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Speaker 1: Hi, Perry High Emily. I'm glad to be back. Thank

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Speaker 1: you for holding down the fort without me last week.

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Speaker 2: I'm glad you had such a nice vacation. And I

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Speaker 2: am really excited about today's topic. This is one where

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Speaker 2: to be honest, I am hoping to get to my

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Speaker 2: smasher pass. Like through the course of our discussion, I

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Speaker 2: am not there.

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Speaker 1: You haven't committed, You're not crazy.

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Speaker 2: It's really difficult. We'll get to that. You know, we're

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Speaker 2: talking about full body scans today. And because I know

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Speaker 2: our listeners are dedicated Treki's Like, I know this because

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Speaker 2: I choose to believe it. And you know, I grew

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Speaker 2: up watching Star Trek the Next Generation, and you know,

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Speaker 2: watching the doctors use the tricorder scanner and you just

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Speaker 2: kind of and all of a sudden, you know everything

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Speaker 2: that's wrong with the person. This is basically why I

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Speaker 2: wanted to become a doctor. I assumed that this is

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Speaker 2: how it would work. I frequently walk around telling people

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Speaker 2: God damn it, I'm a doctor, not a blank like

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Speaker 2: that is one of my go to catchphrases.

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Speaker 1: Yeah, I considered becoming a doctor because I wanted to

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Speaker 1: meet Doogie Houser, Because I had the biggest crush on

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Speaker 1: Neil Patrick Harris.

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Speaker 2: I have some bad news for you, Emily.

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Speaker 1: I know, but it's okay, it's okay. And then I

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Speaker 1: didn't become a doctor, and you know he's not for me,

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Speaker 1: but I still love him anyway. My point is we're

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Speaker 1: going to get to your smasher pass. And this is

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Speaker 1: a great topic because it apparently intersects with your love

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Speaker 1: of Star Trek and my love of bays Rule, and

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Speaker 1: we're going to find out about both of those things.

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Speaker 2: Awesome, let's go.

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Speaker 1: I'm Emily Oster, I'm an economist and a data expert.

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Speaker 2: And I'm Perry Wilson, I'm a medical doctor.

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Speaker 1: It's Thursday, June twenty fifth, and this is wellness.

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Speaker 2: Actually, because you're getting a staggering amount of health and

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Speaker 2: wellness information nowadays from every source imaginable, and some of

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Speaker 2: it is awesome and some.

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Speaker 3: Of it is well actually both Fortunately we're both people

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Speaker 3: who know how to read studies, how to parse the data,

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Speaker 3: and can tell you what's worth thinking about and what

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Speaker 3: you can safely ignore.

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Speaker 2: But before we dig in, a note that this podcast

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Speaker 2: is for educational purposes and should not be construed as

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Speaker 2: medical advice. We don't know your unique situation, so talk

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Speaker 2: to your doctor for personal health decisions.

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Speaker 1: This week we're asking what's the deal with full body scans?

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Speaker 1: Perry and I will give the official smasher pass, and

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Speaker 1: then we'll get to your question of the week. But first,

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Speaker 1: let's do the health news roundup after the break. And

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Speaker 1: now for the health news of the week, Perry. First up,

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Speaker 1: the flu. We're having a flu outbreak on a military base.

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Speaker 1: No one could have predicted this except us, and also

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Speaker 1: many other people say more you.

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Speaker 2: Know, no one likes and I told you so. But

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Speaker 2: I would like to play the following clip from a

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Speaker 2: prior episode of Wellness. Actually, Perry, no.

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Speaker 1: More flu vaccine mandates for military troops.

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Speaker 2: Your thoughts, Yeah, this is weird. Combat readiness is in

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Speaker 2: part determined by the health of the troops in terms

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Speaker 2: of infectious diseases. I mean, if you look at like

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Speaker 2: the history of warfare, you would find that infection has

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Speaker 2: killed vastly more soldiers than combat. Is often responsible for

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Speaker 2: losing the war. I mean, one of the reasons the

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Speaker 2: Americans were able to hold out during the Revolutionary War

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Speaker 2: was because Washington made the rather bold and somewhat risky

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Speaker 2: decision to force smallpox vaccination on all the Revolutionary war troops.

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Speaker 2: And like, let's not forget that the worst influenza pandemic

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Speaker 2: in history, this is the Spanish flu Influenza pandemic, occurred

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Speaker 2: after World War One because a bunch of infected troops

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Speaker 2: brought brought the virus home. So a weird decision. It

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Speaker 2: feels completely like brazenly sort of political to me. I

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Speaker 2: see no justification for this in a rational society. All right,

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Speaker 2: we told you so. We are referring, of course, to

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Speaker 2: influenza outbreak on a joint military base in Lackland, Texas.

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Speaker 2: As at the time of this recording, there are two

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Speaker 2: hundred and twenty two positive cases from the thirty seventh

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Speaker 2: Training Wing. This is from what I can tell, a

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Speaker 2: flying training group who live in close quarters, obviously, and

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Speaker 2: this is happening after Secretary of Defense Pete Hegseth eliminated

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Speaker 2: the flu vaccine mandate, after which time for military personnel,

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Speaker 2: and after which time only forty percent of trainees chose

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Speaker 2: to be vaccinated for the flu, which is a yearly vaccine.

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Speaker 2: You know. Once again, although we said it before, military

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Speaker 2: readiness is more inhibited by illness than just about anything else,

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Speaker 2: which is why vaccination has been such a huge part

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Speaker 2: of armies all around the world. And you know, they

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Speaker 2: even joke about how much stuff they're getting vaccinated for,

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Speaker 2: and there's a reason for that, and this flu outbreak

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Speaker 2: is telling us why. It is the case. Two hundred

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Speaker 2: and twenty two people out of training for a while,

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Speaker 2: two of them hospitalized by the way, which is not great,

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Speaker 2: and we're obviously pulling for them. Emily, do you think

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Speaker 2: this is going to change policy?

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Speaker 1: Nope, I don't, but I wish it would, but I'm

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Speaker 1: not optimistic. I think you will hear people say, well,

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Speaker 1: even if they had been vaccinated, blah blah blah blah.

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Speaker 1: So I don't think it will change policy, even though

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Speaker 1: it definitely should.

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Speaker 2: Absolutely, let's stick with vaccines for a second, because we

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Speaker 2: have some really cool vaccine news. A new study appearing

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Speaker 2: in the Lancet about the HPV vaccine in the UK. Emily,

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Speaker 2: what's going on with HPV so.

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Speaker 1: Great, So I think first it's worth sort of stepping back.

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Speaker 1: We give kids to the HPV vaccine, and we give

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Speaker 1: people the HPV vaccine, typically in adolescents, and it's actually

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Speaker 1: one of the vaccines where you see more hesitancy, and

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Speaker 1: I think we're not always doing a great job of

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Speaker 1: explaining to people why we should give kids this vaccine.

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Speaker 1: The vaccine prevents against the human papuloma virus, two different

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Speaker 1: versions of it, and the reason for this is that

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Speaker 1: those are the primary causes of cervical cancer. So this

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Speaker 1: is really a true cancer vaccine. It's a vaccine that

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Speaker 1: prevents you from getting cervical cancer in principle and also

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Speaker 1: now in practice. So this new study is from the Lancet.

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Speaker 1: It's a population based study which looks at cervical cancer

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Speaker 1: mortality from two thousand and one to twenty twenty four.

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Speaker 1: This cover some of the period in which the vaccine

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Speaker 1: was rolled out. You know, it's not a randomized trial

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Speaker 1: because at this point the vaccine was available to everyone.

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Speaker 1: But they've got calendar year and age to try to

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Speaker 1: sort of suss out the causal impact of the vaccine

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Speaker 1: on cervical cancer. And more or less, this eliminated get

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Speaker 1: close to eliminating cervical cancer. So they estimate a vaccine

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Speaker 1: efficacy between eighty five and one hundred percent, which is amazing,

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Speaker 1: and you know, people die of cervical cancer. Cervical cancer

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Speaker 1: is a it's a really bad cancer. I mean all

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Speaker 1: cancers are bad. This is this is a bad one.

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Speaker 1: And to have a vaccine that is so effective is amazing. Actually,

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Speaker 1: I'm so excited that my kids live in a time

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Speaker 1: when they can be vaccinated for this. And I will

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Speaker 1: just say, not only should you vaccinate your daughters for this,

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Speaker 1: you should also vaccinate your sons because they can give

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Speaker 1: people HPV and because there are some other sort of

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Speaker 1: reasons why they should be vaccinated. So kids should get

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Speaker 1: this vaccine.

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Speaker 2: Absolutely, It's not it's not even just cervical cancer. Cancers

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Speaker 2: of the head and neck are now more driven by

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Speaker 2: HPV than smoking as smoking rates have gone down. Those

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Speaker 2: can obviously affect everybody, boys and girls, men and women.

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Speaker 2: So really positive results. No cervical cancer deaths in this

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Speaker 2: vaccinated group.

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Speaker 1: My eleven year old was bemoaning his need for the

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Speaker 1: HPV vaccine at the next well child visit, which is

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Speaker 1: next week, and I was just like, I can't wait. Sorry, dude,

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Speaker 1: I'm so excited.

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Speaker 2: The best. Yeah, joy, your eleven year old might have

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Speaker 2: something else to bemoan with our last piece of health news,

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Speaker 2: and that is that we are saying goodbye to two

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Speaker 2: close friends, the blue and brown colored Eminems.

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Speaker 1: That's so sad.

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Speaker 2: Why are they going away?

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Speaker 1: I don't know. I blame the Health secretary for everything, actually,

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Speaker 1: but this particular thing. So so, there's been a push

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Speaker 1: towards having no artificial dyes in food. We've talked about

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Speaker 1: this perhaps before. I'm sure we'll talk more about it.

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Speaker 1: I think that's not real evidence based, but at any rate,

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Speaker 1: that's the direction we're going. And so Eminem's is trying

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Speaker 1: to replace all of their dyes with natural colors, and

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Speaker 1: it seems like they can't produce blue and brown. Is

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Speaker 1: that your understanding? Like I was a little confused.

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Speaker 2: I definitely get blue, like that blue color, Yeah, that's

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Speaker 2: not in nature. I don't see that anywhere or that

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Speaker 2: flavor Like blue is its own flavor.

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Speaker 4: But it was not its own fault, but not for

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Speaker 4: eminem but for like, Okay, sure there's like blue, raspberry

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Speaker 4: whatever anyway, yes, blue, but like brown, like, I'm pretty

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Speaker 4: sure you can find something that's brown.

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Speaker 1: There's all kinds of brown things, but it must be

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Speaker 1: the yes. I don't understand that much about color dyes,

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Speaker 1: but I will say blue and brown eminem's are off,

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Speaker 1: and I suspect when we get the new M and

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Speaker 1: ms they will also be not so colored. I mean

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Speaker 1: one of the features of natural dyes, for example in

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Speaker 1: European fruit loops, is that they.

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Speaker 5: Are just like they're not as intense, it's not as

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Speaker 5: fun and so yes, our producer has pointed out in

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Speaker 5: the comments here that chocolate is brown and so could

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Speaker 5: you just leave the eminem But I think it's the

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Speaker 5: candy coating on the m M that's sort of crazy.

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Speaker 2: I mean, what is it without the candy, It's coding.

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Speaker 1: It's nothing. Also would melt on itself, which would be

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Speaker 1: a bad experience.

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Speaker 2: All right, So hoarde your blue eminem's. They will no

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Speaker 2: doubt be worth millions of dollars.

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Speaker 1: They're going to be people who are selling choco tacos

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Speaker 1: and in blue eminems on ets for decades.

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Speaker 2: All right. That is it for the health news of

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Speaker 2: the week. After the break, what's the deal with full

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Speaker 2: body scans? And we are back. What's the deal with

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Speaker 2: full body scans? Emily, I'm going to start by playing

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Speaker 2: a representative clip from Instagram to get us going here.

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Speaker 6: So I just did a full body scan that tests

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Speaker 6: for over five hundred different conditions, including cancer. Now you

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Speaker 6: sit in this little tube thing for an hour and

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Speaker 6: you just watch a little Netflix show, and then you'll

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Speaker 6: get all the information. Now there's gonna be a whole

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Speaker 6: report of like everything from your brain to your toenails.

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Speaker 6: They did find a nodule in my lung. So let

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Speaker 6: me show you that. I can see here, there's a

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Speaker 6: lung nodule. You can see it there. It's point five centimeters. Now,

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Speaker 6: apparently what the doctor told means that lung nodule is

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Speaker 6: either from a prior lung infection, which is okay, and

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Speaker 6: that it doesn't look like it's cancerous. But we're gonna

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Speaker 6: get a scan in a year from now and just

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Speaker 6: make sure it doesn't grow, and we'll keep an eye

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Speaker 6: on it. And so this scan is preventative. And I

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Speaker 6: just don't understand why health insurance companies don't offer and

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Speaker 6: pay for this, and or why our entire system doesn't

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Speaker 6: provide us more preventive care like this. Now, this total

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Speaker 6: scan with pernuvo is twenty five hundred dollars. I know

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Speaker 6: that's a lot of money, but for me, thinking about

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Speaker 6: all the findings you're going to get I think it's

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Speaker 6: well worth.

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Speaker 2: It, all right. So so there you have someone and

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Speaker 2: you know, this was not hard to find a clip

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Speaker 2: like this. There are hundreds of them out there, of

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Speaker 2: people who have gone through these direct to consumer scans.

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Speaker 2: These are things that you pay for out of pocket

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Speaker 2: and you know, find something, and broadly speaking, the people

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Speaker 2: seem quite grateful that they've found a thing.

