Endo Battery

Fast Charged #16. Beyond the Scalpel: AI, Surgery, and the Future of Personalized Care

• Alanna • Episode 175

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Dr. Canio Martinelli, OBGYN specialist and head of clinical programs at Sbarro Health Research Organization, discusses groundbreaking research on AI applications in medicine and surgical decision-making to improve patient outcomes.

• Research shows AI systems like ChatGPT perform comparably to resident physicians in diagnostic accuracy
• Human doctors and AI make different types of errors, suggesting they could complement each other
• AI maintains consistent performance under time pressure while human performance declines
• The "gray area dilemma" in surgery refers to critical decisions surgeons make based on incomplete information
• PULSAR study aims to decode surgical decision-making by analyzing billions of data points
• True personalized medicine must consider what "functionality" means to each individual patient
• Future AI systems could help surgeons tailor procedures to each patient's specific anatomy and goals
• Communication remains challenging when explaining complex medical concepts and statistics to patients

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Alanna:

Welcome to Endo Battery Fast Charged, a series dedicated to keeping you informed and empowered in the realm of endometriosis. Teaming up with board-certified patient advocates, we bring you the latest articles, research and insights to equip you with accurate information and a deeper understanding. Whether you're expanding your knowledge, staying updated or seeking clarity, you're in the right place. I'm your host, alana, and this is Endo Battery Fast Charged charging and empowering your life with knowledge. Welcome back to Endo Battery Fast Charged, where we power through the latest research shaping endometriosis and women's health. I couldn't be more excited to have our very first guest on this series, Dr. Canio Martinelli, an OBGYN specialist and the head of clinical program at Sbarro Health Research Organization at Temple University. Dr Martinelli, recently named FDA AACR Oncology Educational Fellow, is at the forefront of translating cutting-edge science into real-world impact. His work connects emerging research, clinical care and the future treatment for people with endometriosis, helping us better understand where innovation can truly change lives. And just as a friendly reminder, correlation does not equal causation. So let's keep our curiosity fully charged, but stay grounded as we dig in.

Alanna:

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Canio:

First of all, thank you for having me here. You already know how much I appreciate you. I mean, whatever you do is incredible for patients, for people and for us also, scientists and physicians, because research, as we already said, is about trying to find the right answer to people. So we need to resonate together, to talk together, to be much more connected when it comes to research. We're talking about one of my greatest passion, because research is a way to find solution to problems. So every research starts with what we call research question. So what is the question we are trying to address? And after we get that question, then we start to develop different hypotheses and then the goal here is trying to have the right methodology to confirm or not our hypothesis. So it's everything extremely serious and rigid, because at the end, the conclusion that we get from that, out of this, has to be applicable to people in a specific way. It means that we need to not just give an information, just give this study showed this. We need to be able also to tell them these are the data, this is the way you have to apply that. So it's then after we've published the study. That's what we call the critical appraisal. So how can I really use that data? Is it going to use the data in the same way in the world? Is it applied only to specific population, especially right now that we are going in personalized and precision medicine? We will talk about that, but so I think that it's much easier to talk about specific articles. I can tell you all the process.

Canio:

One of the most interesting for people study that we published was the one on AI applied to improve the clinical metrics for physician. Because everybody had this experience, like me as a physician. Also, explore CHATGPT to explore rather than Claude, Gemini, and, since access to healthcare is very, very hard today, as well as education for medical students, everybody, somehow we relate with this new technology, how we relate with this new technology. So our idea is let's do this scientific study in order to try to understand not just whether this new machine, these new tools, are better than physician, but I want to try to understand if the reasoning of this machine is actually different than physician, because if I understand that, I know how to find a way to match them, to merge them, how to really improve the ability for physician. But if I'm even able to understand how, the reason, I can give practical tips to people and say, okay, if you really want to have a suggestion, a tip from Chachi PT or Clodagh Gemini or Meta, you can ask it, but you need to be aware and to be able how to handle that information. That's the key, because as you go to different doctors, they can give you different information. Which one is the right one? You can pick up the right one in front of a doctor because he or she is nice or because you can resonate with it when you do with what we call large language models. So it's like very disconnected stuff. You don't have any, not just you as a patient or people, but you even as a doctor. Right now. You don't have any tips, any guidelines to say, ok, what he's saying is 100% sure, because you don't know where the data comes from.

