So, in this next module, we're going to talk about the discussion section of your
manuscript. In the discussion section, in terms of the
writing, it really gives you the most freedom of any of the other written
sections. So, it gives you the most chance to put
your good writing on display, All of the things we've been talking about
in this course. Of course, since there's so much
flexibility, it actually is the most challenging to write.
But challenging in a good way, Cuz it's really going to get you to really
practice your writing skills. So, follow all the good rules, the rules
for good writing that we've been talking about in this class.
And in the discussion section, you're kind of, remember we had that cone in the
introduction section, You're kind of inverting that cone.
So, where you left off in the introduction section was with this specific question
that you were trying to answer or the specific hypothesis that you were trying
to test in your study. So, you start the discussion section by
saying what happened by answering the, that question, by answering that
hypothesis. So, you asked a question in the
introduction section, you answer it right at the beginning of the discussion
section. We found that is the most common way to
start that discussion section. Then you're going to, you've given what
you think, your answer to that question is based on your data, then you're going to
support that conclusion with your data and other people's data from the literature.
So, give all the lines of evidence. Say how your results fit in to the context
of the literature. Then you're going to defend your
conclusions. So, this is the limitation section, the,
the obligatory limitation section of your discussion section.
You want to anticipate the criticisms that people might have, the reasons that they
might disagree with your conclusions and defend your conclusions.
And then, at the end, you're going to go broad.
So now, you're getting to something, you started with something specific and you're
going to something very general, you want to give the big picture,
What are the big implications of your research.
This is where you want to give implications, recommendations, get the big
picture, take- home message, give your clear, take-home message.
So, in other words, discussions to tell you what do my results mean, and why
should anyone care? And that's a very important point.
A lot of times, people will spend a lot of time saying what the results mean, but
they spend too little time saying why those results are important.
That means if somebody that's outside your immediate little nitch area in science
goes to read your paper, their not going to know why they should care.
You got to make them care. So, here's one kind of way of organizing
the discussion. Here's kind of the elements that most
discussion sections will contain. And, of course, again, it's a little bit
discipline-specific exactly what goes in that discussion, but these, I, I'm giving
a fairly general kind of picture here. So again, you want to start the discussion
section with something like we found that, and then you answer the question that you
ended with in the introduction section. What were you, your aim was or what your
hypothesis was. What did you find relative to that
[unknown] You're gonna going to what the data mean at that very high level.
Give the big picture of one of one of results mean.
You want to clearly and explicitly state if the findings are novel.
You want to point that out for your readers.
You may also have some key secondary findings.
Oftentimes we, you know, we do a study to, to find one thing.
And we find some other interesting findings in the process.
So then, you're going to state those other key secondary findings.
So, you're going to give your findings. And then you're going to put all of that
in context. So, this means, here's where you get into
some detail that I didn't want you to put into your, into your introduction section.
So, you can give possible mechanisms or pathways that might explain something that
you're seeing in your data. Go down, you know, one level in the
biology, for example. If you're looking at people, you might
talk about the genes and cells that might be involved.
Maybe you didn't measure any of that in your study,
But maybe here are, here are the potential mechanisms that explain what I'm seeing
Compare your results with other peoples's results.
So, this is where you get in to dive into the literature and see what's out there
already on this topic. How you, how do your results fit in?
Are they confirming other results that are in the literature?
If they're not confirming, if they're in contrast with other results in the
literature, why do you think your, you got different results?
So, that's when we kind of put everything into the context of the wider literature.
So, that's, right, there you've got the bulk of your discussion.
Somewhere,, as you're getting towards the end of your discussion, you need to have
at least one paragraph on strengths and limitations.
And inevitably, we actually end up oftentimes spending a lot more time on
limitations than we do on strengths. But, do try to remember to highlight the
strengths of your paper you know, you're going to make a sale to your reader that,
hey, here's all the good things about my paper.
Put that in too and, and then you're going to give your limitations.
And, and, no matter what discipline in, you're in, you're going to have to have at
least one paragraph on limitations. You want to explain why you think your
results are robust, despite some potential limitations of, of your study.
And all studies have limitations. My main recommendation on the limitations
paragraph is, the Mark of a good paper for me, is when I'm
looking through the rest of the paper, before I get to the discussion section.
I'm reading the results, looking at the tables and figures and I, and a question
comes up in my head. I'm like, well, yeah, but,, see this is
the problem with your analysis. And I, I think of these things as I'm
going along. Something I'm gonna criticize the authors
on. The mark of a good paper for me is when I
get down to that limitations paragraph in their discussion section, if they've
anticipated my concern. And they directly, at least directly
acknowledge it. Sometimes just having an acknowledgement
that they were aware of that is really important to me as a reader and a
reviewer. And if, even better, if they tell me how
they address that potential problem, that's great, too.
