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An Interview with Distinguished University Professor Gordon Guyatt, OC, FRSC on Chinese Traditional Medicine and Evidence-Based Medicine (Part Two)


Author(s): Scott Douglas Jacobsen

Publication (Outlet/Website): In-Sight: Independent Interview-Based Journal

Publication Date (yyyy/mm/dd): 2019/10/01


Dr. Gordon Guyatt, OC, FRSC is a Distinguished University Professor is the Department of Health Research Methods, Evidence, and Impact at McMaster University. He discusses: chinese medicine and evidence-based medicine; modern science and modern medicine; prognostic models; and PJ Devereaux.

Keywords: Canada, Chinese, Chinese medicine, evidence-based medicine, Gordon Guyatt, medicine.

An Interview with Distinguished University Professor Gordon Guyatt, OC, FRSC on Chinese Traditional Medicine and Evidence-Based Medicine: Distinguished Professor, Health Research Methods, Evidence, and Impact, McMaster University; Co-Founder, Evidence-Based Medicine (Part Two)[1],[2],[3],[4]

*Footnotes in & after the interview, & citation style listing after the interview.*

*This interview has been edited for clarity and readability.*

1. Scott Douglas Jacobsen: Now with regards to other methodologies, as you are methodologist, as others are statisticians. I remember taking a directed studies course in the epistemology of psychology, the foundations of psychology.

It was one-on-one with a professor of psychology, he was the chair of the department.  He said, “We sneak in epistemology classes into psychology. We call them statistics and methodology.”

So, in a way, both the statisticians and methodologists in medicine, it makes you a medical technologist. In that sense, what other more speculative epistemologies in medicine are coming down the pipeline for evidence-based medicine, if any.

Distinguished Professor Gordon Guyatt: When I talk epistemology to people, it is all the threats to evidence-based medicine by alternative epistemologies.

Jacobsen: [Laughing].

It the most interesting in that regard, which is a big way the world is changing. It is the prominence of China.

I joke to people that in my research outfit here. There is a Chinese invasion going on. I know, it would take me a minute to try and figure out how many Chinese and Korean students and faculty members, and postdoctoral fellows.

One of the things is, some of them come from traditional Chinese medicine backgrounds. So, there is now this split within Chinese medicine. Even so, there is Western medicine and traditional Chinese medicine, and they have different epistemologies. Even within the traditional Chinese medicine, there are some people gravitating toward the EBM epistemology, and epistemology the way I understand it,

It is the science of how we know things. How do we know that something’s true? How do we know that is not true? So, evidence-based medicine has a particular epistemology, so traditional Western sides had an epistemology that was focused on basic science and biological action now.

EBM has an epistemology that is much more focused on experiments of human beings looking at patient important outcomes, randomized trials, and observational studies. So, that’s ok, little physiology is fine, but that only gets you so far.

How do you know things? You need to test them out in human beings in the real world. So, that is the EBM epistemology for when you go to traditional Chinese medicine. They know it, because it is being done for 6,000 years. 6,000 years of experience cannot be wrong. So, that is a different way of knowing.

Some of my Chinese colleagues are trying to rationalize these two ways of knowing. I may be wrong. I may be pessimistic, but I am telling them, “You’ve got two different epistemologies here, which will never come together. They represent different ways, different ideas of how things in the world work.”

So, that is my most dramatic epistemological issue that is around at the moment.

2. Jacobsen: Historically, we can look at the Western tradition going through its developments and even regression. There was a long period of regression. Where now someone’s frothing at the mouth on the ground, we go, “That person is having an epileptic seizure.”

Go back sufficient number of centuries, and people hadn’t known the answer in their own epistemology, the answers they came to were, “They’re possessed by the devil, or a demon.”

So I mean, that is a massive regression. But things have changed, become more concrete and EBM-based. So, outside of NMAs (Network Meta-Analyses), and the alternatives coming from of East Asia and general, China in particular, are there any others?

Guyatt: So, there is something called, there is a push toward, real world data and big data. You have these huge databases. You can then use machine learning. People think that you can figure out what treatments work out in the real world by looking at this real world data.

We do not think so. So, we point to the problems with this real data. Patients may do better if exposed to one treatment versus the other. But it may not have anything to do with the treatment.

It may be because the people who took the particular treatment, you are destined to do better, they took, and the one example that I… so I’ll give you two. I’ll give you one primitive example, then one that people thought, something works or didn’t. So, the primitive example is, let’s look at hospitalization as an intervention? Does hospitalization make people better?

Well, as it turns out, people die an awful lot in the hospital much more than they die out in the community. Therefore, clearly, hospitals are harmful. So, that is a vivid illustration that because people do badly in this environment, and not so badly in this environment, it may have nothing to do with the environment.

It might be the nature of the people who got into that environment. So, obviously, we know, “No, people do not die in hospitals because hospitals kill you. It is because the people who go to hospitals are sick.”

So, that when everybody sees that it is a mistake to think that hospitals kill people. It is not too difficult. But there was another one, dramatic one of antioxidant vitamins. Vitamin C, antioxidant vitamins, have what we call observational studies, you look at a big population who take antioxidant vitamins.