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Speaker 1: Yeah, ah, this is a really okay. So let me

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Speaker 1: just tee up Why I think this space is so

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Speaker 1: complicated because these scans are on a long continuum, some

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Speaker 1: of which we recommend, and so you will often hear people,

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Speaker 1: you know, doctors say like, well, don't you don't need

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Speaker 1: a full body scan? Like you don't you don't need

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Speaker 1: a full body scan? But then in the same breath

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Speaker 1: it'll be like, but definitely get this, mamma rep and

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Speaker 1: definitely get this cooling cancer screening can definitely get. And

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Speaker 1: I think that's where it's hard to navigate. It's, well,

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Speaker 1: you told me these scans I want. Why don't I

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Speaker 1: want this other scan? It just seems like it would

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Speaker 1: be better.

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Speaker 2: Isn't more information?

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Speaker 1: Is it more information better? And there's a sense in

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Speaker 1: which the answer must be yes, and then other senses

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Speaker 1: in which the answer must be no. But before we

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Speaker 1: get into that, you know, there are a lot of

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Speaker 1: these scans. The one I think, my guess is people

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Speaker 1: have heard most frequently is a company called Prinovo, which

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Speaker 1: does a full body MRI, sends it to a radiologist

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Speaker 1: and probably some AI to read it. These cost something

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Speaker 1: in the range of you onenty to twenty five hundred

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Speaker 1: dollars and they come back with some information about what

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Speaker 1: to do, and then you maybe have to follow up

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Speaker 1: in some other way. So that's the most popular one.

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Speaker 2: There are a bunch of popularized by no less than

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Speaker 2: Kim Kardashian, who got some significant flack actually for promoting Pernuvo.

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Speaker 2: We can talk about this in a little bit, but

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Speaker 2: but Kim Kardashian got one of these scans. By the way,

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Speaker 2: they Pernuvo often gives these scans to celebrities and influencers.

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Speaker 2: So if you look at the.

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Speaker 1: No one has offered me any oh, I still haven't

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Speaker 1: gotten my graphics card from episode one or two either.

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Speaker 1: Come on, we're waiting, guys.

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Speaker 3: Guys.

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Speaker 2: So Pernuvo will give these scans to influencers and then

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Speaker 2: you know, they'll put something on Instagram and with a

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Speaker 2: little with a little discount code and stuff like that.

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Speaker 2: This is clearly part of their marketing strategy. Kim Kardashian

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Speaker 2: had a pernevo scan they found aneurysm in her brain

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Speaker 2: that got a lot of attention. She talked about it

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Speaker 2: on her reality show. I'm going to hold off for

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Speaker 2: a second to tell you what the like end of

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Speaker 2: that story is. Uh, but I just want to say that, like,

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Speaker 2: this is something that you will hear a lot about

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Speaker 2: from really famous people.

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Speaker 1: Yeah, so there are a bunch of companies that do this.

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Speaker 1: I think one one thing I wanted to kind of

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Speaker 1: in some ways differentiate a little bit is there is

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Speaker 1: another kind of whole body scan called a dexa scan,

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Speaker 1: which people will hear about, which is basically a way

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Speaker 1: to scan how much body fat and muscle you have.

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Speaker 1: These are much cheaper and they are a totally different thing.

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Speaker 1: So I sort of in the space of influencers, these

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Speaker 1: things will get kind of put on top of each other,

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Speaker 1: but they aren't the same thing.

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Speaker 6: Here.

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Speaker 1: We're really talking about these like full body either CT

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Speaker 1: or MRI scans designed to look for things like tumors

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Speaker 1: or aneurysms or other abnormalities of your body as opposed

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Speaker 1: to looking at your body fat percentage.

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Speaker 2: Right, this is really a build as a broad sweep,

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Speaker 2: and we have companies in addition to Pernuvo. People may

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Speaker 2: have heard of Ezra or True Scan or the Simon

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Speaker 2: one scan, or there's even some ct versions. There is

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Speaker 2: one that I want to address just because it's like

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Speaker 2: exploding on social media just over the last week. So, Emily,

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Speaker 2: what if I were to submerge you in water and

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Speaker 2: shoot ultrasound waves through your body? Does that seem like

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Speaker 2: a good idea?

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Speaker 1: Yes, yes, it sounds amazing, is like a massage.

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Speaker 2: This just so people, this is the new mid Journey scan,

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Speaker 2: which is like the tech bros are are over the

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Speaker 2: moonflo mid Journey is the company that did like AI

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Speaker 2: image generation.

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Speaker 7: Yeah, like that was the big what I'm familiar Now

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Speaker 7: they're a full body scanning company, Pivoting, and they've showed

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Speaker 7: a demo of like you get submerged in water and

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Speaker 7: this thing goes like and you get a whole scan

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Speaker 7: of your body using ultrasound.

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Speaker 1: Waves and then what how does that help?

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Speaker 8: What?

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Speaker 2: Well, it's the similar idea to the full body MRI scans,

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Speaker 2: except they they're arguing it'll be cheaper and we can

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Speaker 2: sort of get to the costs in a minute. Uh

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Speaker 2: and and and the problems with full body ultrasound as well.

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Speaker 2: I just want to say it at the top because

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Speaker 2: I think people will start hearing about this and and

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Speaker 2: we will touch on it. But frankly, the data is

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Speaker 2: all in that full body MRI scan space. This full

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Speaker 2: body ulchstund is completely new, and like there's very little

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Speaker 2: we can say about its diagnostic accuracy or anything because

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Speaker 2: it hasn't been really used on humans yet, but you'll

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Speaker 2: see it in your feeds.

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Speaker 1: How much do you think that the AI, the growth

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Speaker 1: of AI is powering this. I mean a radiology is

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Speaker 1: one of the things that AI is sort of the

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Speaker 1: best at, the best kind of doctoring on. I would

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Speaker 1: have thought this is like we're only able to do

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Speaker 1: this because AI is reading a huge share of these scans.

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Speaker 2: In almost all of these companies, AI is built as

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Speaker 2: augmenting the read from the radiologists. No one is saying

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Speaker 2: like it's only AI.

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Speaker 1: You look skeptical, No, I think that's I mean, that

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Speaker 1: is the way that radiology I think will work for everything,

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Speaker 1: is that the AI does a first read and somebody

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Speaker 1: does so as a follow up read, we have actually

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Speaker 1: quite a significant radiologist shortage and the moment, and so

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Speaker 1: this is a natural place for AI to come in,

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Speaker 1: including for these kind of scans.

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Speaker 2: Yeah, and if you're one of these companies trying to

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Speaker 2: raise money from investors, like, are you not going to

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Speaker 2: say the AI AI?

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Speaker 1: It's like everybody's an AI company a meal delivery service.

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Speaker 1: It's more of an AI meal delivery AI stake. You know,

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Speaker 1: we're optimized AI.

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Speaker 2: It's all that I I Okay, total aside, But my

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Speaker 2: dystopian prediction for the future of AI and consumerism is

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Speaker 2: that soon enough, Amazon is actually going to start sending

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Speaker 2: things to your house that you haven't ordered. That it's

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Speaker 2: going to just like it's going to show up and

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Speaker 2: then they'll be like, look, look, send it back if

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Speaker 2: you don't if you don't want it.

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Speaker 1: That already happens in my house because it's one of

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Speaker 1: my children has ordered it. But it's going to be

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Speaker 1: even worse when Amazon is doing it.

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Speaker 2: Yeah, all right, mark my words.

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Speaker 1: Okay, So before we get into the like what do

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Speaker 1: we know about these things in the data? I actually

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Speaker 1: want to start a little bit bigger picture with how

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Speaker 1: we think about scan and how we think about results

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Speaker 1: that we get in medicine. So I'm going to give

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Speaker 1: you a quiz. It's a quiz for everybody, So everybody

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Speaker 1: put your thinking cap on. Imagine you have a disease

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Speaker 1: that affects one in ten thousand people. Okay, and you

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Speaker 1: have a test for the disease that detects ninety nine

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Speaker 1: percent of cases with a two percent false positive rate.

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Speaker 1: So it's really good test. It finds ninety nine percent

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Speaker 1: of cases. Small false positive rate of two percent.

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Speaker 2: Amazing. Tests sounds great.

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Speaker 1: You give someone the test and they test positive, what

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Speaker 1: is the chance that they have the disease? Short jeopardy pause,

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Speaker 1: do you do?

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Speaker 9: Do you do?

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Speaker 1: Okay, what do you think?

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Speaker 2: I think that all your bays are belonged to us.

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Speaker 2: I know what you're getting at here, But let me

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Speaker 2: tell you what I think your intuition is, and then

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Speaker 2: we can talk about why the intuition is wrong. The

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Speaker 2: intuition is, here's a test that detects ninety nine percent

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Speaker 2: of disease. Great, only has a two percent false positive rate. Great,

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Speaker 2: you got a positive test. Pretty sure you have the disease?

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Speaker 1: Pretty sure you have.

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Speaker 2: That sounds right, yeahad bad news. Right, you have the

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Speaker 2: positive test, but you are you mentioned that this is

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Speaker 2: a pretty rare disease, one in ten thousand, and so

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Speaker 2: maybe you can walk us through the math Emily of

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Speaker 2: like what the actual value of a positive test is here.

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Speaker 1: So the answer to the question is one half of

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Speaker 1: one percent. So with a positive test, the chance that

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Speaker 1: you actually have the disease is one in two hundred.

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Speaker 1: And the reason for that is that if you think

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Speaker 1: about this test, Let's say you have ten thousand people,

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Speaker 1: you test, one of them has it, You're going to

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Speaker 1: find that the very very good chance you find out

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Speaker 1: ninety nine percent chance you ninety nine percent chance. Let's

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Speaker 1: say we find that one person, and then there's a

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Speaker 1: two percent false positive rate, and that means of the

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Speaker 1: ninety nine people who don't have it, about two hundred

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Speaker 1: of them are going to test positive. And so now

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Speaker 1: you've got two hundred people who tested positive who don't

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Speaker 1: have it, and one person who tested positive who does

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Speaker 1: and that means that it's about one in two hundred.

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Speaker 1: That's the chance that you actually are affected. And so

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Speaker 1: if you're sitting there with a positive if you're sitting

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Speaker 1: there with a positive test, so I think this will

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Speaker 1: really like. I have given this test to many people,

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Speaker 1: ranging from middle school students to members of the Ivy

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Speaker 1: League corporation, and almost nobody gets it right. And in fact,

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Speaker 1: when you get this to doctors, they also mostly don't

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Speaker 1: get it right. I guess you're an exception, and a

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Speaker 1: lot of people have the instinct it's like ninety five percent.

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Speaker 1: The reason that I say this is that one of

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Speaker 1: the most significant issues with this kind of full body

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Speaker 1: scan and by the way, with any kind of scanning

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Speaker 1: for conditions, is that many of these conditions are pretty

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Speaker 1: rare and false positives are pretty calm. So when you

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Speaker 1: go to scan people for something like breast cancer, even

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Speaker 1: with the standard mammogram, there's a lot of false positives,

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Speaker 1: a lot of incidental findings, a lot of just we

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Speaker 1: saw something on the mammogram and then when we went

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Speaker 1: more into it, it it wasn't. It wasn't in there.

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Speaker 1: And that's important for two reasons. One is that if

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Speaker 1: you do more of this scanning, you're just going to

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Speaker 1: have more of these false positives. Now you'll have more

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Speaker 1: true positives also, but you will end up with a

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Speaker 1: lot of people either very anxious or just having a

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Speaker 1: lot of additional testing that they didn't necessarily need. And

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Speaker 1: when we think about these tests, we want to weigh

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Speaker 1: those things. And full body scans are an extreme form

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Speaker 1: of a problem that is already there with any kind

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Speaker 1: of scanning. So we already think about this problem with

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Speaker 1: breast cancer screening or colonas could be, or prostive cancer

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Speaker 1: screening or whatever. That's already an issue. If you're now

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Speaker 1: scanning somebody's entire body and you'll picking up every you know,

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Speaker 1: weird rando thing in their elbow, you're gonna have this

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Speaker 1: problem even more extreme.

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Speaker 2: So this is this is so important, but it's also

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Speaker 2: going to play into the central tension that I have

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Speaker 2: with these scans, and we'll get there, But broadly speaking,

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Speaker 2: there's always going to be this thing that like, we

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Speaker 2: can look objectively at the numbers on a population level

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Speaker 2: and be like, okay, yeah, you know, there's a lot

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Speaker 2: of false positives and people are going to be chasing

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Speaker 2: this down and seeing the doctor and stuff. But there

438
00:23:23,200 --> 00:23:25,320
Speaker 2: is that one guy, what if it was you, There's

439
00:23:25,320 --> 00:23:27,480
Speaker 2: that one guy you said who actually had that rare

440
00:23:27,520 --> 00:23:33,600
Speaker 2: disease that we caught, and there is clearly some value

441
00:23:33,840 --> 00:23:37,120
Speaker 2: in catching that one guy. But the question is sort

442
00:23:37,119 --> 00:23:41,360
Speaker 2: of how many false positives is worth the true positive?

443
00:23:41,400 --> 00:23:44,480
Speaker 2: And this is where I like get stuck on full

444
00:23:44,480 --> 00:23:45,000
Speaker 2: body scans.

