on my bad days,i’m more of a dilettante. you’ll get to decide. someone also once said, “he sounds likesomebody who can’t keep a job,” and that’s also true. so, i deliberately included the fact thati was a pediatrician on these slides. how many here work with children in theirresearch capacities?

thank you. how many work solely with children? thank you very much. so, my interest in pediatric research startedearly, when i realized that we didn’t know very much about children from a logical way,by doing clinical research. when i started in my medical career, researchon children was considered unethical; things have changed. so, our disclaimer and conflict of interestfor nih is becoming as long as irb consent forms.

the presentation does not endorse or recommend– oh, i can see it here – any commercial products, processes, or services. the views and opinion of the speaker are myown, and do not necessarily reflect those of the us government. and these statements may not be used for advertisingor product endorsement purposes. so here’s the outline of my presentation,we’re going to talk about—we’re going to give a brief introduction to research,the introduction of bias – we are all biased. we’re going to go through some examplesof things that we knew that were lies, we’re going to talk about the grade system, whichis an organized and systematic way of looking

at data and trying to create from it information,and there will be a one-slide, take-home message for you. so research has evolved. and you can decide when you think “then”was, but “then” was small-scale, and now research is done on an industrial level. it was potentially therapeutic, and now wedo studies not so much to help people get better, but to increase our storehouse ofknowledge – basic science. in the beginning, we did research on ourself[sic], our friends and our families, and we’ve expanded that to patients, prisoners, andorphans.

so, research has come a long way. so how have we evolved in our data collectiontechniques? we started out with personal experience. never underestimate the power of personalexperience. how many people took an aspirin this morningbefore they came here? not for the headache i’m about to induce,but for your cardiac disease. so, that occurred because a hospital administratorwent to a rheumatologist in his community who wanted to know why he wasn’t admittingto the hospital as often as the other internists. it turned out that his practice had far fewerheart attacks than others; the difference

was that he used aspirin to treat all hispeople with rheumatoid arthritis and had a lower incidence of heart disease. that was confirmed later on with clinicaltrials. expert opinion, that’s when you get peoplelike myself who get up here and talk as if they were experts, and i definitely have opinions. they came, developed into small-case seriesof natural history studies, large-case histories, and natural history studies. we then started to do cohort studies wherewe compared groups, and we finally ended up with randomized trials, where we took thesame group of people to try to reduce the

number of variables and then try out our interventions. clinicians, in their case series presentations,talk about “in my experience.” as a private practitioner for 20 years, ihave said “in my experience,” which said that i had seen one case. if i said “time and time again,” it meanti had seen two. and if it’s “been shown over and overand over again,” i saw three cases that were therapeutic successes. so how many people here think that they’rebiased? how many people here think they’re not biased?

well, i’m glad to see that. the introduction of replay into major leaguebaseball, the sport of my particular choice, has truly demonstrated how biased i have been,because i knew that every call that went against the baltimore orioles was a mistake. now, unfortunately, i’ve been shown thatit’s not a mistake and, far more often than i would have ever given them credit, the umpiresare correct. so bias comes from a lot of different places,but i like to start out with plato’s allegory of the cave. for those of you who were bored to death inphilosophy class, i apologize.

but for plato’s allegory of the cave, itstarts out that you’re taken at birth and you are chained to a step where all you cansee is the cave wall. it’s your only information. and then people behind a stone wall – soyou can’t see the people – hold up objects in front of a light source, in this case afire, and throw images onto the wall, fantastic images. but they are your only reality. you begin to believe that they are real. much like powerpoint, now that i think aboutit.

okay. the prisoners eventually break free and getto see behind the wall, and realize that the shadows were created by objects, and theymove closer and closer to the cave entrance where they gain both knowledge of what theworld is really like, because they get to see the objects and not the images of theobjects, and then when they leave the cave entrance, they have gained wisdom. and the philosophers of this time were thescientists of today. they were trying to ask the big questionsto get the big answers. so you have to sort of recognize your limitations.

there’s a really small circle of what youactually know, that is surrounded by a slightly larger circle of what you think you know,an even larger circle of what you know you don’t know, and then by far the largestuniverse is what you don’t know that you don’t know. every time we have a clinical trial that’sdone twice and gets two different responses, people say “oh, somebody did it wrong,”and i say, “no, we didn’t know what we didn’t know, and there’s a variable outthere that we’re not yet measuring.” the first people were honest, the second groupwere honest – assuming that they are honest – but, there’s something else going onthat we don’t know.