Canio:

So what we did? We actually use different LLMs different like ChatGPT, clove, gemini, Meta, the different version and we choose 24 residents in a gynecology and we develop 60 different clinical scenarios. So we ask to the resident and we ask to LLMs to try to approach the clinical scenario, to give us an answer. We did this in a different situation because the idea is trying to see how can I use that technology and even to test the technology in different situations. So we decided to split the question following language. So we test the scenarios in English and in Italian, and then we test the scenarios even with time constraints. It means that for some question, people were completely free to answer, with all the calm of the world, the question. In other scenarios they needed to give the same question, the same answer in 10 seconds.

Canio:

And then the resident, the gynecological resident. They were actually grouped in different years of expertise, so they were the fresh one and the senior one. That was a study that was conducted last year, so during 2024,. That's important to see because the technology, especially in AI, is speeding up so much. So maybe right now the duration could be different. But I actually did another test where I saw that the metrics are pretty much the same. So let's start from the first outcome, so the first results from the study. If we took overall, the all LLM versus the all resident across all the different ages of expertise, we are basically seeing that they match as a result. So right now, specifically to that, 60 scenarios, ai is able to give you almost the same answer of residents, not senior physicians.

Canio:

But then we did what we call sub-analysis, so within the data we try to explore correlation in order to get practical information. So we understood, for example, that if you compare the senior physician at a resident in gynecology to the average of LLMs, the senior are much, much better than the average LLM. But if you take the best LLM, there was one that you need to pay. That one is almost same accuracy of the senior residents. But the other sub-analysis was during time constraints, because when you put pressure on people and residents you see that their performance metrics gonna really reduce a lot, while the LLM metrics are staying stable. And then we also saw the pattern, what we call the pattern of mistake of humans compared to machines, and we saw that the pattern of mistakes is actually different. So that's important because what you do as a physician or a people like, whenever you inquire those machines, they come to a conclusion the different one you take. So who is the one that is right, me or the machine?

Alanna:

Right.

Canio:

The problem is that being able to predict whether a machine is wrong is as much as hard than being able to predict whether a physician is wrong. So the real take from the study is maybe for people right now are able to give you general advices in the right way, but if you really want to have the answer from yourself, it's still not well performed. Because if you want to know something about anything issue, any field, you want to have a kind of recommendation, it could be okay using the stuff. But if you have a problem and you want to have a solution that is really perfect for yourself in your specific time, your specific situation, well that's not the right tool. And when it comes for physician, it depends the way you use LLM, because basically this stuff is just an advanced researcher. It depends the way you use LLM, because you basically this stuff is just an advanced researcher. It depends the way you write the prompt. So in the way you give them the data to analyze the case, the more option the machine has, it's easier for them, for the machine to make a mistake. And that's where it comes.

Canio:

Expert physician, expert physician, the one that's not just you know, mastering one technique and offering only one technique to patient or one treatment. The great physician is one that knows a lot of different kind of management and is able to apply that specific management or treatment to your specific condition of life. So in this matching course, you know, senior physicians are so much better than LLM. But here again, we don't need LL to substitute humans. We need LLM in maybe, for example, when you have pressure, when you have a high workload, you need to take a decision. It can help you to give the data that you need to take the decision. So it's much more like a co-piloting and when it comes to people, it can be an assistant to check on it, like, okay, did you check this, did you? But not just for giving medical advices, but in order to get the data that then will be incorporated by the system and the physician is able to track the data in a more clear way. Here is all about having the chance to see the pattern. The pattern of the data coming from a patient is critical for the physician to act in the best way.

Canio:

So this first study basically, is showing that if we need to train a system, we know that the way AI and doctors so humans reason is different. We need to be aware of these differences and then, when we want to implement a system that is AI powered with human in the loop, it means that there is a system that is taking care about you you need AI that is called supervised. So it means that I need to teach the AI whatever I know and I need to set boundaries, because these LLM in the studies were like commercial LLM, so everybody can get access to that. The next step is going to be being able to use that technology but, more importantly, being able to teach and set boundaries. So it says you cannot go too far away from that. If the patient really want to have that kind of answer, you're not able to give it.