445
00:23:45,080 --> 00:23:46,960
Speaker 1: And I think that's a part of the reason it's

446
00:23:46,960 --> 00:23:51,560
Speaker 1: a very complicated question is that it's probably different if

447
00:23:51,560 --> 00:23:55,240
Speaker 1: we think about what the individual incentive is versus the

448
00:23:55,640 --> 00:23:59,560
Speaker 1: kind of health system incentive. So when we make decisions about,

449
00:23:59,600 --> 00:24:03,200
Speaker 1: you know, who should we recommend a mammography for, we're

450
00:24:03,200 --> 00:24:06,320
Speaker 1: making decisions based on trading off false positives and true

451
00:24:06,359 --> 00:24:11,359
Speaker 1: positives from a kind of like population cost perspective, which

452
00:24:11,480 --> 00:24:14,399
Speaker 1: may be the right frame for some decisions, but maybe

453
00:24:14,440 --> 00:24:16,720
Speaker 1: different from you as an individual, And what do you

454
00:24:16,840 --> 00:24:19,080
Speaker 1: you know, what what is sort of your risk tolerance

455
00:24:19,080 --> 00:24:21,000
Speaker 1: and what is your demand? And I will tell you

456
00:24:21,040 --> 00:24:27,600
Speaker 1: from a personal level. When my mom was diagnosed with

457
00:24:27,720 --> 00:24:30,320
Speaker 1: lung cancer, it was an it was an incidental finding

458
00:24:30,440 --> 00:24:33,879
Speaker 1: on a CT scan that was being done for some

459
00:24:34,000 --> 00:24:36,280
Speaker 1: other reason. She had vertigo, They thought she might have

460
00:24:36,320 --> 00:24:39,399
Speaker 1: a stroke. It turned out they found lung cancer. She

461
00:24:39,640 --> 00:24:41,680
Speaker 1: was in a risk group because she was a former

462
00:24:41,720 --> 00:24:47,000
Speaker 1: smoker who like could have been going for yearly CT scans.

463
00:24:47,560 --> 00:24:50,119
Speaker 1: And there's a lot of debate about exactly who should

464
00:24:50,160 --> 00:24:52,919
Speaker 1: be doing those, and I think all the time, like

465
00:24:53,040 --> 00:24:56,080
Speaker 1: boy ex post, I really wish that she had she

466
00:24:56,200 --> 00:24:58,360
Speaker 1: had been doing that, even if it you know, from

467
00:24:58,400 --> 00:25:01,640
Speaker 1: a kind of population standpoint, maybe that isn't that isn't

468
00:25:01,640 --> 00:25:04,760
Speaker 1: the right thing. So for me, that really crystallizes this

469
00:25:05,080 --> 00:25:08,200
Speaker 1: trade off between yeah, what about the one that you missed?

470
00:25:08,640 --> 00:25:12,280
Speaker 2: Yeah, one hundred percent. So there are a couple of

471
00:25:12,320 --> 00:25:15,240
Speaker 2: concepts that I want people to be aware of. You've

472
00:25:15,480 --> 00:25:19,320
Speaker 2: hit on one, which is the false positive thing. In medicine,

473
00:25:19,320 --> 00:25:21,040
Speaker 2: we call these incidental omas.

474
00:25:21,520 --> 00:25:23,560
Speaker 1: So I just give things regular names.

475
00:25:23,760 --> 00:25:26,600
Speaker 2: I could, you know, because we like to have jargons.

476
00:25:26,600 --> 00:25:34,120
Speaker 10: So that OSMA, this was originally named after these things

477
00:25:34,160 --> 00:25:36,280
Speaker 10: you see on adrenal glands when you get a cat

478
00:25:36,359 --> 00:25:37,080
Speaker 10: scan of the abdomen.

479
00:25:37,160 --> 00:25:38,960
Speaker 2: So we give people cat skins of the abdomen for

480
00:25:39,119 --> 00:25:41,600
Speaker 2: you know everything, right, you come in with abdominal pain

481
00:25:41,640 --> 00:25:42,959
Speaker 2: to the E D whatever, You're getting a cats scan

482
00:25:43,000 --> 00:25:46,320
Speaker 2: your abdue pelvis, and then you know, not infrequently you

483
00:25:46,359 --> 00:25:49,280
Speaker 2: see these little spots on an adrenal gland. We typically

484
00:25:49,320 --> 00:25:52,679
Speaker 2: refer to them as incidental omas. They were found incidentally.

485
00:25:52,680 --> 00:25:56,760
Speaker 2: You weren't looking for them, but they tend to necessitate

486
00:25:56,840 --> 00:25:59,880
Speaker 2: at least some further work up and and and down

487
00:26:00,080 --> 00:26:02,960
Speaker 2: stream stuff, and that can happen anywhere in the body.

488
00:26:03,600 --> 00:26:08,000
Speaker 2: Incidental omas are things that turn out to be nothing.

489
00:26:08,320 --> 00:26:11,600
Speaker 2: So you find them and then you've got to do

490
00:26:11,680 --> 00:26:14,960
Speaker 2: something about it, But then it turns out it didn't matter, right,

491
00:26:15,080 --> 00:26:17,119
Speaker 2: Like you buy up see that thing on your adrenal

492
00:26:17,240 --> 00:26:19,960
Speaker 2: land and it was a benign It was a benign growth.

493
00:26:19,960 --> 00:26:23,160
Speaker 2: It would never have hurt you. It's an incidental oma.

494
00:26:23,800 --> 00:26:26,320
Speaker 2: There's a related concept which people have a little more

495
00:26:26,320 --> 00:26:29,960
Speaker 2: trouble with called over diagnosis, which is when you diagnose

496
00:26:30,040 --> 00:26:33,920
Speaker 2: something that really is bad, like a cancer or something

497
00:26:34,000 --> 00:26:37,680
Speaker 2: like that, but it turns out that had you not

498
00:26:37,800 --> 00:26:40,920
Speaker 2: diagnosed it, it probably wouldn't have hurt you at all,

499
00:26:41,080 --> 00:26:43,680
Speaker 2: because you were going to die of something else before

500
00:26:44,000 --> 00:26:47,840
Speaker 2: this got to you at all. So overdiagnosis is sort

501
00:26:47,840 --> 00:26:51,000
Speaker 2: of a different thing from an incidental oma or a

502
00:26:51,040 --> 00:26:54,040
Speaker 2: false positive. To give you a sense. It's hard to

503
00:26:54,040 --> 00:26:56,560
Speaker 2: get it like some of this stuff, but there are

504
00:26:56,640 --> 00:27:00,359
Speaker 2: autopsy studies where we'll look at people who died something

505
00:27:00,359 --> 00:27:02,639
Speaker 2: else and ask a question, for example of like, okay,

506
00:27:02,920 --> 00:27:05,760
Speaker 2: you died of not a cancer related problem. We do

507
00:27:05,800 --> 00:27:08,840
Speaker 2: an autopsy, how often do we find cancer? And the

508
00:27:08,880 --> 00:27:13,399
Speaker 2: answer is pretty often, Yeah, five percent of the time.

509
00:27:13,640 --> 00:27:17,240
Speaker 2: There's a nice study of eight hundred thousand non cancer

510
00:27:17,320 --> 00:27:21,040
Speaker 2: autopsies in Japan where the overall cancer rate was four

511
00:27:21,040 --> 00:27:22,920
Speaker 2: point two percent. I will say it's a little higher

512
00:27:22,920 --> 00:27:25,560
Speaker 2: in recent years now it's like up to seven percent overall.

513
00:27:25,600 --> 00:27:27,640
Speaker 2: And then of course the older the person was when

514
00:27:27,640 --> 00:27:30,800
Speaker 2: they died, the higher the chance of finding an incidental

515
00:27:30,800 --> 00:27:33,679
Speaker 2: cancer was. This isn't a false positive, like had you

516
00:27:33,760 --> 00:27:36,800
Speaker 2: found that while they were alive and biopsied it, it

517
00:27:36,840 --> 00:27:39,440
Speaker 2: would have come back cancer. They would have gotten cancer treatment,

518
00:27:39,840 --> 00:27:42,639
Speaker 2: but they didn't die of that, so all of that

519
00:27:42,680 --> 00:27:46,800
Speaker 2: treatment was theoretically over diagnosis. So that's the second major concept.

520
00:27:47,520 --> 00:27:49,000
Speaker 1: Yeah, and I think this is I mean, this is

521
00:27:49,000 --> 00:27:51,679
Speaker 1: where you know, for men in particular, a lot of

522
00:27:51,720 --> 00:27:54,399
Speaker 1: men get prostate cancer before they're dead, and at some

523
00:27:54,480 --> 00:27:58,080
Speaker 1: point we almost everyone, almost everybody agains, and at some

524
00:27:58,160 --> 00:28:01,240
Speaker 1: point it makes sense to just stop greening for prostate cancer,

525
00:28:01,240 --> 00:28:03,880
Speaker 1: because if you're ninety three years old and we find

526
00:28:03,880 --> 00:28:07,680
Speaker 1: out you have prosta cancer, it's fine, like something else

527
00:28:07,760 --> 00:28:09,720
Speaker 1: is going to take you before the prostate cancer. For

528
00:28:09,760 --> 00:28:12,639
Speaker 1: almost everybody, and so that is that is in a

529
00:28:12,720 --> 00:28:15,399
Speaker 1: space of overdiagnosis. Again, it's not that it's it's not

530
00:28:15,440 --> 00:28:18,760
Speaker 1: that it's not cancer. It's just not we care about

531
00:28:18,840 --> 00:28:21,160
Speaker 1: the death, not the specific cause of death.

532
00:28:21,280 --> 00:28:23,840
Speaker 2: Yeah, so this brings me back to Kim.

533
00:28:23,760 --> 00:28:27,159
Speaker 1: Ki, Kim k Kim k what's to stay up to?

534
00:28:27,600 --> 00:28:30,960
Speaker 2: So, so what Kim Kardashian told the world, I mean,

535
00:28:31,000 --> 00:28:33,240
Speaker 2: we don't have her report, her Pernuva report, but what

536
00:28:33,280 --> 00:28:36,600
Speaker 2: she what she said publicly is that they found a

537
00:28:36,680 --> 00:28:39,760
Speaker 2: little or small aneurism in her brain, as well as

538
00:28:39,760 --> 00:28:44,440
Speaker 2: some uterine fibroids. She said that, you know, after that

539
00:28:44,520 --> 00:28:46,360
Speaker 2: Pernuva found the little aneurism in her brain, she went

540
00:28:46,360 --> 00:28:49,320
Speaker 2: to see her SINAI. She got quote tons of brain scans,

541
00:28:49,720 --> 00:28:52,440
Speaker 2: met with a bunch of neurosurgeons, et cetera, et cetera,

542
00:28:53,320 --> 00:28:57,280
Speaker 2: and decided to do nothing. It was small enough that

543
00:28:57,320 --> 00:29:00,640
Speaker 2: they decided to do this thing called watchful waiting. That's

544
00:29:00,760 --> 00:29:05,000
Speaker 2: probably the right choice talking about overdiagnosis of aneurysms. If

545
00:29:05,040 --> 00:29:07,680
Speaker 2: you look at the rate of brain incidental brain aneurysms

546
00:29:07,720 --> 00:29:09,840
Speaker 2: at autopsy. Okay, so you take people who did not

547
00:29:09,920 --> 00:29:12,120
Speaker 2: die of a brain aneurism, but you look at their

548
00:29:12,160 --> 00:29:16,120
Speaker 2: brain after they died. Two to six percent have an

549
00:29:16,080 --> 00:29:19,120
Speaker 2: aneurysm in there, so they are there and a lot

550
00:29:19,160 --> 00:29:24,640
Speaker 2: of them don't rupture. And Kim Kardashian's surgeons looked at

551
00:29:24,640 --> 00:29:26,840
Speaker 2: the size and said, well, this is something we can

552
00:29:26,920 --> 00:29:29,840
Speaker 2: keep an eye on. Now. She build this as like

553
00:29:30,640 --> 00:29:34,240
Speaker 2: a life saving thing, like she has talked about this

554
00:29:34,480 --> 00:29:37,240
Speaker 2: as she is so thankful to know about this. She's

555
00:29:37,280 --> 00:29:40,080
Speaker 2: reducing the stress in her life to know potentially reduce

556
00:29:40,080 --> 00:29:42,000
Speaker 2: the risk of this aneurysm bursting.

557
00:29:42,440 --> 00:29:46,720
Speaker 1: I would find this so awful, Like I mean, I

558
00:29:46,880 --> 00:29:49,240
Speaker 1: feel like, at least for some people, and I would

559
00:29:49,240 --> 00:29:53,080
Speaker 1: put myself in this camp. The idea that I have

560
00:29:53,160 --> 00:29:56,840
Speaker 1: this aneurism but I'm doing nothing about it is so

561
00:29:57,000 --> 00:30:00,320
Speaker 1: much worse than not knowing, because in every time you

562
00:30:00,360 --> 00:30:02,480
Speaker 1: have a headache, you're like, oh my God, is in miamirism?

563
00:30:02,840 --> 00:30:02,959
Speaker 3: Right?

564
00:30:03,360 --> 00:30:05,640
Speaker 1: And this is a big part of this is people

565
00:30:05,920 --> 00:30:10,920
Speaker 1: just being anxious about what might happen because the information

566
00:30:11,080 --> 00:30:14,200
Speaker 1: is because we're not robots and we can't like just

567
00:30:14,240 --> 00:30:15,800
Speaker 1: pretending information is not there.