A big population does not take antioxidant vitamins. You look at what happens. These antioxidant vitamins, if you looked at the report, were supposed to do good things for you. It turned out that when they did the observational studies.

People with the antioxidant vitamins had less cancer and less cardiovascular disease than people who didn’t take antioxidant vitamins. Message, we should all take antioxidant vitamins. It will reduce cancer and cardiovascular disease. Fortunately, they decided to do the randomized trials.

The randomized trials showed no difference between people who took and did not take the antioxidant vitamins in, either cancer and cardiovascular disease, and in some instances, a possible suggestion of harm.

So, it was true that people in the real world who took antioxidant vitamins had less cancer and cardiovascular disease than people who did not. It had nothing to do with the antioxidant vitamins.

Jacobsen: [Laughing].

Guyatt: What it has to do with is the nature of the people who took antioxidant vitamins were different than the people who didn’t take antioxidant vitamins, we needed randomized trials to store data. Now, there is a push with this big data of real world data, which will tell us about treatments.

People do not seem to have learned the lesson of the antioxidant vitamins example. Yes, they may do better when they’re exposed or not exposed or more primitively to lessen the hospitalization.

They’re ready to attribute it to the treatment, but it may not be the treatment at all. Sometimes, it is. Sometimes, it isn’t. We would argue that you need randomized trials to be definitive to know whether it is or it isn’t. If we believe those observational studies, we would all be taking antioxidant vitamins, too, and no one would be benefiting.

So that is an interesting epistemological debate now. Can this big data that tell us what’s true? Or we need randomized trials?

3. Jacobsen: What were some of the more overblown claims?

Guyatt: That they can tell you what works and what does not work? That is the fundamental overblown.

Jacobsen: What are some secondary ones?

Guyatt: The other things that it is useful for is, for instance, development of prognostic models. So, it is often important to say, “Is this person at high risk or low risk of something?” The big data potentially can, by having huge amounts of data, they can come up with great prognostic blocks.

So, that is something. The only problematic part is the claim that it can tell us what works and what does not work.

4. Jacobsen: Are there any other areas in professional life that you want to explore?

Guyatt: I can do something more. As I say, I am methodologist, but I work with people who do, fortunately, frontline research. That is practical. I can tell you about one of my colleagues by the name of PJ Devereaux.

10 or 15 years, probably 15 years ago, now, maybe more, PJ, started to focus on non-cardiac surgery. So, people go into surgery. They’re not going for their heart. They’re going for all sorts of other regions.

So, the first big discovery that PJ made was lots of people are having heart attacks. Nobody is noticing. The reason they do not notice is you come out of undergoing this non-cardiac surgery, which has the metaphor of running a marathon.

Most of the people who go to non-cardiac surgery have not been training for six months to run a marathon.

Jacobsen: [Laughing].

Guyatt: A matter fact, they may not have been getting out there. Getting out of their seats in front of the television set much, so, now, you put them to a marathon and – lo and behold, perhaps no big surprise – a fair number are having heart attacks that nobody was noticing. Why not?

Because they come out of the operating room, they’re sedated. They’re out of it. If they were awake, they’d be saying, “Doc, I am having this terrible chest pain,” but they can tell you they do not know. They’re asleep or sedated.

What PJ said, “Hey, wait a minute, let’s take everybody or at least these high risk people coming, and let’s do electrocardiograms. Let’s do enzymes, which tells us what’s going on too hard.” He found out that 80% of the people having heart attacks were missed.

If you did the regular clinical day, you were missing 80% of people that had heart attacks. So, that was interesting; that was important. But the issue still remained, we know what to do with you. If you come into the emergency department with a heart attack, we have a hundred thousand people studied in randomized trials.

We know what to do with that. Should we be doing the same thing with people who are having these heart attacks coming out of surgery?

Jacobsen: [Laughing].

Guyatt: Maybe, but maybe they’re different. So, now we have to find out. We have to find out what we should do about that. Now, he is done the first big important study showing that if you give these people anticoagulants, blood thinners sometimes we called them, they do better.

The implications of that, I do not think we need to stay informed that the most important things we do for people coming to the emergency room with heart attacks, is give them aspirin and drugs to lower their lipids, the fats in the blood those things. We should be doing those things.

It is clear from the results of PJ’s works that we should be doing that to these people who have these otherwise unrecognized heart attacks after they’re not a cardiac surgeon, so PJ with his work is revolutionizing the perioperative medicine.

Of all the people I work with in terms of doing the biggest impact work with immediate impact in terms of medical care and improving outcomes, PJ’s doing the best stuff.

Appendix I: Footnotes

[1] Distinguished Professor, Health Research Methods, Evidence, and Impact, McMaster University; Co-Founder, Evidence-Based Medicine

[2] Individual Publication Date: October 1, 2019, at; Full Issue Publication Date: January 1, 2020, at

[3] B.Sc., University of Toronto; M.D., General Internist, McMaster University Medical School; M.Sc., Design, Management, and Evaluation, McMaster University.

[4] Credit: McMaster University.


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