Canio:

So just says you cannot go too far away from that. If the patient really want to have that kind of answer, you're not able to give it. So just say refer to the doctors and we need them to train time after time, year after year and in a way that it can take care of the most annoying stuff and humans can be much more oriented. They can be used in very complex scenarios, but overall that's even a challenge for humans, actually, because you are understanding here that as a human, you need to go faster here. As a physician, you need to be extremely trained to think. You need to be extremely trained not to analyze the data, but to talk with people, because they are getting to us. I'm not saying that I will make a diagnosis. I'm saying that if we do not evolve as humans, ai will get better than us.

Canio:

That's for sure, because it's all around the data and this study is showing that Average AI is basically average resident in a gynecology. It's kind of you know, push even for next generation, young generation. You need to do better than me because I'm going to build a system that better than me. If you're not better than me, my system is going to be better than you as a physician, and so it's kind of a research. It's kind of scary because if you look at the data, basically what we're showing is that actual LMM can be even useful in learning for physicians.

Canio:

But I want to give hope. You know that if you read the discussion of the paper, it's all about how can I get this data to tailor the new AI model to help physicians. And with those data now we're going to the next step. That is the ongoing paper that we are basically writing right now. I can't say the name of the study because actually it's going to be published very soon. It's called Pulsar Study. It's a study where we are right now building the architecture, the digital human architecture, of the system that we will develop in the future. And that's very cool because it comes from my heart. My passion is I don't know if you have any question for the other study, otherwise I jump in this new.

Alanna:

Well, I think I want something to point out in this study and maybe you touch on this more. This isn't necessarily a replacement for doctors. It's a tool to be helpful for providers and for patients, not a replacement for those that might be a little bit scary. You're saying that this study is emphasizing that it's not taking over, it's making them better.

Canio:

Yes, absolutely. You know, I know education medical in healthcare is crazy, but that's a cool thing. We need to keep pushing, because whenever we understand, whenever actually we explore this system, we are basically exploring a way to understand how people reason even more, because whatever we are doing in the computer science applied to healthcare is actually just transpose what humans already do in their brain and build a artificial system that is able to do basically the same stuff and the same way. While we are exploring this new technology, what's happening is that we are even exploring ourselves much better. So it's a way that is kind of a challenge, because the more you develop it, the more you have new connection in your mind, and AI right now is not able to find something that is not possible, to find Everything new that it finds is not necessarily true. Same things in the research right, whatever we find, it's not necessarily true. As same things in the research right, whatever we find, it's not necessarily true. We need them to validate and, in real people, to see it, to show that it's really, really good. So in doing this, ai is extremely brilliant. When we tell them exactly a very limited system, we say that's how we work, we give you rules and that's where you have to act Unlikely. Right now we're not talking about ChatGPD or anything commercial available. It's a system that uses their technology, but they're a system underdeveloped, so it's still not reality.

Canio:

There are also lots of studies that are showing that most of the AI tools that have been developing in healthcare, in the first stages phases they are brilliant. Then, when they apply the tools to real situation, real hospital, they really do not perform well. Yes, that's because we need to understand, we need to validate those stuff, so it's not necessarily right. So the science is cool because it's kind of remember that you need to be humble, you know, still need to consider that you've wrong. Your research question may be wrong. If it's wrong, it doesn't mean that is bad. It means that that is not the right way. You need to find another one. And from that perspective, we are getting to like by make failure and mistake. We are getting to like buying make failure and mistake. We are getting to something much, much better and it's going to happen. It's happening very, very, very fast.

Alanna:

Interesting. So did this research then translate into what your next paper that's going to be published Did it? Was that the catalyst to this next paper, Exactly?

Canio:

Absolutely the way we, you know, whenever we do research, let's say, even needs to resonate with the money and the funding that we have. So we need to be strategic in what we do because we need to worth every penny, every dollar we have. So we need to move in that direction and every evidence that whenever you publish a paper, you, whatever people read, there are data. But behind that paper there is a team of people that recently talk, so they make briefing and meeting, they analyze data, contextualize data within the actual medical knowledge and the future and the different other science. How can I do that? So it's everything you experience coming from the paper. So that's why whenever scientists publish paper, they're excited.