568
00:30:15,920 --> 00:30:19,440
Speaker 2: And I don't know, yeah, and so much of what

569
00:30:19,640 --> 00:30:22,400
Speaker 2: I see when and it's mostly doctors online who are

570
00:30:22,400 --> 00:30:24,920
Speaker 2: pushing back against full body skins. It's mostly like people

571
00:30:25,040 --> 00:30:28,320
Speaker 2: my profession that's like, guy's careful, false positive and all

572
00:30:28,320 --> 00:30:30,360
Speaker 2: this kind of thing. And then there's a lot of

573
00:30:30,400 --> 00:30:34,360
Speaker 2: sort of wellness tech people who are like, no, no, no,

574
00:30:34,560 --> 00:30:37,000
Speaker 2: you know, more information is better. I want to find

575
00:30:37,000 --> 00:30:39,959
Speaker 2: the thing before it becomes a problem. And hey, I

576
00:30:40,000 --> 00:30:42,040
Speaker 2: know there's you know, I know there's false positive. I

577
00:30:42,080 --> 00:30:45,600
Speaker 2: know there's over diagnosis. We'll figure that out over time,

578
00:30:45,680 --> 00:30:48,840
Speaker 2: we'll like learn how better to manage these things. And

579
00:30:49,120 --> 00:30:51,560
Speaker 2: that's the step where I'm like, you know, this is

580
00:30:51,560 --> 00:30:53,480
Speaker 2: going to require if you really want to deal with

581
00:30:53,560 --> 00:30:56,520
Speaker 2: a false positive issue, a lot of people to have

582
00:30:56,600 --> 00:30:59,760
Speaker 2: a spot on their liver, yeah, and to be like, well,

583
00:31:00,360 --> 00:31:03,160
Speaker 2: there's a one percent chance it's cancer or maybe a

584
00:31:03,200 --> 00:31:05,120
Speaker 2: three per Let's say there's a five percent chance it's cancer.

585
00:31:05,160 --> 00:31:07,440
Speaker 2: There's a ninety five percent chance it's not. I'm not

586
00:31:07,480 --> 00:31:09,880
Speaker 2: going to do anything because like, chances are it's fine.

587
00:31:10,320 --> 00:31:12,120
Speaker 2: There's some people who can handle that. There are a

588
00:31:12,120 --> 00:31:14,920
Speaker 2: lot of people who can't. Right, Like, the whole reason

589
00:31:14,960 --> 00:31:16,640
Speaker 2: they did the scan was to find the spot on

590
00:31:16,640 --> 00:31:18,000
Speaker 2: the liver. Now it needs a biopsy.

591
00:31:18,320 --> 00:31:21,400
Speaker 1: Yeah, And I think we see this in you know,

592
00:31:21,600 --> 00:31:24,840
Speaker 1: in outside of the full body scan space in you know,

593
00:31:25,000 --> 00:31:29,040
Speaker 1: in in mimography. So our mamogram approtion mamorrams gotten better

594
00:31:29,080 --> 00:31:32,040
Speaker 1: over time. We're doing more mammograms broadly, that's very good.

595
00:31:32,080 --> 00:31:34,959
Speaker 1: It means we're catching cancers earlier. But there's actually a

596
00:31:35,120 --> 00:31:37,720
Speaker 1: fairly large or at least some set of kind of

597
00:31:37,720 --> 00:31:40,920
Speaker 1: stage zero cancers for which it may be based on

598
00:31:40,960 --> 00:31:42,400
Speaker 1: the age of the person or what it looks like,

599
00:31:42,480 --> 00:31:46,280
Speaker 1: you just shouldn't do anything. And we are almost always

600
00:31:46,280 --> 00:31:48,960
Speaker 1: doing things. So typically people will be given the choice,

601
00:31:48,960 --> 00:31:50,880
Speaker 1: you know, we could do watchful waiting or we could

602
00:31:51,120 --> 00:31:53,400
Speaker 1: do stuff. And people want to do stuff once they

603
00:31:53,440 --> 00:31:56,960
Speaker 1: say you have you know, ductal blah blah blah with

604
00:31:57,000 --> 00:32:00,240
Speaker 1: the word car sonoma on it, you're doing stuff, And

605
00:32:00,360 --> 00:32:05,080
Speaker 1: that is maybe a good idea, but it does it

606
00:32:05,120 --> 00:32:07,680
Speaker 1: does have trade offs. I think it's just not it's

607
00:32:07,760 --> 00:32:10,440
Speaker 1: not free for you or for money. I mean, it's

608
00:32:10,480 --> 00:32:12,240
Speaker 1: not free in terms of money, but it's not free

609
00:32:12,280 --> 00:32:14,000
Speaker 1: in terms of your emotional state either.

610
00:32:14,800 --> 00:32:18,080
Speaker 2: Yeah. I want to walk through the sort of third

611
00:32:18,200 --> 00:32:22,400
Speaker 2: large concept which we've we've tested on here, which is

612
00:32:22,480 --> 00:32:23,960
Speaker 2: called care cascades.

613
00:32:24,360 --> 00:32:26,080
Speaker 1: Can I say before we get to that, can I

614
00:32:26,120 --> 00:32:29,160
Speaker 1: can I say, sort of something else that happens in here.

615
00:32:29,160 --> 00:32:32,600
Speaker 1: That where I think this intersects with some of the

616
00:32:32,640 --> 00:32:35,880
Speaker 1: way we're doing medicine these days, which is it is

617
00:32:36,000 --> 00:32:39,360
Speaker 1: much more common for people to do like a share

618
00:32:39,400 --> 00:32:42,160
Speaker 1: what do we call shared decision making, and shared decision

619
00:32:42,200 --> 00:32:44,520
Speaker 1: making is great because it puts the patient in the

620
00:32:44,600 --> 00:32:47,200
Speaker 1: driver's seat at some of the time, and we generally

621
00:32:47,280 --> 00:32:50,360
Speaker 1: don't want the kind of nineteen fifties doctor where it

622
00:32:50,400 --> 00:32:52,160
Speaker 1: was like he says there and smokes a cigarette and

623
00:32:52,200 --> 00:32:55,080
Speaker 1: tells you you know what to do, like we've moved away,

624
00:32:55,120 --> 00:32:57,640
Speaker 1: and that's yeah, that's probably for the best, especially with

625
00:32:57,920 --> 00:32:58,200
Speaker 1: we're not.

626
00:32:58,200 --> 00:33:01,280
Speaker 2: Going to tell your wife she has cancer just well.

627
00:33:01,360 --> 00:33:05,719
Speaker 1: It'll just make her sad. But the idea that the

628
00:33:05,760 --> 00:33:08,200
Speaker 1: patient should be in the driver's seat around some of

629
00:33:08,240 --> 00:33:13,240
Speaker 1: these decisions is very stressful for people, and I somehow

630
00:33:13,280 --> 00:33:15,360
Speaker 1: it comes up more in these cases where we're just

631
00:33:15,400 --> 00:33:19,880
Speaker 1: not sure what to do, and that piece of it

632
00:33:19,920 --> 00:33:23,160
Speaker 1: feels like, I'm not I'm not sure there's a good

633
00:33:23,160 --> 00:33:25,240
Speaker 1: solution to it, but I think it's yet another piece

634
00:33:25,280 --> 00:33:26,320
Speaker 1: adding to this anxiety.

635
00:33:26,640 --> 00:33:29,600
Speaker 2: Yeah, it varies obviously based on the patient. Some patients

636
00:33:29,680 --> 00:33:31,200
Speaker 2: very much want to be told what to do. It's

637
00:33:31,240 --> 00:33:33,360
Speaker 2: my experience, and they'll phrase that in different ways, but

638
00:33:33,400 --> 00:33:35,640
Speaker 2: often it comes to me as like, Okay, if I

639
00:33:35,680 --> 00:33:37,480
Speaker 2: were your brother or if I were your father, what

640
00:33:37,520 --> 00:33:39,840
Speaker 2: would you tell me to do here? Which is always

641
00:33:39,880 --> 00:33:41,720
Speaker 2: a hard question for me. I'm like, no, I would

642
00:33:41,720 --> 00:33:46,080
Speaker 2: never tell my own family because I'm completely completely biased.

643
00:33:46,600 --> 00:33:49,400
Speaker 2: And then some yeah, some have very strong opinions. And

644
00:33:49,440 --> 00:33:52,440
Speaker 2: then I mean, just to say it as doctor like,

645
00:33:52,560 --> 00:33:56,560
Speaker 2: there are medical legal implications of this stuff, right, someone

646
00:33:56,560 --> 00:33:58,959
Speaker 2: comes to me after a full body scan and says,

647
00:33:59,320 --> 00:34:04,080
Speaker 2: you know, hey, they found this thing on my you know,

648
00:34:04,600 --> 00:34:07,880
Speaker 2: on my kidney. It's ambiguous what it is. Could be cancer,

649
00:34:08,320 --> 00:34:10,520
Speaker 2: might not be. What do you think I should do?

650
00:34:10,680 --> 00:34:12,560
Speaker 2: There's definitely a part of my brain that's like, well,

651
00:34:12,760 --> 00:34:16,359
Speaker 2: you know, if I don't work this up, if I'm

652
00:34:16,400 --> 00:34:18,879
Speaker 2: like it is cancer, what if it is cancer five

653
00:34:18,960 --> 00:34:22,120
Speaker 2: years from now? And and you know, so that's just

654
00:34:22,200 --> 00:34:25,640
Speaker 2: sort of the way we're structured. It's much safer for me,

655
00:34:25,840 --> 00:34:27,480
Speaker 2: in a cover your ass kind of sense, to be like,

656
00:34:27,520 --> 00:34:30,160
Speaker 2: all right, well, let's get you a dedicated scan of that,

657
00:34:30,280 --> 00:34:32,000
Speaker 2: let's get a biopsy of that, and so on and

658
00:34:32,000 --> 00:34:35,040
Speaker 2: so forth, And that is what happens. So a lot

659
00:34:35,080 --> 00:34:37,359
Speaker 2: of these arguments that's like, oh, guys, we just it's

660
00:34:37,400 --> 00:34:39,879
Speaker 2: not the scans that are the problem, it's the psychology

661
00:34:39,880 --> 00:34:41,600
Speaker 2: around them, and we have to get better at dealing

662
00:34:41,640 --> 00:34:44,880
Speaker 2: with ambiguity. Is a nice theory, but we're just not

663
00:34:45,000 --> 00:34:45,640
Speaker 2: very good at it.

664
00:34:45,760 --> 00:34:47,520
Speaker 1: No, we're just not. It's a nice theory, but not

665
00:34:47,960 --> 00:34:48,920
Speaker 1: very realistic.

666
00:34:49,200 --> 00:34:53,560
Speaker 2: Yeah, all right. A guy walked into a routine physical

667
00:34:53,880 --> 00:34:57,440
Speaker 2: feeling completely fine. He walked out ten units of blood

668
00:34:57,480 --> 00:35:00,520
Speaker 2: and fifty thousand dollars later, and he never acts had

669
00:35:00,600 --> 00:35:02,640
Speaker 2: anything wrong with him.

670
00:35:03,239 --> 00:35:05,720
Speaker 1: This is great, not great.

671
00:35:06,320 --> 00:35:10,920
Speaker 2: This is such a great piece of writing from Michael Rothberg,

672
00:35:11,320 --> 00:35:14,319
Speaker 2: who is writing in jama about the true experience of

673
00:35:14,360 --> 00:35:22,040
Speaker 2: his father and how medicine obligates subsequent testing. So let

674
00:35:22,080 --> 00:35:23,640
Speaker 2: me tell you what happened to this person and get

675
00:35:23,640 --> 00:35:26,719
Speaker 2: your opinion. Okay, this older guy comes in, gets his

676
00:35:26,880 --> 00:35:30,319
Speaker 2: routine physical. His doctor does a physical exam, listens to

677
00:35:30,360 --> 00:35:33,239
Speaker 2: his heart, pushes on his belly, feels his a orda

678
00:35:33,440 --> 00:35:36,320
Speaker 2: an aortic exam. You've kind of pushed down the belly,

679
00:35:36,840 --> 00:35:39,080
Speaker 2: feel the pulse. The doctor thinks the order is a

680
00:35:39,080 --> 00:35:42,319
Speaker 2: little bit big. Maybe there's an aneurysm there, but there's

681
00:35:42,360 --> 00:35:44,799
Speaker 2: an appropriate test for that, that's an abdominal ultrasound. So

682
00:35:44,800 --> 00:35:47,360
Speaker 2: he sends him for an abdominal ultrasound. Turns out the

683
00:35:47,360 --> 00:35:50,120
Speaker 2: A order is normal, but during that ultrasound they see

684
00:35:50,120 --> 00:35:53,200
Speaker 2: something in the head of the pancreas. That's an incidental finding.

685
00:35:53,280 --> 00:35:54,759
Speaker 2: All right, Well, now there's something in the head of

686
00:35:54,760 --> 00:35:57,400
Speaker 2: the pancreas. So the next step is, of course to

687
00:35:57,400 --> 00:36:00,520
Speaker 2: get a CT scan. Well, good news. The T scans

688
00:36:00,560 --> 00:36:02,960
Speaker 2: showed the pancreas was normal, but there was a solitary

689
00:36:03,040 --> 00:36:06,960
Speaker 2: lesion on his liver. Okay, that needs to be biopsied,

690
00:36:07,040 --> 00:36:10,680
Speaker 2: so he gets a liver biopsy. It's not liver cancer,

691
00:36:11,040 --> 00:36:13,839
Speaker 2: that's good news, but it was a hamangioma, which led

692
00:36:13,960 --> 00:36:18,520
Speaker 2: to a huge bleed requiring a ten unit's blood transfusion

693
00:36:18,920 --> 00:36:23,880
Speaker 2: and an inpatient stay that cost fifty thousand dollars. Every

694
00:36:24,320 --> 00:36:29,399
Speaker 2: step of this care was appropriate. There was no malpractice here.

695
00:36:29,800 --> 00:36:32,680
Speaker 2: This is all exactly what you do. The only exception

696
00:36:33,000 --> 00:36:37,120
Speaker 2: actually is that initial physical exam palpating the A order.

697
00:36:37,200 --> 00:36:40,000
Speaker 2: There's no evidence that we're good enough at our physical

698
00:36:40,080 --> 00:36:42,960
Speaker 2: exam to tell if someone's order is dilated or not.