So, that's really the mark of a good paper.
A lot of times people write limitation section in a very generic way.
Like, oh, well, my study was small and it had, you know,
In, in another words, in a way that could apply to almost any study in that
discipline. Don't write a generic limitations section,
really point out what matters, what limitations matter, for your specific
paper. Try to anticipate the readers' criticisms.
You also want to spend a little bit of time seeing what's next. Usually again,
that goes kind of towards the end of the discussion section.
Well, some of these results need to be confirmed in future studies.
Here are the unanswered questions. Here's the studies I can think of that
come out of this study. That's really helpful to readers and other
people in your field to figure out what studies need to happen next.
So, put that in and then make sure that you also include in your discussion,
Again, this is probably going to be somewhere towards the end.
The so what. Implicate, speculate, recommend.
This is where you're saying, hey, why, why are these, why should anybody care about
my findings? If you're doing something in the basic
sciences tied to humans, what are the potential implications for people given
the big picture implications, why are my results important, why is this study
important? Tell your readers why they should care.
And this might mean you're moving in this part, you could use the word speculate,
you know, and that's okay. It's okay to speculate a little as long as
you're telling your readers hey this is where I think we can go with this.
Give that little bit of why it might be important.
And then, it's often nice in a discussion section to have one paragraph at the end,
where you have a very strong conclusion. Some, some journal lectures have a
separate section for a conclusion. But if they don't, just wrap up your
discussion section by restating your main findings.
The same finding that you opened your discussion section with, and give some
kind of final take-home message for your readers.
So, some tips on the discussion section, And again, showcase your good writing.
Use the active voice. Tell it as much as you can like a story.
Start and end with the main findings. So, the first sentence of your discussion
section should be something like, it doesn't have to be exactly this, but it
should be something like we found that, and you answer that question that you
asked, you answer the hypothesis, what did you find relative to that hypothesis you
were testing. And then, you want to wrap up your
discussion section again with some kind of conclusion where you restate that main
finding. It's okay to repeat it because you're
reader will want to really, you know, and really have it sink in what the main
finding was. Be very, very careful in your discussion
section that you don't travel too far from your data.
Now again, it's okay near the end to give some kind of speculation.
Big picture implication, how might this help you with. It's okay to step away from
your data a little bit at the very end, where you're very clear that this is your
potential implications. What I'm talking about is in your main
findings, when you're talking about your main findings.
Your key findings and possibly Your secondary findings, which you see
happening a lot is people will find one thing in their data, and yet, when they go
to the discussion, they'll tell you what they wanted to find not what they actually
found in their data. This is one of the reasons that I like to
go and look at tables and figures first before I read anybody's discussion section
so that I can make a judgment about what I think they really found, and what their
data really supports as opposed to what they're hoping it supports.
And there's always a temptation, there's something that we really want to prove,
and there's, there's a temptation to even if your data don't show it, then just act
like they showed it. So, be very careful about that.
One of the major problems that happens in discussion sections so people will really
reach way past their data. Sometimes, this means also people will
start discussing things that they don't have any data on.
And so again, don't travel too far from your data.
Stick to what you actually measured in your study.
As I mentioned earlier, I talked about this already, you want to focus, focus on
the limitations that matter. Don't just stick in any generic
limitations just cuz you know you have to have a limitations paragraph if you can
anticipate what the reader will criticize in your paper.
If you can anticipate that in, and beat them to the punch,
You will impress a lot of readers and reviewers again that's the mark of a good
paper for me. And finally, on the discussion section,
Make sure you're clear and consistent about your take-home message.
I, again, I think this gets to that oftentimes, the data,
We want the data to show one thing and they don't quite show what we want them
to. And we still want to tell our readers that
the data showed that. We still just believe in that hypothesis.
So, make sure that, and what that ends up happening is a little bit of waffling.
So, make sure that you're clear about your take-home message and that you're
consistent and you don't kind of say, well, you know, maybe it's this,
Maybe it's that. And you've got to, you've got to make it clear what you think your
data actually say. And be consistent about that and don't
start wandering again into what you wanted your data to say So, here's an example of
a discussion section. So, I have to start, to introduce this
discussion section, I'm going to start with the last part of the introduction
section from this paper, cuz that's where they state the research question, what
they were trying to do. So, this was a study looking at low fat
diets versus low carb diets, hot topic. So, they said we performed a study
designed to test the hypothesis, notice it's very easy for the reader, in that
introduction section to find what they were doing cuz they actually said we want
to test this hypothesis. That severely obese subjects with a high
prevalence of diabetes or the metabolic syndromes.