Canio:

Maybe nobody's going to read the paper when it comes to people like, not the medical community, but for us it's amazing because behind that paper there are ideas, confirmed, dismissed. People get gut hungry, you know. And then there is all the all of this stuff. So there are humans, life, working for something and challenging each other to do something different and change the actual like management. So it helped us to jump to the other program that actually is not just this paper, but it is our history, other papers, like they merged together and they jumped in this new paper, that is, a theoretical paper with some computational predictions or some preliminary data. That is called PULSAR. That's basically meaning probabilistic and user-centered learning for surgical adaptive recommendation.

Alanna:

That's a long word for a lot of us, oh my gosh, alan.

Canio:

I think I could speak about that for hours, because it's really what really drives me. So please stop me if I keep going like without any direction. But it's really something that I, you know, whenever I go to sleep.

Alanna:

I think about that, believe me. So the research is like. The words in the title itself are more manpower than I want to put in half the time just to think of what it is. What is this paper, though? What does it entail? Because I'm sure that it's like a new pathway to what the future holds for patient-centered care.

Canio:

Okay, Maybe we can talk about that by having a conversation. I want to try to introduce the things by using metaphor.

Alanna:

Okay.

Canio:

So let's say that, do you like cooking, love it. So whenever you cook, do you follow a specific recipe, the specific steps, or you go by your mind? A little bit of both. Okay, exactly, exactly. I love this answer. So sometimes you change the recipe, you change ingredients. The question here is how and what is the things that you follow to change your mind about some step. You know what I mean. Like, how do you? We're talking about your decision-making process in your mind. So whenever you decide to change something in your like, let's say, management of the dish, what is the things that really drive you?

Alanna:

If I like the flavor of something or if I don't have the right ingredient, but something can substitute it, so like knowing what I have available to me versus what I want, if that makes sense.

Canio:

Okay, it makes sense. But you know, maybe you can open your kitchen and you find a hundred ingredients. How do you pick especially specifically that one instead of another one?

Alanna:

What might work better in my mind, like what I feel like would make it better.

Canio:

That's based on what your feeling is based on what?

Alanna:

Yeah?

Canio:

And what's your opinion is based on?

Alanna:

The way I feel or the way I prefer things. Exactly yeah.

Canio:

Now we are entering what we call in this paper the gray area dilemma. So there is a gray area in our mind of decision-making process, especially in surgery, where actually we do something. Most of the process is evidence-based, but sometimes during this process there are little choices in every surgery. Every surgery is completely different than anyone else and from anyone else, because every patient is completely different than each other. So then, surgery is a specific field of medicine because it's the same time diagnosis and treatment, because you don't have all the data that you really need before the surgery. Most of the time you discover something when you open the patient and when you're there you're making diagnosis and you have to change, sometimes, your mind. That's why, when we share informed consent, there is a huge list of something that can happen, like we are doing. A surgeon that maybe you know for endometriosis they say I can resect the bowel, you can come up with a stoma, we can resect ureter, it may be possible that we have a urinary stoma, you know, but there is even a risk of fatality. That's because we don't have a clear idea what's happening 100% before opening the patient. When you are there, you really have the. You know the reality, so you need to adapt your decision-making process as a surgeon between evidence coming from science and then something that you have in your mind that's based on your experience, your intuition, something we're still not able to identify. That's the gray area dilemma, and the best surgeon is the one that somehow has that gray area that is much more efficient and effective when, at the end, it takes the choice for the patient during surgery. It doesn't mean that it's necessarily the one that is the oldest one, but it's the one that the sum of his option is actually the result as a result, as the best outcome for the patient is actually the result as a result, as the best outcome for the patient. And that's why surgery is extremely hard to teach, rather than compared to any other field of medicine, because it's hard to explain, objectify and structure the gray area of the decision making of the surgeon.

Canio:

Now, where this study, you know, fit, this study fit in, uh in in all of this. So the thing here is I need someone, since, starting from the previous study where we wanted to try to codify the uh decision-making pattern of the machine, here is trying to codify the decision-making process of the human by using AI. The goal is using AI technology Uh, I'm not going to go into the details in this but the human by using AI. The goal is using AI technology. I'm not going to go into details in this, but the goal is using AI in order to detect and to study how the surgeon behaves in all the different scenarios, all the different surgeries. But the most important thing is then to anchor and to attach to this reasoning and evaluation what's happening to the patient, because we need to anchor the algorithm to the outcome for the patient, because the matrix here is I can judge whether the action of a surgeon was better than the other one by relating his action or her action to the outcome of the patient. And it's not just that, because then any patient is different. Someone could say exactly the goal is try to destroy them.