699
00:36:43,280 --> 00:36:46,759
Speaker 2: But like if your doctor does that, right, you're sort like, oh,

700
00:36:46,760 --> 00:36:48,279
Speaker 2: this is a good I mean, this is their job

701
00:36:48,280 --> 00:36:52,160
Speaker 2: they do, They're doing stuff. Listen. This is a care cascade,

702
00:36:52,200 --> 00:36:56,400
Speaker 2: albeit obviously one that's very dramatic. But I love this

703
00:36:56,480 --> 00:37:00,120
Speaker 2: story because it illustrates how like these aren't bad decisions.

704
00:37:00,160 --> 00:37:02,920
Speaker 2: Every step of this way was the answer to the

705
00:37:02,920 --> 00:37:05,080
Speaker 2: board question, like what do you do if you see

706
00:37:05,120 --> 00:37:06,879
Speaker 2: a mass in the head of the pancreas you get

707
00:37:06,880 --> 00:37:10,440
Speaker 2: a dedicated CT scan, Like that is all correct, but

708
00:37:10,520 --> 00:37:14,279
Speaker 2: it all started from a screening test. In this case,

709
00:37:14,280 --> 00:37:16,120
Speaker 2: it was a physical exam, not a full body scan.

710
00:37:17,840 --> 00:37:22,200
Speaker 1: Yeah, I mean, ah, that's obviously a very extreme story,

711
00:37:22,239 --> 00:37:27,320
Speaker 1: but I do think it is even in the Instagram

712
00:37:27,400 --> 00:37:29,200
Speaker 1: versions of this, it is what we're seeing from a

713
00:37:29,239 --> 00:37:31,799
Speaker 1: lot of people who have these sands. Do you see this?

714
00:37:31,880 --> 00:37:35,120
Speaker 1: You see this? You see you know this? Your liver

715
00:37:35,280 --> 00:37:37,520
Speaker 1: is too big? At least one person I know has

716
00:37:37,520 --> 00:37:39,799
Speaker 1: had these, Like they were like just like your liver

717
00:37:39,920 --> 00:37:41,960
Speaker 1: is enormous, Like you have an enormous liver, And it

718
00:37:42,000 --> 00:37:43,759
Speaker 1: led to all this stuff and it's turned out like

719
00:37:43,760 --> 00:37:45,480
Speaker 1: this person just had kind of a big liver, like

720
00:37:45,520 --> 00:37:51,799
Speaker 1: sometimes sometimes you have that, and so we're over diagnosing,

721
00:37:51,840 --> 00:37:55,600
Speaker 1: we're care caskating, we're just But again, it's not that

722
00:37:55,680 --> 00:37:58,040
Speaker 1: the decisions were wrong, And I think that's that's the

723
00:37:58,120 --> 00:38:00,000
Speaker 1: question is where do you stop the decisions? I think

724
00:38:00,040 --> 00:38:02,359
Speaker 1: that's what's such a hard piece of this is which

725
00:38:02,400 --> 00:38:04,560
Speaker 1: of these things do you not want to do? Which

726
00:38:04,600 --> 00:38:07,319
Speaker 1: piece of information do you want to just stop? And

727
00:38:07,360 --> 00:38:08,960
Speaker 1: then we're attempted to say, well, you know what, I

728
00:38:09,040 --> 00:38:11,719
Speaker 1: just shouldn't have gotten the information in the first place,

729
00:38:11,760 --> 00:38:13,799
Speaker 1: and then we wouldn't have been wouldn't have had all

730
00:38:13,800 --> 00:38:15,480
Speaker 1: these other problems. But then what have you missed the

731
00:38:15,480 --> 00:38:17,200
Speaker 1: one guy? That's why this is hard.

732
00:38:17,600 --> 00:38:20,720
Speaker 2: That's all right. So we've got the big concepts people,

733
00:38:20,840 --> 00:38:23,760
Speaker 2: all right, everyone understands now we have a visceral feeling

734
00:38:23,880 --> 00:38:27,400
Speaker 2: about about what the conceptually, what the issues are. I

735
00:38:27,400 --> 00:38:28,960
Speaker 2: think we need to talk about the data.

736
00:38:29,120 --> 00:38:38,320
Speaker 1: Yes, what happens when this actually occurs? I don't know. Okay,

737
00:38:38,400 --> 00:38:43,279
Speaker 1: So yeah, So let's say a thousand people get a

738
00:38:43,280 --> 00:38:47,400
Speaker 1: full body scan. Seventy of them, just seventy are going

739
00:38:47,440 --> 00:38:50,480
Speaker 1: to get totally normal results, nine hundred and thirty will

740
00:38:50,520 --> 00:38:54,279
Speaker 1: have something abnormal. Three hundred of them we'll need some

741
00:38:54,480 --> 00:38:57,319
Speaker 1: specific follow up twenty two will have cancer. Five will

742
00:38:57,360 --> 00:39:01,359
Speaker 1: have cancer that the scan missed. Anyway, that is a

743
00:39:01,400 --> 00:39:04,600
Speaker 1: broad summary of the data from these full body scans.

744
00:39:05,760 --> 00:39:08,320
Speaker 1: How do we feel about that? Doesn't seem amazing?

745
00:39:09,120 --> 00:39:12,719
Speaker 2: This crystallizes everything we've been saying to me. All right,

746
00:39:12,719 --> 00:39:13,920
Speaker 2: first of all, if you're going to get one of

747
00:39:13,920 --> 00:39:16,200
Speaker 2: these scans. This, by the way, this data comes from

748
00:39:16,239 --> 00:39:21,040
Speaker 2: the Polaris study, which is a Perneuvo funded study. This

749
00:39:21,760 --> 00:39:24,959
Speaker 2: it's still recruiting, but this was their initial read. So, yeah,

750
00:39:25,000 --> 00:39:26,520
Speaker 2: you get one of these scans, there's a ninety three

751
00:39:26,520 --> 00:39:28,560
Speaker 2: percent chance they'll find something abnormal. Like if you think

752
00:39:28,600 --> 00:39:30,399
Speaker 2: you're going to go and get a clean bill of health,

753
00:39:30,440 --> 00:39:30,680
Speaker 2: you're not.

754
00:39:30,880 --> 00:39:33,520
Speaker 1: You're not. No, But and I think that's good. That's

755
00:39:33,560 --> 00:39:35,759
Speaker 1: good for people to know coming into this, right, you know,

756
00:39:35,920 --> 00:39:37,879
Speaker 1: like if you go for one of these, like they're

757
00:39:37,880 --> 00:39:40,319
Speaker 1: finding something, right, This is like this is actually what

758
00:39:40,360 --> 00:39:42,200
Speaker 1: my doctor told me the first time I had an mammorgram.

759
00:39:42,239 --> 00:39:44,399
Speaker 1: She was like, they always find something. It's probably fun.

760
00:39:45,640 --> 00:39:49,280
Speaker 2: Yeah. The next question, of course, is is it actionable?

761
00:39:49,360 --> 00:39:49,520
Speaker 8: Right?

762
00:39:49,600 --> 00:39:52,960
Speaker 2: Like, Like you know, people have found for example, modest

763
00:39:53,040 --> 00:39:56,240
Speaker 2: slip discs or something like that, and for some people

764
00:39:56,239 --> 00:39:59,240
Speaker 2: that's useful, like, oh that's why I have backpain. Okay, great,

765
00:39:59,280 --> 00:40:01,359
Speaker 2: there's nothing to be on about it, or maybe get

766
00:40:01,360 --> 00:40:05,200
Speaker 2: some PT. But three hundred of so thirty percent of people,

767
00:40:05,920 --> 00:40:08,520
Speaker 2: we'll have a finding that requires specific follow up, like

768
00:40:09,200 --> 00:40:11,600
Speaker 2: you now have to see another doctor or get another scan,

769
00:40:11,760 --> 00:40:14,840
Speaker 2: or get another test or something to tie this together.

770
00:40:15,239 --> 00:40:18,680
Speaker 2: And that's where we start running into this. There's sort

771
00:40:18,680 --> 00:40:22,760
Speaker 2: of an ethical issue here, which is that the ethical

772
00:40:22,800 --> 00:40:27,239
Speaker 2: issue with full body scans is that they privatize profits

773
00:40:27,320 --> 00:40:31,120
Speaker 2: but socialize the risks. This is one of the problems

774
00:40:31,160 --> 00:40:33,279
Speaker 2: I have with these companies is that, like, they make

775
00:40:33,320 --> 00:40:35,839
Speaker 2: money from us, right, like you pay them, but if

776
00:40:35,880 --> 00:40:39,360
Speaker 2: they if they find something like your insurance company pays

777
00:40:39,480 --> 00:40:43,640
Speaker 2: for all that subsequent workup and stuff. And the more

778
00:40:43,680 --> 00:40:46,279
Speaker 2: people that do this, the companies keep that money, right,

779
00:40:46,320 --> 00:40:50,520
Speaker 2: they keep the profits that they've made. But theoretically, you're

780
00:40:50,560 --> 00:40:53,200
Speaker 2: the economist then only theoretically, if we're chasing down all

781
00:40:53,200 --> 00:40:56,040
Speaker 2: these incidental omas, it's going to drive up insurance costs

782
00:40:56,120 --> 00:40:59,399
Speaker 2: for everyone, right, because the insurance just has to be like, well, part,

783
00:40:59,440 --> 00:41:01,520
Speaker 2: you know, we got to allocate X number of dollars

784
00:41:01,560 --> 00:41:04,279
Speaker 2: per person for the weird stuff that it's going to

785
00:41:04,280 --> 00:41:05,600
Speaker 2: get fined in full body st sure.

786
00:41:05,600 --> 00:41:07,880
Speaker 1: And also, like there's a much more direct version of this,

787
00:41:07,920 --> 00:41:10,440
Speaker 1: which is if anybody is over sixty five, were already

788
00:41:10,600 --> 00:41:12,760
Speaker 1: you know, your tax dollars are paying for their healthcare,

789
00:41:12,800 --> 00:41:15,000
Speaker 1: and so if they get this thing, then Medicare is

790
00:41:15,000 --> 00:41:17,000
Speaker 1: paying and you're paying for medical rights.

791
00:41:17,320 --> 00:41:19,680
Speaker 2: So you're not the thirty percent of people who get

792
00:41:19,680 --> 00:41:22,120
Speaker 2: follow up, like generally they're not paying for well, they're

793
00:41:22,120 --> 00:41:24,560
Speaker 2: paying their deductibles or whatever. And so people should be aware,

794
00:41:24,640 --> 00:41:26,960
Speaker 2: especially if you buy a deductible plan, that you might

795
00:41:26,960 --> 00:41:29,839
Speaker 2: be digging into that, but you know that's still sort

796
00:41:29,840 --> 00:41:34,120
Speaker 2: of a socialized form of medicine. Twenty two have cancer

797
00:41:34,239 --> 00:41:37,520
Speaker 2: and here's and this is this is a study where

798
00:41:37,600 --> 00:41:40,960
Speaker 2: they they proved it. These are biopsy proven cancer. So

799
00:41:41,000 --> 00:41:43,839
Speaker 2: two point two percent out of the thousand people who

800
00:41:43,880 --> 00:41:46,319
Speaker 2: got a pernuvo scan. And this is where I keep

801
00:41:46,400 --> 00:41:49,640
Speaker 2: just like hitting the wall in terms of what I

802
00:41:49,680 --> 00:41:53,160
Speaker 2: think about this, because from a public health standpoint, I'm like, well,

803
00:41:53,160 --> 00:41:55,000
Speaker 2: clearly this is a bad idea. It costs way too

804
00:41:55,040 --> 00:41:57,880
Speaker 2: much to the public. It's like no one, no no

805
00:41:57,960 --> 00:42:01,480
Speaker 2: agency would ever recommend this. It's it's clearly not cost effective,

806
00:42:01,480 --> 00:42:05,400
Speaker 2: you're spending so much money chasing down nonsense. But for

807
00:42:05,440 --> 00:42:09,799
Speaker 2: an individual one of those twenty two people, Emily like,

808
00:42:10,040 --> 00:42:11,640
Speaker 2: what if you're one of those twenty two people?

809
00:42:13,040 --> 00:42:15,040
Speaker 1: Yeah, And I think it's once you put it in

810
00:42:15,080 --> 00:42:19,400
Speaker 1: that frame again, our psychology makes it very difficult because

811
00:42:19,440 --> 00:42:21,520
Speaker 1: then you're in a I don't know, like a loss

812
00:42:21,560 --> 00:42:26,160
Speaker 1: frame of Basically, if you don't do this, what if

813
00:42:26,160 --> 00:42:27,959
Speaker 1: you're one of the twenty two people, and then that's

814
00:42:28,000 --> 00:42:33,520
Speaker 1: the salient that's the salient fact that feels terrible. And

815
00:42:33,560 --> 00:42:36,040
Speaker 1: of course I would do anything to not be you know,

816
00:42:36,080 --> 00:42:38,760
Speaker 1: to have this found as part of those those twenty

817
00:42:38,800 --> 00:42:39,280
Speaker 1: two people.

818
00:42:39,560 --> 00:42:41,840
Speaker 2: Yeah, Now we don't know if those twenty two what

819
00:42:41,960 --> 00:42:46,480
Speaker 2: number of them are overdiagnosis? Right, some in theory, those

820
00:42:46,520 --> 00:42:50,160
Speaker 2: cancers that really were found might have never amounted to anything.

821
00:42:50,560 --> 00:42:53,080
Speaker 2: I think people might not appreciate this is like we

822
00:42:53,120 --> 00:42:58,239
Speaker 2: as doctors don't really understand disease before it becomes symptomatic

823
00:42:58,680 --> 00:43:02,600
Speaker 2: in a lot of cases, like the way we diagnose

824
00:43:02,840 --> 00:43:06,360
Speaker 2: most cancers, not the screening ones, not breast cancer and

825
00:43:06,400 --> 00:43:09,840
Speaker 2: colon cancer, but cancers that come up in other ways

826
00:43:09,880 --> 00:43:12,520
Speaker 2: is because someone has symptoms and general you know, yes,

827
00:43:12,680 --> 00:43:17,759
Speaker 2: sometimes they get found. Incidentally, we know that it seems

828
00:43:17,800 --> 00:43:20,520
Speaker 2: that cancers start off small and get bigger over time.