and if you get confusing results, it’s wonderfulfor generating hypothesis, but it’s not so wonderful at generating information. so don’t be fooled by your limitations. benjamin disraeli, among many others, havebeen credited with the sense there are lies, damn lies, and statistics. i was fortunate enough to have several statisticscourses, one in physics and two in the social sciences, and dave melis, my first statisticsprofessor in the social sciences, said to me, “what’s the most important statisticaltest?” and so we put out the chi-square, other testscame to the—the t-test came up, analysis

of variance came up, and he said “no, themost important statistic is the interocular statistic. if it hits you between the eyes as important,it’s important. if you’ve got to prove it with math, maybenot so much.” so if you don’t read the newspaper, you’reuninformed. if you do read the newspaper, you’re misinformed. i feel the same way about scientific literature:i go from uninformed to potentially misinformed. those who cannot remember the past are condemnedto repeat it, which gets taken up by clarence darrow as “history repeats itself, and that’sone of the things that’s really wrong with

history.” so how do we go from data to knowledge? data is defined as a set of discrete, objectivefacts or values that does not have any attached meaning or context. you change data into information, and here’swhere you introduce bias. data that has been organized and processed,who organizes it and processes it? human beings, and are able to instruct orinform other users. so you organize your data, you take the datathat you like, that fits your hypothesis, and you organize it in a way that supportsyour hypothesis.

if you can get from information to knowledge,which is the sum of what is known as integrated and applied gain through further education,practical experience, and training. but bias comes in it very early in the stuffthat we look at, in the information that we think we have and the knowledge that we thinkwe have, because it’s introduced when you take this step, this jump, from the thingsyou measure to what you think they’re saying to you. my background is in physics, so you have tohave at least one equation. u = v * r/w. usefulness of the information is dependent upon the validity of the information,times the relevance, divided by the work it

takes to find that information. and you allhave a way of doing this in your own head, because you all have a way where you go—youall have places where you go to get information. some of you use evidence-based textbooks,some use systematic reviews, portable summaries, drug reference handbooks, some of you go tocolleagues, practice guidelines… and sometimes the relevance is high, but the validity islow, sometimes the relevance is high, the work is low. and as you go down you have differentways of looking at how you go for information. at the very bottom of this slide, i have massmedia and the internet now. and by the internet now, i don’t mean the systematic databasesthat are on the internet, but i mean when you type in “lyme disease” and the firstthing that comes up is the first thing you

click on. please be careful, unless you’relooking for things to put in your gin and tonics. so one more time, going through the evolutionof data to knowledge. data is composed of discrete elements that are categorized, calculable,you collate them, you quantify them, and you collect them. in information, you contextualizethis data. you compare data to other data that you might have, you converse, you connect,most importantly, you filter, you prioritize, and you order, all based on your experiences,knowledge, and bias, or lack thereof, and you order and frame. in knowledge, which isa little bit higher than information, you structure, you interpret, you evaluate thevalue of the information, and you deconstruct

the information that you’re given. peoplewho are knowledgeable, and i suspect you all have someone you go to for knowledge, whatthey do is they can sort through all the information that you have. so you get a feeling for notjust the information that’s out there, but where it’s rank ordered in the universe.there are very few wise people, and there are very few wise clinicians. but these peopleactually have the ability to apply knowledge through the filter of their experience. so we have both conscious and unconsciousbiases. the conscious ones are pretty easy; i support the baltimore orioles, the umpiresare always wrong when they call plays against the baltimore orioles. that’s a pretty consciousbias. i probably have hundreds of unconscious

biases. for a while, people were trying toget around these biases—one of the systems that were being used were things called wordclouds, where you would put up the word clouds, you would have people answer a set of questionsabout topics, and they would then create the size of the answers by the responses by theparticipants. but, everybody just looked at that slide and they picked out a single word.i’m not going to ask you what that word is. another word cloud, because you have emotional attachment to some words. some people maysee ‘molecular,’ some people may see ‘epidemiologic,’ some people may see ‘genetic.’ well, geneticis the largest. there are those of us who