Canio:

Let me say that the patient in all little pieces. Those are data before the surgery, with all the imaging, objective evaluation, clinical examination, and so we get some of the data. Then, during the surgery, we have a video, so we get information coming from the video getting during the surgery, and that's another package of data. The other thing that's very interesting is that whenever you do surgery, you are modifying the anatomy. You are modifying the biology of the body, so what's happening is that we need to be able to relate our action to the modification in that specific patient.

Canio:

Science is always working on understanding the differences, so all of this little action will generate differences between the patient, and so at the end you have to like try to picture that at one patient during all the process, so assessed in a dynamic way, so pre, during surgery, after surgery, will be actually represented by billions of data. And these data are actually belonging they're not like random they are belonging to specific compartments and that's a called layer, and that's where actually multi-army comes in. So we don't just need to get the data, but we need to organize in their own compartment and then we need to be able to relate them in a structured way. Each of them needs to be like part of a compartment and then we need to in this billion of data. We need that to understand what was the strategy that actually, in that specific case, helped the surgeon to get to the best result for the patient. And then in the next case, the machine would be.

Canio:

After doing that, with different physicians and different people, the machine would be able to guide us and also the patient, by little by little, say okay, if you do this, be careful, because last time you did that, you did the same stuff.

Canio:

Although it's anatomically and technically correct, let me say that was not good extremely good for the patient. Because whatever we need to do is to anchor this to the patient outcomes. Most of the time if you talk to physicians, to surgeons, they say I did this and it was perfect. The other one did another stuff that was indeed perfect. So here is not about doing what physician or surgeon says is perfect, but is to anchor any, every single step to the patient outcomes. And that is going to improve even understanding of how a surgeon thinks. In that gray area is even medical education and also in the shared decision making, because at this way, after some times, before the patient comes to me, you know, before the surgery I can, by looking at the data, say okay, that's the best choice for you among the all possible ones and it's going to give you that specific risk and that specific benefit.

Alanna:

Interesting. So it's like a, it's a guide for the doctor to give the patient the best outcome while also giving, like the patient, a voice in the long-term outcome. Exactly, exactly. That's great and that's like something, because I think we always say it's not a one-size-fits-all, especially with endometriosis surgery, and so just tailoring the approach to the actual patient prior to surgery for better outcomes long-term, is that kind of Wow, I was so confused when I explained all of this and you got everything very clearly.

Canio:

I don't know how you did it. I was so. It was a mess when I said that, because I follow my inner flow, you know what I mean.

Alanna:

No, I picked it up. I just am like fascinated by the fact that you know, as patients, we are constantly looking at okay, like, is this bowel resection right for me, or is this whatever resection right for me, or is this whatever Is it right for me? And this is just a good, more educated way of approaching a surgery and making it truly multidisciplinary from, like, the AI standpoint, mixed with the provider, mixed with the patient, the outcome being better. This is crazy to me.

Canio:

What you said. See, this conversation is beautiful for this reason, because it's I'm learning something from you and I or treatment, we take our decision by looking at data coming from. We say, population study, so all the data we have. We say, ok, you have to do that because the chance of success is this or because it's best for you. But OK, the question is how, as a patient, how am I similar to the population that was using in the study that generate the evidence? And that's where medicine is not precise, because the evidence are coming from a set of data that not necessarily are the same of the very next patient that is in front of me. And when it comes to surgery, it's even more difficult, because I don't give the same pills to everybody, but actually I'm tailoring the surgical procedure to the specific patient and that's unique in any patient. So, whatever you introduce a decision and the differences, you are making the decision-making process much more complicated.

Alanna:

Yeah.

Canio:

And very hard to understand.

Alanna:

Is this what? When you did this paper, were they seeing video or AI like virtual reality ways of doing the surgery, or was it just words Like was it just feedback?