829
00:43:22,480 --> 00:43:25,440
Speaker 2: We tend to find them when they are not small,

830
00:43:26,000 --> 00:43:30,759
Speaker 2: unless it's a screening detected cancer. We don't really know

831
00:43:30,840 --> 00:43:34,600
Speaker 2: if the small ones always become the big one. Like,

832
00:43:34,640 --> 00:43:36,960
Speaker 2: we don't because we've never done a study where we

833
00:43:37,000 --> 00:43:38,839
Speaker 2: do a full body scan and just sit on our

834
00:43:38,880 --> 00:43:42,640
Speaker 2: hands and watch. Maybe maybe that'll happen in the future.

835
00:43:42,680 --> 00:43:46,160
Speaker 2: I'm not sure, but it's you're sort of obligated because

836
00:43:46,200 --> 00:43:49,399
Speaker 2: of your understanding of cancer biology to like you got

837
00:43:49,400 --> 00:43:52,560
Speaker 2: to do something about these twenty two cancers. But I

838
00:43:52,600 --> 00:43:54,040
Speaker 2: can't tell you for sure that they all would have

839
00:43:54,120 --> 00:43:54,840
Speaker 2: killed these people.

840
00:43:55,360 --> 00:43:58,400
Speaker 1: Yeah, I mean, I think what's missing from this boat,

841
00:43:58,440 --> 00:44:02,280
Speaker 1: from this discussion and from the data is probably two things.

842
00:44:02,280 --> 00:44:05,719
Speaker 1: So one is just a lot of these being done right.

843
00:44:05,760 --> 00:44:08,240
Speaker 1: So if we had a bigger database and we understood

844
00:44:08,320 --> 00:44:11,040
Speaker 1: more about what are the things that will turn into

845
00:44:11,040 --> 00:44:13,000
Speaker 1: something and what are the things that are not, then

846
00:44:13,400 --> 00:44:15,480
Speaker 1: this information would be more valuable because you would have

847
00:44:15,520 --> 00:44:17,920
Speaker 1: more of a sense of what to action on. Even

848
00:44:17,960 --> 00:44:20,719
Speaker 1: within those people with the abnormal findings, you would have

849
00:44:20,719 --> 00:44:23,520
Speaker 1: a better sense of like you know, yeah, like seventy

850
00:44:23,560 --> 00:44:27,120
Speaker 1: three percent of people have an allsion on their pancreas.

851
00:44:27,200 --> 00:44:29,560
Speaker 1: Is no big deal, Like we see that, we see.

852
00:44:29,400 --> 00:44:29,960
Speaker 2: That all the time.

853
00:44:30,280 --> 00:44:32,200
Speaker 1: Since we haven't done this, it's hard to say that.

854
00:44:32,800 --> 00:44:36,080
Speaker 1: I think the second piece here is most of the

855
00:44:36,160 --> 00:44:39,080
Speaker 1: time people are doing this once, or at least most

856
00:44:39,120 --> 00:44:40,839
Speaker 1: of the data is on the first time you do

857
00:44:40,880 --> 00:44:44,360
Speaker 1: a scan, and a lot of what we are looking

858
00:44:44,400 --> 00:44:47,759
Speaker 1: for in medicine to diagnose whether something's a problem, is

859
00:44:47,800 --> 00:44:51,560
Speaker 1: it is a change. Think about dermatology, Think about skin

860
00:44:51,600 --> 00:44:54,239
Speaker 1: cancer screening. You know, why do you go back to

861
00:44:54,280 --> 00:44:56,480
Speaker 1: the dermatologists every six months if you're at high risk

862
00:44:56,520 --> 00:45:00,080
Speaker 1: for skin cancer. It's because they're looking for did that change?

863
00:45:00,280 --> 00:45:02,080
Speaker 1: You know, there's no problem of just being a person

864
00:45:02,120 --> 00:45:04,399
Speaker 1: full of moles. The problem is when your moles start

865
00:45:04,440 --> 00:45:07,239
Speaker 1: looking weird and being a different weird color. Yeah, and

866
00:45:07,360 --> 00:45:10,320
Speaker 1: this is a case where if you have one scan

867
00:45:10,800 --> 00:45:14,080
Speaker 1: and then you can compare them over time those second

868
00:45:14,719 --> 00:45:16,400
Speaker 1: that second scan is going to be a lot more

869
00:45:16,440 --> 00:45:19,600
Speaker 1: cost effective than the first scan because the second time

870
00:45:19,640 --> 00:45:21,560
Speaker 1: you do the scan, all of those things that like

871
00:45:21,600 --> 00:45:23,360
Speaker 1: we saw it before and it looks the same now

872
00:45:23,520 --> 00:45:26,200
Speaker 1: we're just ignoring. We're into the things that are that

873
00:45:26,280 --> 00:45:28,520
Speaker 1: are changing, and I think that's the kind of promise here.

874
00:45:28,560 --> 00:45:30,680
Speaker 1: I assume Pernevo who tells you should do this every

875
00:45:30,800 --> 00:45:33,680
Speaker 1: you know, twenty three days or whatever, because that's what

876
00:45:33,800 --> 00:45:37,120
Speaker 1: money is for. But it does feel like that's a

877
00:45:37,520 --> 00:45:38,680
Speaker 1: that's a piece of something.

878
00:45:38,840 --> 00:45:41,520
Speaker 2: I think there's something there. I will say the mid Journey,

879
00:45:41,560 --> 00:45:44,759
Speaker 2: that whole body ultrasound submerging water like sci fi thing,

880
00:45:45,080 --> 00:45:47,799
Speaker 2: is specifically marketing it for that. They're like, we're going

881
00:45:47,840 --> 00:45:50,000
Speaker 2: to be cheaper. They're saying it might only be one

882
00:45:50,040 --> 00:45:52,799
Speaker 2: hundred dollars a scan, which, like, you know, I've got

883
00:45:52,800 --> 00:45:54,000
Speaker 2: a bridge to sell you if it turns out to

884
00:45:54,040 --> 00:45:56,440
Speaker 2: one hundred dollars, but that's fine. Maybe VC funds it

885
00:45:56,800 --> 00:45:59,360
Speaker 2: like Uber. But but they're like, oh yeah, then you

886
00:45:59,360 --> 00:46:01,560
Speaker 2: can do this heatedly. So we're going to solve the

887
00:46:01,560 --> 00:46:04,439
Speaker 2: false positive and the incidental oma stuff by just you'll

888
00:46:04,440 --> 00:46:06,759
Speaker 2: do this again and again and we'll watch things over time.

889
00:46:06,800 --> 00:46:09,160
Speaker 2: And the answer is like, okay, maybe, but we have

890
00:46:09,280 --> 00:46:11,439
Speaker 2: no data to suggest that that's the case yet.

891
00:46:12,080 --> 00:46:12,640
Speaker 1: Yeah.

892
00:46:12,719 --> 00:46:16,320
Speaker 2: So there's those five people in the in the Pernuvo

893
00:46:16,360 --> 00:46:19,360
Speaker 2: study that ended up having a cancer that Pernuvo missed.

894
00:46:19,760 --> 00:46:22,440
Speaker 2: I'll point out there's just some things that MRIs don't

895
00:46:23,280 --> 00:46:26,200
Speaker 2: see very well. So so breast, colon and thyroid are

896
00:46:26,239 --> 00:46:29,440
Speaker 2: like kind of classic hard to see on MRI. A

897
00:46:29,480 --> 00:46:32,080
Speaker 2: whole body MR you can get dedicated breast MRI, for example,

898
00:46:32,120 --> 00:46:35,800
Speaker 2: but that's not how this works. Breast and colon obviously

899
00:46:36,239 --> 00:46:39,239
Speaker 2: are ones that we screen for. So you know, even

900
00:46:39,239 --> 00:46:41,319
Speaker 2: if you're doing a whole body scan, you still want

901
00:46:41,320 --> 00:46:45,320
Speaker 2: to do your age recommended cancer screening too.

902
00:46:45,719 --> 00:46:49,320
Speaker 1: Yes, you just want to be constantly getting screened.

903
00:46:52,239 --> 00:46:55,440
Speaker 2: Got to generate those generate medical expenses.

904
00:46:57,719 --> 00:47:03,319
Speaker 1: So I think it's worth we're sort of saying how

905
00:47:03,360 --> 00:47:05,920
Speaker 1: common it is to find some kind of as you

906
00:47:06,000 --> 00:47:08,680
Speaker 1: call it, an incidental loma when we do these. When

907
00:47:08,719 --> 00:47:11,160
Speaker 1: we do these just in case people go to do it,

908
00:47:11,520 --> 00:47:14,080
Speaker 1: like what it's going to happen is not that they're

909
00:47:14,120 --> 00:47:16,840
Speaker 1: going to tell you everything's fine. Something is going to happen.

910
00:47:16,880 --> 00:47:19,000
Speaker 1: So to put some datea on this, there's a twenty

911
00:47:19,040 --> 00:47:25,080
Speaker 1: fourteen study that looked at six sixty six MRIs performed

912
00:47:25,160 --> 00:47:28,600
Speaker 1: on people, and they looked at what were the incidental findings,

913
00:47:28,600 --> 00:47:31,680
Speaker 1: And I think an important point to note is that

914
00:47:32,280 --> 00:47:36,440
Speaker 1: they found some incidental finding in six hundred and fifty

915
00:47:36,520 --> 00:47:39,480
Speaker 1: nine of the six hundred and sixty six people that

916
00:47:39,520 --> 00:47:42,839
Speaker 1: they scanned, so that is almost everyone. I don't know

917
00:47:42,920 --> 00:47:44,960
Speaker 1: what happened to those seven people, maybe there was an

918
00:47:45,040 --> 00:47:49,760
Speaker 1: error in their scan or something, but basically everyone found

919
00:47:50,640 --> 00:47:54,880
Speaker 1: something that was wrong. And you know, some of this

920
00:47:55,120 --> 00:47:58,399
Speaker 1: is in your brain, some of it is in your

921
00:47:58,680 --> 00:48:01,759
Speaker 1: your spine, some of that are renal cysts. There were

922
00:48:01,840 --> 00:48:03,840
Speaker 1: many different things that they found, but for me, the

923
00:48:03,880 --> 00:48:06,719
Speaker 1: headline here was like you always get something. Yeah, was

924
00:48:06,760 --> 00:48:07,640
Speaker 1: that your headline?

925
00:48:07,960 --> 00:48:10,160
Speaker 2: Yeah? And I mean, I'll put some numbers on it

926
00:48:10,200 --> 00:48:13,000
Speaker 2: for you. So like between twenty two in the younger

927
00:48:13,000 --> 00:48:18,120
Speaker 2: age group, twenty two percent had some evidence of brain infarct,

928
00:48:18,600 --> 00:48:21,279
Speaker 2: forty five percent in the older group. Things you know,

929
00:48:21,440 --> 00:48:22,920
Speaker 2: just like little areas of the brain that look like

930
00:48:22,960 --> 00:48:25,840
Speaker 2: they've had a problem in the past. Five percent of

931
00:48:25,880 --> 00:48:28,600
Speaker 2: the younger cohort had pulmonary nodules sixteen percent and the

932
00:48:28,680 --> 00:48:32,280
Speaker 2: older cohort renal cysts seventeen percent and forty one percent,

933
00:48:32,360 --> 00:48:35,880
Speaker 2: spinal degeneration twenty three percent and forty five percent. So

934
00:48:35,920 --> 00:48:38,400
Speaker 2: these are like you're gonna you're gonna see this stuff,

935
00:48:38,440 --> 00:48:41,920
Speaker 2: so be prepared, like you there's a very good chance

936
00:48:41,960 --> 00:48:43,840
Speaker 2: that someone's going to tell you after a full body

937
00:48:43,840 --> 00:48:46,200
Speaker 2: scan that you've got a nodule in your lung, you've

938
00:48:46,200 --> 00:48:48,840
Speaker 2: got a spot in your kidney, you have disgeneration, or

939
00:48:48,920 --> 00:48:50,839
Speaker 2: even that you may have at one point in the

940
00:48:50,880 --> 00:48:53,840
Speaker 2: past had a micro's stroke that you've never noticed.

941
00:48:54,920 --> 00:48:59,960
Speaker 1: Yeah, So can we talk about whether there are medical

942
00:49:00,239 --> 00:49:02,799
Speaker 1: risks to doing this? So, I mean we've talked a

943
00:49:02,800 --> 00:49:05,200
Speaker 1: tremendous amount about what I think of as sort of

944
00:49:05,680 --> 00:49:08,680
Speaker 1: cost risks, but also psychology risks. You know, you're going

945
00:49:08,719 --> 00:49:10,719
Speaker 1: to somebody's going to tell you have a you had

946
00:49:10,760 --> 00:49:12,759
Speaker 1: a mini stroke, Like maybe that's going to freak you out.

947
00:49:13,480 --> 00:49:16,560
Speaker 1: But we are also, interestingly, many of the same people

948
00:49:16,560 --> 00:49:18,479
Speaker 1: who are into these things are also very worried about

949
00:49:18,560 --> 00:49:21,160
Speaker 1: radiation from like their microwave, And so I always find

950
00:49:21,160 --> 00:49:23,600
Speaker 1: it interesting to I'd be like, people don't want to

951
00:49:23,600 --> 00:49:26,520
Speaker 1: stand in front of their microwave because of microwaves, but

952
00:49:26,600 --> 00:49:29,400
Speaker 1: then they want to go into an MRI machine and

953
00:49:29,440 --> 00:49:30,480
Speaker 1: get you know, zappered.