That specific group of people would one, a, have a greater weight loss.
And two, they would have a greater weight loss without any detrimental effects on
their cardiovascular risk profile. So remember, these low carb diets, you
know, encourage you to eat a lot of steak and cheese.
And they were worried, well, maybe, you know, your cholesterol's going to
skyrocket, and even if you lose weight, and you're still gong to be unhealthy.
So, that was right, there were two aims they were aiming for.
So, that was the end of their introduction section.
So, we get to the start of the discussion section.
It starts, we found that severely obese subjects with a high prevalence of
diabetes and the metabolic sydrome that's in the population they're studying,
Lost more weight in a 6-month period on a carb restricted diet than on a fat
restricted diet. So, they answered question eight.
So, they set out to see whether or not the low carb diet would be better than the low
fat diet. And, yes it was in terms of weight loss.
So, that answers question eight. Big ifs, some mechanisms now, so the
greater weight loss in that low carb group suggests that maybe it's due overall
caloric intake. So, they had some more information, some
more data, that said let's not, it's not that they, that there's something magic
about a that low carb diet. It's just simply that the diet's so
restrictive and easy to follow that people actually do tend to do a better job at
reducing their caloric intake. So, there's nothing magic going on, it's
just a diet that seems to be more successful for, just because it reduces
calories. So, they give some mechanisms, some
supporting information from their data. However, the explanation for this
difference is not clear. Subjects in this group may have
experienced greater satiety, so, so they're not sure why they ate less
calories. Maybe if you're able to eat meat, you feel
more full, so therefore, you know, you eat less.
Or maybe it just has something to do with the simplicity of the diet.
So, they're trying to give some possible reasons why they might may have seen
result that they did. So, possible mechanisms and unanswered
questions. So, they get a little bit into the
supportive details within that first paragraph.
And then, they jump to the second paragraph.
They answer that second question, which was the concern about, if you eat a lot of
steak and cheese, your cholesterol might skyrocket and you might have heart
attacks. So, they tell you, they answer that
question. Subjects in the low carb group actually
had greater decreases of triglyceride, that's good, than subjects in the low fat
group. We had greater increases in insulins,
insulin sensitivity, also, which is also good.
And there were no adverse effects on serum lipids, things like cholesterol.
So, that answers their second question. So, it doesn't appear to have bad effects
on terms of cardiovascular risk. Now, they're going to put that finding
into the context of previous studies so they go into some previous studies here.
They did find something that was actually a little bit unexpected.
They, they thought that the reason the, the low carb group did better in terms of
these heart parameters was simply because they lost more weight.
If you lose weight, your heart health improves.
However, even if they adjusted for the weight loss, it still seemed like there
was even something better about the low carb diet.
So, they had kind of an unexpected result. Yeah.
They gave some supporting, supportive details of that.
So, they're telling, they're putting everything in the context of previous
studies saying that was a little bit surprising.
So, that's our context. We get some more supporting details.
So, they are talking about their own data. Well, hey we, we are, we think this
finding is robust, because it was consistent across different weight-loss
strata. Here's, again, some more speculation about
mechanisms. And then, we start in on the limitations.
So, other uncontrolled variables, or other unknown variables may have contributed to
this effect. In addition, more precise,
We didn't measure its insulin sensitivity as precisely as we could have.
So, there were some limitations. There are some things we might want to do
in a future study. Another, and then, we're really jumping
into the, the obligatory limitations paragraph here.
So, major limitation, a lot of our subjects were taking other drugs in
addition to doing this weight-loss program and how did we control for that.
So, in other words, they try to tell you a little bit about how they tried to guard
against this limitation having any major effect on the study.
So, they did another analysis, that take, took that into account.
It didn't really change the results. So, the fifth paragraph now that you have
a strength. So, one of our strengths is we have, you
know, high proportion of flag subjects in a group.
It is a group that was previously under represented in this type of study.
And it gives some future directions, they say, well, other studies should look at
different cultures and different ethnicities.
They give another limitation. There's a high dropout rate.
So, weight-loss studies. A lot of people don't finish them, and
that might have affected our results. So, this,
This discussion section was actually seven paragraphs long, so here's the last
paragraph. So, notice that they restate in the last
paragraph. They restate their conclusion, they
restate their answers to the two questions they were asked.
And that's okay, you actually want to recap.
Taken together, our findings demonstrate that this population lost more weight
during six months on a carb restricted diet than on a low fat diet.
Carb restricted diet actually led to greater improvements in insulin
sensitivity, and again that's a good thing.