have color issues. we can’t see certaincolors because of our genetic makeup. and some letters—some words actually disappear,and you can see open spaces, even though it’s supposed to be a way of doing this. the structureof the organization and not only the size of the word but where it’s placed in thediagram, and its orthogonal relationship – whether it’s lateral the way you’re used to reading,or vertical – makes a difference on how you see the particular piece of information. okay, so you’ve looked at this, what didyou see? we saw some fish, some people saw fish, okay, how many people saw fish? howmany people saw birds? how many people saw fish in two colors? excellent. how many peoplesaw birds in two colors? alright. can we go

back? okay, now that you know what you’relooking for, you can see that there are both fish and birds in two different colors. butwe’re humans, and when we look at facts, we look at them with our biases or our preferencesor our abilities to move forward. i’m going to give some examples of thingsthat we know, or we knew, that we now know we don’t know. so let’s start with leprosy,one of the most feared diseases in human history. and what did we know? we knew that leprosyis the result of sins. and why did we know that? it was because fathers and their childrenoften had it, and mothers and their children often had it together. family groups had it,so they must be sinners. and we knew also that isolation was the best treatment forall of these people, so we put them in leper

colonies, of which there are still two inthe united states. now what do we know? leprosy is caused bya bacterium, the mycobacterium leprae. the mechanism for infection is still not understood,but it is not highly contagious and the incubation period is as long as two to eight years inendemic areas. one of the reasons i decided to specialize my field of interest in infectiousdisease was the following fact. 95% of adults are naturally immune to leprosy. you needto have two doses of consecutive genes in a very uncommon allele structure to be susceptibleto leprosy. so 95% of the people in this room are genetically incapable of getting it. iwonder how many other infectious diseases are caused by genetic susceptibility.

estrogen prevents heart attacks. how manypeople went to medical school or nursing school and this was a true statement? okay, everybodywith gray hair should put up their hands as yes. this was a case of logic, and not data.estrogen prevented heart attacks. there was a fact – i’m sorry, not logic, but therewas a fact – young women have fewer heart attacks than young men. that was a fact. italso was a fact that young women have more estrogen than young men, and so the logicalconclusion of that was that high levels of estrogen are protective of the cardiovascularsystem. and we believed that. we didn’t actually do much with it, but we believedit. but then what happened was, we started giving hormonal replacement therapy to womenwho had gone through menopause. and hormonal

replacement theory is associated with an increasedrisk of stroke, stroke severity, and venous thromboembolization , but not coronary heartdisease. so it didn’t protect you from coronary heart disease, didn’t make it any worse,either. but it made a lot of the other vascular consequences much, much greater. althoughmost trials studied all the patients, increase was not related to age. combining hrt— increasethe combined hrt increases the risk of venous thromboembolism compared with estrogen monotherapy.so we knew for a fact that estrogen prevented heart attacks, right up until the fact thatwe had data that it did not. stress causes ulcers. in medical school, thebillroth i was the most common medical procedure done in the united states. it was an operationwhere you took out a piece of the stomach

or the duodenum to take care of an ulcer disease.post world war ii, there was an increasing us incidence of peptic and duodenal ulcers.there was also an increase in smoking, alcohol consumption, and modern life was consideredmore stressful than other lives. so the conclusion was, that smoking, alcohol consumption, andmodern life stress led to increased ulcers, and that led a lot of people to have a lotof stress reduction therapy, alcohol avoidance – which is probably good on a couple oflevels – and smoking cessation, but despite all of those things, ulcers did not disappear.we now have data that ulcers are an infectious disease, not a lifestyle disease. the bacteriumhelicobacter pylori is linked to the development of peptic ulcers disease, gastric malignancies,and dyspeptic syndromes. and h. pylori is

present in more than 90% of duodenal ulcersand about 80% of stomach ulcers. so for those of you who have stomach ulcers, you now geta six-week course of antibiotics and you’re cured. one of the most amazing slides, whichi didn’t include in this deck, was the incidence of ulcer disease comparing to the use of antibioticsin the united states. ulcer disease started to fall before we understood what was causingit, but it was inversely proportional to how much antibiotic was used in the united states.so as the amount of antibiotic used went up, the amount of ulcer disease went down. wewere treating ulcer disease without being aware of it. eating fat makes you fat, and leads to heartdisease. this is one of my favorite – favorite

– this is one of the most egregious examplesof scientific fraud. we all know that you are what you eat. we all know that fat iscalorically dense. we all know that fat is implicated in heart disease through the mechanismof atherosclerosis. so the biases that we had was that fat makes you fat, and fat leadsto heart disease. and it was based on the keys’ seven country study. it showed a curvilinearrelationship between the percentage of fat intake of the total caloric intake to themortality of heart disease in six of seven countries. the problem was that dr. keys hadcollected data on 22 countries and chose to present the six out of seven – didn’twant to make it look too good – so they only presented six out of seven to prove hishypothesis.