954
00:49:31,040 --> 00:49:34,120
Speaker 2: Yeah, is that a problem? I don't, I mean not really.

955
00:49:34,800 --> 00:49:37,480
Speaker 2: You know, the medical risk is mostly from the downstream stuff.

956
00:49:37,520 --> 00:49:41,280
Speaker 2: So MRIs don't use ionizing radiation, and contrast to CT scans,

957
00:49:41,440 --> 00:49:45,640
Speaker 2: So there theoretically is no increased risk of cancer from

958
00:49:45,840 --> 00:49:48,760
Speaker 2: repeated MRI scanning. There are companies that offer whole body

959
00:49:48,800 --> 00:49:51,680
Speaker 2: CEET scans, and that certainly is radiation and so like

960
00:49:51,760 --> 00:49:54,360
Speaker 2: that in theory would increase your risk of cancer over time,

961
00:49:54,800 --> 00:49:58,200
Speaker 2: But for a whole body MRI probably not. I guess

962
00:49:58,320 --> 00:50:00,399
Speaker 2: if you're claustrophobic, there's a risk you could break out.

963
00:50:00,400 --> 00:50:00,560
Speaker 6: You know.

964
00:50:00,600 --> 00:50:02,520
Speaker 2: Those tubes are a little bit Yeah, we should.

965
00:50:02,320 --> 00:50:04,040
Speaker 1: Say the MRI they put you on a tube and

966
00:50:04,040 --> 00:50:05,920
Speaker 1: they get you in this like great loud tube and

967
00:50:05,960 --> 00:50:08,080
Speaker 1: they go, yeah.

968
00:50:07,960 --> 00:50:11,680
Speaker 2: So it's it's not it's not super comfortable, but but yeah,

969
00:50:11,680 --> 00:50:14,040
Speaker 2: I don't think it's a cancer issue.

970
00:50:14,239 --> 00:50:16,719
Speaker 1: Okay, no cancer should is it? Okay to stand in

971
00:50:16,719 --> 00:50:21,879
Speaker 1: fron of the microwave? How is that? We'll do that later.

972
00:50:22,239 --> 00:50:25,360
Speaker 2: I have to watch my food carefully because I'm so hungry.

973
00:50:28,440 --> 00:50:33,600
Speaker 1: Okay. Does anyone think anyone in the official doctor space

974
00:50:34,280 --> 00:50:36,160
Speaker 1: think these are a good idea?

975
00:50:37,600 --> 00:50:38,919
Speaker 2: Uh? No?

976
00:50:39,600 --> 00:50:39,839
Speaker 1: Yeah.

977
00:50:39,880 --> 00:50:46,759
Speaker 2: The the formal like recommendations from the medical societies are like,

978
00:50:48,080 --> 00:50:51,680
Speaker 2: are are that no, that screening whole body MRI is

979
00:50:51,719 --> 00:50:54,520
Speaker 2: not cost effective and will not save lives?

980
00:50:54,760 --> 00:50:58,600
Speaker 1: How do they come? Like? I will say, though, twenty

981
00:50:58,640 --> 00:51:02,799
Speaker 1: two people, like, how do they confront that. I know,

982
00:51:02,960 --> 00:51:06,399
Speaker 1: some of those people maybe were saved by this.

983
00:51:06,600 --> 00:51:08,080
Speaker 2: Yeah, so you're saying there's a chance.

984
00:51:08,640 --> 00:51:09,879
Speaker 1: Yeah, you're saying there's a chance.

985
00:51:10,080 --> 00:51:12,520
Speaker 2: I mean, in the end, I think the problem is

986
00:51:13,120 --> 00:51:18,719
Speaker 2: that we don't have the real high quality study that

987
00:51:18,760 --> 00:51:21,840
Speaker 2: you would do here, which is a full on randomized

988
00:51:21,880 --> 00:51:23,920
Speaker 2: trial where you take ten thousand people and you do

989
00:51:23,960 --> 00:51:26,000
Speaker 2: a whole body MRI and you do take ten thousand

990
00:51:26,000 --> 00:51:28,000
Speaker 2: people that don't you follow them for ten years and

991
00:51:28,040 --> 00:51:31,680
Speaker 2: you see what happens. You know, the thing about those

992
00:51:31,719 --> 00:51:34,840
Speaker 2: twenty two people with cancer, we already like some of

993
00:51:34,880 --> 00:51:38,080
Speaker 2: them maybe the cancer never matters, never would have been detected,

994
00:51:38,080 --> 00:51:40,920
Speaker 2: they die of something else. Some of them the cancer

995
00:51:40,960 --> 00:51:47,760
Speaker 2: would be detected anyway, they develop a symptom. And although

996
00:51:47,800 --> 00:51:52,040
Speaker 2: it's often true that detecting a cancer earlier is better

997
00:51:52,080 --> 00:51:55,000
Speaker 2: than detecting it later, that's not always the case. And

998
00:51:55,120 --> 00:51:57,040
Speaker 2: you know, something comes up in some other way and

999
00:51:57,080 --> 00:51:59,200
Speaker 2: you pick up that cancer and you get to treat

1000
00:51:59,200 --> 00:52:01,560
Speaker 2: it and there's no difference and outcomes and so there's

1001
00:52:01,560 --> 00:52:04,080
Speaker 2: a lot of ways that even in those twenty two people,

1002
00:52:04,320 --> 00:52:07,399
Speaker 2: like if you would do the sliding doors thing of

1003
00:52:07,560 --> 00:52:09,600
Speaker 2: you know, their life where they went through the whole

1004
00:52:09,600 --> 00:52:11,680
Speaker 2: body MRI and their life where they didn't go through

1005
00:52:11,719 --> 00:52:14,920
Speaker 2: the whole body MRI. The outcomes end up being the same,

1006
00:52:15,000 --> 00:52:18,239
Speaker 2: at least on sort of the broad population level.

1007
00:52:18,880 --> 00:52:22,240
Speaker 1: Yeah. I like that study. That study is probably too expensive.

1008
00:52:22,640 --> 00:52:26,040
Speaker 2: It's an expensive study. There's one study I really kind

1009
00:52:26,040 --> 00:52:27,880
Speaker 2: of liked that. I thought it was clever in this space.

1010
00:52:28,719 --> 00:52:30,799
Speaker 2: This is from the European Journal of epidem Aology, and

1011
00:52:31,040 --> 00:52:35,200
Speaker 2: they actually they did a whole body MRI on about

1012
00:52:35,360 --> 00:52:38,680
Speaker 2: thirty three hundred people, so everyone got it, but they

1013
00:52:38,800 --> 00:52:43,360
Speaker 2: actually only disclosed the findings to half of them. That's

1014
00:52:43,440 --> 00:52:46,799
Speaker 2: what they sort of randomized, which I think is really interesting.

1015
00:52:47,880 --> 00:52:48,040
Speaker 8: Yeah.

1016
00:52:48,080 --> 00:52:49,680
Speaker 2: I think this is sort of something that maybe you

1017
00:52:49,680 --> 00:52:54,200
Speaker 2: could only do in Europe. And what they found is

1018
00:52:54,239 --> 00:52:58,320
Speaker 2: that if you disclosed to people, the rate of biopsy

1019
00:52:58,440 --> 00:53:00,320
Speaker 2: of something that turns out not to be care answer

1020
00:53:00,480 --> 00:53:05,480
Speaker 2: was thirty nine percent higher. Okay, so basically, you see something,

1021
00:53:05,560 --> 00:53:07,360
Speaker 2: they get biopsy and it's not cancer. So you've got

1022
00:53:07,400 --> 00:53:10,000
Speaker 2: about a forty percent increased risk of getting a needle

1023
00:53:10,040 --> 00:53:11,800
Speaker 2: stuck in you and it turns out to be nothing.

1024
00:53:12,480 --> 00:53:16,160
Speaker 2: But the rate of biopsies that did turn out to

1025
00:53:16,160 --> 00:53:21,160
Speaker 2: be cancer was seventy four percent higher. Now, there weren't

1026
00:53:21,200 --> 00:53:24,359
Speaker 2: numerically there were very few of these, but sure enough,

1027
00:53:24,440 --> 00:53:26,600
Speaker 2: some of those spots do turn out to be cancer,

1028
00:53:26,640 --> 00:53:30,279
Speaker 2: as we've seen before. The authors concluded that there was

1029
00:53:30,560 --> 00:53:37,160
Speaker 2: good evidence of overtesting an overdiagnosis and called for further research.

1030
00:53:37,200 --> 00:53:39,600
Speaker 2: But I think this is a potential study design that,

1031
00:53:39,680 --> 00:53:42,000
Speaker 2: like if people were willing to not hear their results

1032
00:53:42,000 --> 00:53:44,759
Speaker 2: for two years, but then you can even imagine like

1033
00:53:44,760 --> 00:53:47,520
Speaker 2: two years later when the study's over, they're like, oh,

1034
00:53:47,600 --> 00:53:49,520
Speaker 2: by the way, here are your results. Like there's there's

1035
00:53:49,560 --> 00:53:50,960
Speaker 2: a big spot on your liver.

1036
00:53:50,880 --> 00:53:53,680
Speaker 1: Like check, get it checked out.

1037
00:53:53,800 --> 00:53:55,799
Speaker 2: Don't be mad. I mean you have to do a

1038
00:53:55,800 --> 00:53:57,240
Speaker 2: really good job of informed consent.

1039
00:53:57,600 --> 00:53:59,600
Speaker 1: Yeah, yeah, I mean I think there's all kinds of

1040
00:53:59,600 --> 00:54:04,080
Speaker 1: interesting sort of information procedures you could think about here,

1041
00:54:04,120 --> 00:54:06,040
Speaker 1: Which is, you know, can you have your AI after

1042
00:54:06,080 --> 00:54:09,040
Speaker 1: a reads the radiology report? Can you have it like

1043
00:54:09,320 --> 00:54:14,040
Speaker 1: decide how to like which pieces of information are potentially

1044
00:54:14,080 --> 00:54:18,240
Speaker 1: actionable and which are and which are not. Again, something

1045
00:54:18,239 --> 00:54:20,560
Speaker 1: for which having more data would be more helpful.

1046
00:54:21,680 --> 00:54:24,200
Speaker 2: I've got one other hypothetical for you. Yeah, Okay, Emily

1047
00:54:24,239 --> 00:54:26,600
Speaker 2: as we're coming down to smasher past this. So I

1048
00:54:26,640 --> 00:54:30,320
Speaker 2: was thinking that one of the issues on a population

1049
00:54:30,440 --> 00:54:33,160
Speaker 2: level is the costs born by downstream care, which, as

1050
00:54:33,160 --> 00:54:34,919
Speaker 2: we said, are sort of born by all of us,

1051
00:54:35,000 --> 00:54:38,320
Speaker 2: and the profits are absorbed by these few companies. Okay,

1052
00:54:39,280 --> 00:54:43,800
Speaker 2: imagine a world where the insurance companies start to say, like, look, listen,

1053
00:54:44,400 --> 00:54:48,680
Speaker 2: we're not gonna pay for anything that happens downstream of

1054
00:54:48,719 --> 00:54:51,400
Speaker 2: these scans, which I don't know if that's possible, or

1055
00:54:52,040 --> 00:54:55,960
Speaker 2: give you an alternative view where they're like, Okay, anything

1056
00:54:56,000 --> 00:54:58,080
Speaker 2: that you chase down after these scans, if it turns

1057
00:54:58,120 --> 00:54:59,960
Speaker 2: out to be like a cancer or something that would

1058
00:55:00,000 --> 00:55:02,400
Speaker 2: I killed you, we'll pay for all that care. But

1059
00:55:02,480 --> 00:55:04,600
Speaker 2: if it turns out to be an incidental oma and

1060
00:55:04,640 --> 00:55:06,680
Speaker 2: you get a biopsy and it's benign, we're not paying

1061
00:55:06,680 --> 00:55:11,200
Speaker 2: for any of that. Do you think that changes the

1062
00:55:11,239 --> 00:55:14,920
Speaker 2: individual calculus here if more of the risk of the

1063
00:55:15,000 --> 00:55:17,839
Speaker 2: results is borne by the individual instead of like the

1064
00:55:17,840 --> 00:55:20,719
Speaker 2: insurance company or society, does that change the outlook for

1065
00:55:20,760 --> 00:55:21,360
Speaker 2: these companies?

1066
00:55:22,640 --> 00:55:24,719
Speaker 1: I think it would change the outlook for the companies

1067
00:55:24,960 --> 00:55:27,719
Speaker 1: quite a lot in the long term, because I think

1068
00:55:27,719 --> 00:55:30,480
Speaker 1: it would limit their growth. I mean, right now, this

1069
00:55:30,680 --> 00:55:33,279
Speaker 1: is broadly consumed by a set of people with quite

1070
00:55:33,280 --> 00:55:38,360
Speaker 1: a lot of resources, for whom I suspect that incentive

1071
00:55:38,400 --> 00:55:40,960
Speaker 1: would be pretty small on the margin.

1072
00:55:41,800 --> 00:55:43,480
Speaker 2: They're like, Okay, I'll pay for it. That's fine.