And that was, some of those improvements were independent of weight-loss And there
was a greater, also greater reduction in triglyceride levels, and it's good to get
you triglycerides down. So, they've just restated what they said
in the opening, two paragraphs, but you want to sum up what they found.
The answers to those two main questions. Then, they give you some big picture
things .. They say, well, hey, we've got to interpret these findings with caution.
This doesn't mean that everybody should rush out and go on a low fat diet because
the magnitude of the overall weight loss relative to how overweight these subjects
were was actually pretty small. So, they did lose more weight, but it
wasn't a huge amount. There was a high dropout rate, so that
makes it difficult to interpret the results of our study.
So, based on all that they give a take-home message.
So, this is the take-home message that they're really giving to physicians.
This study proves on principle, it does not provide clinical guidance.
Given the known benefits of fat restriction, future studies evaluating
long term cardiovascular outcomes are needed before we can endorse a low fat
diet. So, they are giving kind of a take-home
message. They don't want to overstate their
findings, they're saying, There's still some questions to be
answered, and we don't want to translate our findings yet into an actual clinical
recommendation. Alright. So, one more example of a
discussion section, so again this discussion section starts by stating
exactly what they did in the study, what they found.
So, in the present work, we give closed formula, this was a mathematical study,
which has a slightly different flair to it but, they're saying exactly what they did
there, they aim to give closed formula for this question and they did it.
We give closed formula for the generating function of the cumulants of the current
in the open TASEP. They say a little bit about what's novel
about their method. It's not, a, an asymptotic.
It's a, an exact method, and that's different than previous work.
They put it in context with other studies. They put in a lot of context with other
studies other people have tried to do similar things.
Again, they point out what's novel. And then the one limitation that they
point out that's, that's the main limitation that is probably important for
this study is that they hadn't actually formally got a mathematical proof of the
formula. It's only a conjecture, they're saying.
So, there's a limitation here, but they're going to defend their conclusion.
They actually are, defend it pretty strongly.
We have absolutely no doubt that our expressions are true.
That's a really strong statement. And there's, there's reason for that.
They, they have tried it in all, dozens of cases and derived it from all previously
known results. They, they have all of these reasons why
they're really sure that even though they haven't done the formal, mathematical
proof yet, that they've nailed it. This their findings, their method actually
opens up some interesting has some interesting implications and opens up some
future directions. So, furthermore, this final formula allow
us to draw some interesting physical consequences and to open new problems.
So, they're telling you, you know, hey, this is opening up a whole potential you
know, new set of interesting questions. They, again repeat, and maybe this belongs
in that last paragraph. But they, again, repeat the fact that this
is only conjectures. And they haven't done that rigorous
mathematical proof yet, But they do tell you that, they don't
think that, that rigorous mathematical proof is anything other than a long
computation that doesn't have any, they've already captured the deep mathematical
insight here. And they tell you they're presently
working on that anyways. So, that's a future study to give some
other things that they are planning to do for future studies.
So, some of our, a lot of what's next in future studies, in this particular case,
They are derivation opens up a lot of future studies.
And this, this discussion was five paragraphs long.
So, here's the last paragraph. So finally, they are going to give some
wider implications. So, they solved a problem for a particular
instance. What is TASEP?
However, this, their solution might actually apply to the partially asymmetric
case, PASEP. So, there is a potential wider implication
of their work. So, they are giving those wider
implications here. And then they give kind of a big picture
take-home message at the very end. There must exist a general and hopefully
elegant structure that encompasses all of the cases, and we are well aware that such
a structure may be difficult to discover. So, they're saying that this, this,
structure, there may be a structure that applies across all of these different
cases, but it might be really difficult to discover.
So, they're kind of given, you know, that's the take-home message, how this
fits into the bigger picture. So, a few things of what not to do in your
discussion section. So, this is real with discussion section.
It started with, this meta-analysis is subject to a number of limitations.
So, do not start your discussion section with the limitations.
Start with the, we found that, The main finding that you found.
The limitations, bury that deeper into the discussion section.
That should be, several paragraphs down. In terms of verb tense on the discussion
section, Well, first of all, your verbs should be
in the active voice for, for sure, and then, similar to the methods and results
and introduction sections, you're going to use the past tense when you're referring
to things that have already been completed.
Those are study details, results, analyses, because you've already completed
the analyses and, and other people's work. So, we found that, subjects may have
experienced, Miller et al found. But you're going to use the present tense,
when talking about what the data suggests, because the data are still suggesting
that. The greater weight loss suggests.
The explanation for this difference is not clear,
Potential explanations include. The preceding program is copyrighted by
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Please visit us at med.stanford.edu.