so there’s now been a meta-analysis of prospectiveepidemiological studies that show there is no significant evidence concluding that dietarysaturated fat is associated with an increased risk of coronary heart disease or cardiovasculardisease. so fat is now good for you again, congratulations. please don’t eat too muchof the bacon at the break. but there’s a new logic developing, which i will ask youto pay attention to, is that carbohydrates cause diabetes. boy, is that logical. eata lot of carbohydrates, you trigger a lot of insulin, you wear out your pancreas, yougain a lot of calories, it makes so much sense. i have no idea if it’s true or not, buti’m going to hear a lot about that, i think, over the next couple of years.

denis browne splints. so, the fact is thatinternal tibial torsion was a twisting of the tibia. logic: exerting a correcting forceusing a splint or brace would correct the twisting. and the generalized conclusion isforce works for both orthodontia, scoliosis, and torsion. here’s probably one of themost famous examples of somebody getting rid of their denis browne splints – one of myfavorite movies, as well. so, this is forrest gump running, and he is in denis browne splints,or was put in them because he had internal tibial torsion. in 1991 there was a prospective,randomized, controlled study that showed that the denis browne splints did work very well;however, doing nothing worked equally well. since 1991, many other splints have been designedto treat this condition, but no non-surgical

treatment has been shown to be any more effectivethan doing nothing at all for young children with uncomplicated tibial torsion. i knewfor a fact when i was a young pediatrician that denis browne splints were the treatmentfor tibial torsion, and i would wonder how many people i have scarred for life for that. bad air causes infections. logic, facts: peoplewho live near swamps, standing water, and refuse piles have more infectious diseases,true. something in the air increased the chances of infection, so the conclusion was that badair causes diseases. it developed into the miasma theory: diseases such as cholera, malaria,yellow fever, or the black death were caused by miasma, a noxious form of bad air alsoknown as the night air. the theory held that

the origin of epidemics was due to a miasmaemanated from rotting, organic matter. and they almost got this right. so here’s acholera epidemic also put down to bad air. so since we’re talking about human subjectprotections, i had to talk about one of the earliest clinical trials. not the earliest,but one of the earliest recorded clinical trials. the earliest one i can find is actuallydone by a british surgeon on a ship who showed that limes prevented scurvy, but this wasn’tfar behind this. so walter reed performed a clinical trial to see if it was bad airor mosquitoes that carried yellow fever. he filled a tent with bad air, an infectiousitem, and he had a tent in a mosquito-filled area. and only individuals exposed to themosquitoes became ill. and in 2016 with our

knowledge about vectors, we know that vectorscarry diseases and it’s the mosquitoes that carry disease—a lot of diseases that arenot so good to men. this is one of the first clinical trials that used an informed consent.so you got paid for your participation, a 100 dollars for participation, 200 dollarsif you died. it was going to be paid in gold, and you could be paid to your things. so,in the irb world, there’s inducement and coercion. coercion is getting people to dosomething because they’re fearful, and inducement is to—getting them to do something becausethey lust after the reward. 100 dollars was equal to six months of pay for soldiers atthis particular time. i will ask your irbs to consider whether that’s an inducementor not. the only female volunteer for this

entire series of studies by walter reed wasa nurse named clara maass, and she died of yellow fever. and the public outcry againstthat ended challenge studies, human challenge studies, in yellow fever for about 90 years. and here’s a copy excerpted from walter’sinformed consent. and the dot-dot-dots that are there are actually a fairly short seriesof sentences, but it basically says “the undersigned understands that he endangershis life to a certain extent, but it being entirely impossible for him to avoid the infectionduring his stay on this island, he prefers the chance of contracting it intentionallyin the belief that he will receive from the commission of the greatest care and the mostskilled medical service.” walter reed signed

this on november 1st, 2016 [sic: 1900], andthat sentence is about as long as most sentences that i presently see in our irbs that aresupposedly clearly written. i want to talk to you now about the grade system. if we’regoing to reduce bias, which is the goal in science to some degree, or one of the goalsof science, you need to protect against errors. you need to resolve disagreements in how youlook at things, you need to facilitate critical appraisal, and you need to communicate informationin a systematic and explicit approach. one of the many systems that’s out there isgrade. clever mnemonic, always go for the good mnemonics. grade of recommendation, assessment,development, and evaluation, grade. and the aim of this system was to develop a common,transparent, and sensible system for grading