1073
00:55:43,719 --> 00:55:46,920
Speaker 1: Yeah, Kim Gardassian can pay for her extra stuff. But

1074
00:55:47,520 --> 00:55:50,000
Speaker 1: if you think about trying to expand this into like

1075
00:55:50,040 --> 00:55:53,600
Speaker 1: everybody has this, yeah, then yes, absolutely, it's going to

1076
00:55:53,920 --> 00:55:56,400
Speaker 1: you know, the like downstream costs of this are going

1077
00:55:56,480 --> 00:55:59,000
Speaker 1: to matter. On the other hand, again getting back to

1078
00:55:59,040 --> 00:56:01,280
Speaker 1: the psychology, if you put it in a loss frame

1079
00:56:01,560 --> 00:56:04,440
Speaker 1: like you are, we're going to find what if you're

1080
00:56:04,480 --> 00:56:07,640
Speaker 1: one of the twenty two people like so, maybe it'll

1081
00:56:07,640 --> 00:56:09,600
Speaker 1: cost you a little bit of money, but you might

1082
00:56:09,640 --> 00:56:11,719
Speaker 1: find out that you have cancer and then not die

1083
00:56:11,760 --> 00:56:16,040
Speaker 1: from it so soon. That's a very very very powerful pull.

1084
00:56:18,200 --> 00:56:20,360
Speaker 2: All right, Emily, I need you to go first here.

1085
00:56:20,560 --> 00:56:23,240
Speaker 2: You've got to tell me, please convince me, smash your pass.

1086
00:56:23,280 --> 00:56:24,400
Speaker 2: This is really hard.

1087
00:56:25,719 --> 00:56:29,200
Speaker 1: I also find this one very hard. But right now

1088
00:56:29,239 --> 00:56:33,680
Speaker 1: I am a pass. I think that the the incidental

1089
00:56:33,760 --> 00:56:37,799
Speaker 1: findings feel so much more central to me than the

1090
00:56:37,840 --> 00:56:41,680
Speaker 1: other findings. And I think if people are doing a

1091
00:56:41,719 --> 00:56:45,160
Speaker 1: good job of the regular cancer screenings that they are

1092
00:56:45,200 --> 00:56:49,960
Speaker 1: supposed to be doing mimography, callon cancer screening, et cetera.

1093
00:56:50,920 --> 00:56:55,680
Speaker 1: That is sufficient, So I'm a pass.

1094
00:56:57,000 --> 00:57:01,880
Speaker 2: Yeah, those screenings had random LID trial data to show

1095
00:57:02,280 --> 00:57:05,640
Speaker 2: that they decrease breast cancer specific or colon cancer specific mortality,

1096
00:57:05,680 --> 00:57:08,399
Speaker 2: like they did that work. These companies have not done

1097
00:57:08,400 --> 00:57:12,680
Speaker 2: that work yet. So by that standard, I'm going to

1098
00:57:12,760 --> 00:57:15,160
Speaker 2: say I'm definitely a pass on the sort of population

1099
00:57:15,239 --> 00:57:18,920
Speaker 2: wide level. But for an individual, I'm actually let me

1100
00:57:19,200 --> 00:57:21,640
Speaker 2: just personally like, no, I'm a pass, Like, I don't

1101
00:57:21,680 --> 00:57:25,080
Speaker 2: want to do this. I think I'll find it'll find

1102
00:57:25,080 --> 00:57:26,720
Speaker 2: something that I have to deal with. I don't want

1103
00:57:26,720 --> 00:57:29,320
Speaker 2: to deal with more. Sh I'm going to pass.

1104
00:57:29,600 --> 00:57:34,880
Speaker 1: Okay, we're passing. You seem sad, but you know this

1105
00:57:34,960 --> 00:57:37,040
Speaker 1: is so this I will say, this is the hardest

1106
00:57:38,040 --> 00:57:40,080
Speaker 1: these that we have done for me in terms of

1107
00:57:40,160 --> 00:57:43,240
Speaker 1: like the smash of pass decision. It's something I do

1108
00:57:43,320 --> 00:57:45,360
Speaker 1: think about, like cause and I do all kinds of

1109
00:57:45,360 --> 00:57:47,360
Speaker 1: other weird you know, like all these like blood tests

1110
00:57:47,360 --> 00:57:49,600
Speaker 1: and all this kind of stuff, but this one is

1111
00:57:49,680 --> 00:57:51,160
Speaker 1: just like one step too far from me.

1112
00:57:51,600 --> 00:57:53,400
Speaker 2: Yeah, we're not going to blame anyone for doing it.

1113
00:57:53,480 --> 00:57:55,720
Speaker 1: How about that No, that's for sure. We don't blame people.

1114
00:57:56,320 --> 00:57:58,959
Speaker 1: All right, that's it for full body Scans. Your mail

1115
00:57:58,960 --> 00:58:10,280
Speaker 1: back Question of the Week after the Break.

1116
00:58:06,760 --> 00:58:09,480
Speaker 8: Hey, Emiliam Perry, this is Margaret from Charleston and my

1117
00:58:09,600 --> 00:58:11,920
Speaker 8: question is about the shingles vaccine or I guess it's

1118
00:58:11,920 --> 00:58:15,560
Speaker 8: called the Shingri vaccine, you know, the one for shingles.

1119
00:58:16,080 --> 00:58:17,920
Speaker 8: I've been trying to convince my mother to get it

1120
00:58:17,960 --> 00:58:20,360
Speaker 8: as a way of preventing dementia? Am I right about this?

1121
00:58:20,480 --> 00:58:23,040
Speaker 8: Should I keep harassing her? Love the show?

1122
00:58:23,080 --> 00:58:26,040
Speaker 1: Thanks so much so. There's a lot of new data

1123
00:58:26,320 --> 00:58:31,800
Speaker 1: on this which I find very compelling, So I have

1124
00:58:31,920 --> 00:58:33,920
Speaker 1: been trying. I will just say, full disclosure. I've been

1125
00:58:33,920 --> 00:58:35,120
Speaker 1: trying to get my mother in law to get the

1126
00:58:35,120 --> 00:58:38,760
Speaker 1: Shingrix vaccine for like a decade, and every time there's

1127
00:58:38,760 --> 00:58:42,680
Speaker 1: new information about it, I send it to her. And

1128
00:58:42,720 --> 00:58:45,840
Speaker 1: my read is that the evidence on dementia, while not

1129
00:58:46,000 --> 00:58:51,280
Speaker 1: perfect randomized trial evidence, is increasingly quite strong. It's now

1130
00:58:51,320 --> 00:58:53,960
Speaker 1: coming out of a number of studies stronger for women

1131
00:58:54,480 --> 00:58:59,120
Speaker 1: than for men, and reasonably large effect sizes, you know,

1132
00:58:59,360 --> 00:59:02,320
Speaker 1: five to ten percent reduction in dementia risk, which is

1133
00:59:02,360 --> 00:59:06,800
Speaker 1: like pretty solid, pretty solid for something that also prevents

1134
00:59:06,840 --> 00:59:10,600
Speaker 1: you from getting shingles, which I hear is a terrible experience, horrible,

1135
00:59:11,240 --> 00:59:11,880
Speaker 1: so painful.

1136
00:59:12,080 --> 00:59:18,280
Speaker 2: Yeah, I mean, the data on dementia is observational, right.

1137
00:59:18,560 --> 00:59:21,000
Speaker 2: You look at large data sets of people, some of

1138
00:59:21,000 --> 00:59:22,840
Speaker 2: whom got the vaccine, some who didn't, and then you

1139
00:59:22,880 --> 00:59:24,760
Speaker 2: look and see if they end up with dementia. And

1140
00:59:24,760 --> 00:59:27,200
Speaker 2: of course there are other factors that predispose both to

1141
00:59:27,600 --> 00:59:30,200
Speaker 2: vaccination and maybe to your dementius. The sort of healthy

1142
00:59:30,240 --> 00:59:32,120
Speaker 2: user effect you can adjust for a lot of things,

1143
00:59:32,600 --> 00:59:34,720
Speaker 2: you know. That said, we interpret this in the context

1144
00:59:34,720 --> 00:59:37,080
Speaker 2: of the risk of the vaccine, which is extraordinarily low,

1145
00:59:37,360 --> 00:59:41,360
Speaker 2: the other benefits like preventing shingles, which is clearly documented

1146
00:59:41,400 --> 00:59:43,960
Speaker 2: in randomized trials, and like, that's great. So this feels

1147
00:59:44,000 --> 00:59:46,240
Speaker 2: like an icing on the cake thing. I will say.

1148
00:59:46,920 --> 00:59:49,440
Speaker 2: Vercella zoster virus, which is the virus that causes both

1149
00:59:49,520 --> 00:59:53,000
Speaker 2: chicken pox and shingles, is a weird one. It's a

1150
00:59:53,080 --> 00:59:56,640
Speaker 2: DNA virus, so it can integrate into the DNA of

1151
00:59:56,680 --> 00:59:59,520
Speaker 2: your cells. That's actually how it lays dormant for so

1152
00:59:59,600 --> 01:00:03,200
Speaker 2: long and goes and it's sitting in your DNA and

1153
01:00:03,280 --> 01:00:06,120
Speaker 2: just waiting to be reactivated and then for reasons that

1154
01:00:06,120 --> 01:00:09,280
Speaker 2: we don't fully understand, stress sometimes like UV light or

1155
01:00:09,320 --> 01:00:14,120
Speaker 2: whatever will reactivate this. That's actually why shingles appears in

1156
01:00:14,160 --> 01:00:17,400
Speaker 2: a patch in your body, because it's traveling out from

1157
01:00:17,440 --> 01:00:19,920
Speaker 2: a nerve, like it's coming from the nerve that innervates

1158
01:00:19,960 --> 01:00:22,400
Speaker 2: that patch of skin. It's also why it's so incredibly painful.

1159
01:00:22,840 --> 01:00:25,840
Speaker 2: So it integrates into DNA, and it's neurotropic, so it's

1160
01:00:25,840 --> 01:00:29,240
Speaker 2: a virus that likes to infect nerve cells. So you know,

1161
01:00:29,480 --> 01:00:33,960
Speaker 2: dementia nerve cells integrates into DNA lasts a really long time.

1162
01:00:34,160 --> 01:00:37,960
Speaker 2: Like I'm there's a lot of convincing push here, so

1163
01:00:38,200 --> 01:00:39,960
Speaker 2: I see no reason not to get this vaccine.

1164
01:00:40,120 --> 01:00:42,360
Speaker 1: Yeah, I mean I think that in some sense, there's

1165
01:00:42,360 --> 01:00:43,960
Speaker 1: no reason not to get this vaccine. There are other

1166
01:00:44,000 --> 01:00:47,120
Speaker 1: reasons to get this vaccine other than the dementia risk.

1167
01:00:47,280 --> 01:00:50,240
Speaker 1: And so in many ways this feels to me like, yes,

1168
01:00:50,280 --> 01:00:53,360
Speaker 1: it's one more thing, but also it's just interesting and

1169
01:00:53,400 --> 01:00:55,880
Speaker 1: it's an interesting thing to think about as we contemplate

1170
01:00:55,960 --> 01:01:00,280
Speaker 1: what causes some people to have dementia versus first is

1171
01:01:00,360 --> 01:01:04,200
Speaker 1: not having dementia. So yes, if my mother in law

1172
01:01:04,240 --> 01:01:08,120
Speaker 1: is listening, you should get the shingles vaccine, like I've

1173
01:01:08,120 --> 01:01:08,680
Speaker 1: been telling you.

1174
01:01:11,600 --> 01:01:14,160
Speaker 2: All Right, that's it for us today. Stick with us

1175
01:01:14,200 --> 01:01:17,560
Speaker 2: next week when we'll ask what's the deal with mRNA.

1176
01:01:19,640 --> 01:01:24,080
Speaker 1: Wellness Actually is produced in association with iHeartMedia. Our senior

1177
01:01:24,080 --> 01:01:27,800
Speaker 1: producer is Tamar Avishai. Our executive producer at iHeart is

1178
01:01:27,880 --> 01:01:31,480
Speaker 1: Jennifer Bassett. Our theme music is by Eric Deutsch, and

1179
01:01:31,520 --> 01:01:33,600
Speaker 1: our content is for educational purposes only.

1180
01:01:34,320 --> 01:01:36,560
Speaker 2: If you like the show, help other people find us,

1181
01:01:36,920 --> 01:01:39,680
Speaker 2: leave a rating and review on Apple Podcasts or your

1182
01:01:39,760 --> 01:01:42,520
Speaker 2: podcatcher of choice and help us spread the word about

1183
01:01:42,520 --> 01:01:45,560
Speaker 2: the show. You can follow us on Instagram at Wellness

1184
01:01:45,600 --> 01:01:48,680
Speaker 2: Actually pod and don't forget we want to hear from you.

1185
01:01:49,200 --> 01:01:51,720
Speaker 2: Head over to Wellness Actually dot fm and leave us

1186
01:01:51,760 --> 01:01:54,360
Speaker 2: a question for our mailbag or suggest a topic for

1187
01:01:54,360 --> 01:01:55,040
Speaker 2: a future show.

1188
01:01:55,800 --> 01:01:57,680
Speaker 1: We'll let the influencers have the last word.

1189
01:01:58,160 --> 01:02:00,800
Speaker 9: I got my results from my pronuvosk and if you

1190
01:02:00,800 --> 01:02:02,439
Speaker 9: don't know who that is, it's a full body MRI.

1191
01:02:02,800 --> 01:02:05,760
Speaker 9: So we're gonna go over what they found. And they

1192
01:02:05,760 --> 01:02:07,920
Speaker 9: found twenty six findings. So when I saw this, I

1193
01:02:07,960 --> 01:02:09,520
Speaker 9: was like, oh my gosh, there's twenty six things that's

1194
01:02:09,560 --> 01:02:13,120
Speaker 9: wrong with me. But really yes, but no. But with

1195
01:02:13,240 --> 01:02:16,120
Speaker 9: the minor findings that they did find is that this

1196
01:02:16,520 --> 01:02:18,680
Speaker 9: right here is my lip filler. It says right here,

1197
01:02:18,720 --> 01:02:21,680
Speaker 9: just like filler or botox injections. So this is just

1198
01:02:21,720 --> 01:02:34,280
Speaker 9: showing my lip filler, which is like okay,