Canio:

through. That's a very, very interesting question because it's like it's actually a very detailed question and so I need to jump necessarily to details. So in order to design, first of all, you start by designing the algorithm that is more efficient, Because in order to do something, you can do it in different ways. Then you have to find the one that really is tuned in the right way. So this starts as a theoretical study coming from observation that I had personally in my life experience as a physician and scientist, Because one of the fantastic things you know, if you really want to judge I don't want to say judge, but if you really want to have the real feeling of a surgeon, I think it's based on how really he traveled in his life. So because as much she has or she has been exposed to something different, then he can understand really what's the best things. Because whenever you are in the best center in the world, they're going to teach you that's the right method and that's okay because it comes from a school. But you need to get to that conclusion after you see different methods and you start to believe actually, that is to see that there are surgeons that are doing different kind of techniques and they still have treating in the best way possible and patients are happy about that. So the thing is coming from two problems. So evidence, real problem, where you see all of these, a surgeon in school says that they are doing the best things for the patient.

Canio:

And then methodological problem. That's coming from my research field, where actually you have to question everything you do. And how do you stop questioning what you do when you have no more grade zones? How do you not have grade zones? You start to get the data and start to, mathematically speaking, give a system for each of the data you come in game. So the question is what kind of data are there? The kind of data, every kind of data. So they comes from imaging, they come from talking, so word they come from. It's called finite element analysis. So what this conceptualization thinks about is that we get the data from ultrasound or RMI and we translate those data that are basically pixel in a system, a computer that is able to simulate the real functioning of the anatomy, but in a functional way, so you can simulate what's happening. You say, okay, I want to do this procedure. You simulate the procedure and you see that something else is happening and that's why you can tailor the surgery before doing the surgery, and then it's going to tell you that the things you're doing is good.

Canio:

Well, this is easy when it comes to cancer. So oncological surgery, because the goal is overall survival. But when it comes to function, it's a completely different stuff, Because how do you really assess functionality in women today? By some questionnaire what is functionality? It's actually really. It's different for every woman. So, for example, I can say that for a young athlete woman, functionality is being able to have no pain and to be as much as she can and give the best performance. For an old lady, maybe the functionality is able to hold the hands of her grandson. So even if she's pained, it's not a problem. So is everything based on this question? How can I represent mathematically people's problem and human's problem? Because whatever you are searching, you're human acting and not human. And so when the study will be published, the cool things are going to be the architecture of all of this, because it's going to be extremely detailed and every step is connected to another one.

Alanna:

And then there are loops that reinforce and retune all the system again connect it to another one, and then there are loops that reinforce and retune all the system again. It's like taking both the visual of the scans and then you're taking the patient. Does it account for, like patient symptoms? Does it account for, like all of the functional things that are at that point failing them for lack of a better word and taking that into consideration and giving you or the provider the approach that's going to best suit them long term in a visual manner? Or is it just like spelling it out, it's like a checklist, or is it like a visual aid for them?

Canio:

And that's where it comes. Another project, right, and that makes me feel that we are going in the right direction. This is something very, very, you know, exclusive, preliminary, because here comes the second point communication. And do you really understand whether what you're telling people patient you're really able to explain in a clear way? Are you able, really you know, whenever you say the chance of success is 80% and the failure 20%? Okay, and the patient said that every patient will believe they're going to be in the 20%.

Canio:

So there is a problem here, even in communication, and that's another part of the problem that is not focused in this paper, but it's another project we are leading, that's translational communication in the medicine, and we are working together with visual artists, so they are helping us in trying to understand how to translate complicated medical stuff in something that people can really understand, but not logically understand, even emotionally understand.

Canio:

They need to understand that, because when you say 80, 20% are very, you know, great data, but the 80% is not just the 80%, it's also what's happening if you don't do that kind of procedure. So if you don't do that kind of procedure, so if you don't choose that procedure, something else is going to happen. So it's giving the right let's say even emotional way to pure great data? The answer for me I don't know. I'm working together with the visual artists, so people that work with that are inspired by emotion much more than me, that have been exposed in life to something different than me. So in science and that's the beauty of the United States, I have to tell this Whenever you work in university, you have all of these academic people working together and give their point of view and really, at the end, try to do something that is really effective, not just beautiful chrome. It needs to work out.

Alanna:

Right, I'm excited to see where that one goes, because I feel like that could change the narrative of outcomes.

Canio:

Yes, which?

Alanna:

is exciting. That's a wrap for EndoBattery's Fast Charge this week. I hope this episode left you inspired and empowered to continue advocating for your care and encourage that research is happening and change can happen in our lifetime. Make sure to join us next time as Kanyo sits down with us to explore more research that's being done. Until then, continue advocating for you and for others.