the quality of evidence and the strength ofthe recommendations. the grade system has been adopted by over 60 groups that createguidelines. when i leave this meeting today, i’ll be heading to atlanta to go to theadvisory committee on immunization practices of the cdc that makes recommendations on thevaccines that you give your children and adults. we use the grade system. the grade system has an underlying beliefthat expert opinions have more bias than case reports and case series, which have more biasthan cohort studies and case control studies, and the randomized, controlled clinical trialhas the least possible bias. so the information—i’m sorry, the data gathered from randomized,controlled clinical trials should be the freest

from bias. i am from the nih; we all haveto put up slides that we have to apologize for. it all starts witha group of people, commonly known as a committee, and they sit down and decide what problemthey want to look at or what thing they want to evaluate, and they decide on what patientsthey’re going to look at, what interventions they’re going to look at, what comparergroups they’re going to look at, and what outcomes they want to look at. they then haveto look at the outcomes, they have to select the outcomes that they think are either critical,important, or not important. so they collect—they decide on what outcomes of these clinicaltrials they want to look at, and then they

rate them to see how clinical. they then takethe world’s literature and review them, put the results into a flow chart that youget to look at, and then you start to make another series of judgments as you rate thequality of evidence for each outcome. you then get to grade down – interesting useof the word – if you think that there’s a risk in bias in the data that you’ve lookedat. if you think there’s inconsistency in the results; if you think that the measurethat they’re looking at is indirect to the measure that you want; you want to look atthe imprecision, can they tell you exactly what they’re looking at; and you want toremember that there’s a publication bias, and positive trials get into the literaturemuch more commonly than negative trials. the

good news is you can also grade up. so, thequality of evidence can go from—can be graded up if there’s a large effect size, and i’mgoing to give you an example of that later; whether there’s a dose response – oneof the few things i remember from pharmacology, if there’s a dose response, it’s probablydoing something – and if there are few or no confounders. when you finish that, yougo down to grade the overall quality of evidence across the outcomes-based, and you do it onthe lowest quality of evidence for the critical outcomes. it then goes back to the group – sothe group starts this process, gets all the data, goes back to the group – and thenthey make recommendations. they can make recommendations – and one of the nice things about the gradesystem is you make recommendations by considering

quality, balance, values and preferences,and revise if necessary if there needs to be a resource. how much does it cost? howavailable is it? can we use it at our hospital or center? and then you come out here andyou use standardized language: “we recommend using… we suggest using… we recommendagainst using… and we suggest against using…” this process is iterative. if necessary, asnew data becomes available, this process often gives you a really good sense of what theinformation is that is not out there that you want, to help you make a decision. andlike napoleon said, all important decisions are made with far too little information.so when we make our recommendations, we often make them with less information than we want,but this at least helps us take the bias out

of the recommendations that we want. so thisis a system: time consuming, labor-intensive, but important. and the more important thedecision you’re trying to make, the closer you should adhere, if not to this system,to another system. for those of you who are unaware, the britishmedical journal, in its december issue every year, puts at least one study in that’sa joke. so “parachutes used to prevent death and major trauma related to gravitationalchallenge: a systematic review of the randomized, controlled clinical trials.” so “parachutes used to prevent death and major trauma related to gravitational challenge: a systematic review of the randomized, controlled clinical trials.” “parachutesreduce the risk of injury of gravitational challenges, but their effectiveness has neverbeen proved by a randomized, controlled clinical

trial.” yeah. but the grade system allows you to upgradedata without having to go to a randomized clinical trial. this was in the—not thefirst, but this was towards the end of this particular article. they looked at the relativerisk reduction. they took information from the united states parachute association thatreported 821 injuries and 18 deaths out of 2.2 million jumps. so they could concludethat, even without a randomized, controlled clinical trial, that this particular interventionwas effective because of the size of the effect size. so, if you’re looking at a 99.9% reduction,it’s probably working, even if the trial is not randomized. i work at nih, i work inthe infectious disease group, and there are

people who we are—you may be aware, c. difficilehas become a major infection, has become a major issue because of antibiotic overusein departments such as the icus, often well-intentioned, but people end up with c. difficile. so then,because we caused it by causing an antibiotic and caused by changing the human microbiome,we then treat it with antibiotics because that makes sense, because if you’ve causedit by antibiotics you treat it with antibiotics to get rid of the c. difficile. but it’sa really specific antibiotic, we tell ourselves. some brave person decided to reconstitutethe colonic microbiome by using fecal transplants. so they would take your poop and give it tome if i had c. difficile. if i give you the usual antibiotic therapy for c. difficile,my success rate after 20 days, 21 days of

therapy, is approximately 17%. if i take poopfrom somebody you select and give it to you as a colonic enema, my success rate is 90%.guess what? we’re doing the controlled clinical trial to see if it’s really effective, becausethat’s what i do. but i can be pretty well sure, with those kinds of improvements inthe care, that fecal transplant is the treatment of choice for c. difficile infection in 2016. so we’re now going to finish this up bygiving you a small test, okay? you are not going to be graded. so, here’s a product that recently got approved,and this is the product label in an advertisement. now, in advertisement, drug companies canonly put on what’s in the label, and so

they basically will show on the label, andi’ll highlight the important parts for you now. this is a treatment for hemangiomas ininfants, another case where a very astute cardiologist who is treating children withbad vascular diseases with propranolol, noticed that when he started propranolol on childrenwith large hemangiomas, the hemangiomas involuted very rapidly. he reported this in a series,it then got picked up by the pediatric dermatologic community, who then did some case series,and we now have some randomized, clinical trials. it has—so on the top are two facts.80% of hemangioma growth is complete at three months. and up to – up to. what does thatword mean when you hear ‘up to?’ – meaning never has it reached 69%… up to 120 glassesof iced tea, nobody has ever gotten 120 glasses

of iced tea… of infantile hemangiomas haveresidual lesions when left untreated. it has proven efficacy, it says. in phase 2 and phase3 clinical trials, 60.4% had complete or nearly complete resolution by six months, versus3.6% of placebos. facts, or information? what do you mean when you hear nearly complete?what do you hear when people say that? i think i know what i mean by ‘nearly complete,’but i’m not sure what they meant, and until i read the clinical trials, i won’t be sure.88% of patients showed improvement at week five of therapy. again, define ‘improvement’for me. so i’m not asking you to be skeptical of everything that comes out; this has beenan amazing, transformative drug for people with hemangiomas. but be careful when you’relooking at information like this. and then

it has the safety profile on the bottom. themost common adverse events with this, blah-blah-blah-blah-blah. and then fewer of 2% of treated patients discontinuedtreatment cost to safety concerns. you might want to know what those are before you starttalking to your parents. also, be careful when people come bearingyou gifts. so i’m here talking about the grade system, it’s a system i use in twodifferent groups that i make—help make recommendations for, but guess what? the grade system andscale have yet to be externally validated. when you look in a scientific way at the correlationbetween reviewers, it’s not so good. so different reviewers faced with the same informationmake different conclusions. the good thing about the grade system is there is a processin the grade system to allow you to reconcile

those differences. and most importantly, ithas never been empirically tested. do the recommendations that we make using the gradesystem, are they better than we made using just informed, expert opinion? my guess forthe last one is yes, but i have no data to support that. so the take-home message. i was fascinatedwhen i got to lovely nashville yesterday – i walked around, i had a couple of hours ofdaylight left – that we were so close to st. thomas’ hospital. anybody here workthere? so st. thomas, in case you didn’t know, was known as the doubting apostle. hewas the one who said, “i won’t believe that jesus raised himself from the dead untili put my hands in his wounds and i see him

with my own eyes.” i don’t want you tobe as bad as st. thomas in terms of being that unbelieving, but remember to doubt whatyou’re told and say, do i know this, do i think i know this, and where am i on thatparticular boundary. so for clinical researchers and scientists and people who are trying toadvance public health and personal health, we will only advance our knowledge by collectingdata, information, and organizing them without the biases that we all have. and to examplethat is when, if you’re blindfolded, and you come up to an elephant, what you thinkyou’re saying will depend on where you go. the people who reach his trunk will thinkhe’s got a hose, the people who reach his tail will think they have a snake, the peoplewho reach the side will think they have a

wall, and the people who reach his legs willthink they have a tree. they’re all feeling the same thing, they just don’t see thebig picture, and they don’t have all the information they need. continue on your work, because as elizabeth—eleanorroosevelt said, “it’s better to light a candle than curse the darkness.” thankyou for your attention.

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