Cause-effect Relationships in The Medical Field

My current plans for the future include an undergraduate major in neuroscience and then medical school. After that I would like to complete my residency in neurosurgery. A career in this profession or at least this general area is the only thing that I can see myself doing. It is an extremely rewarding profession with lots of opportunities to help people. Watching people with cancer and other diseases makes you want to do something about it, for example my mom had cancer. Experiences like this are really eye-opening. The combination of this and my curriculum during high school is what has convinced me that this is what I should do with my life – Science classes are the only ones during high school that I have truly enjoyed. In everyday life logic is important and can be very helpful, but it is of vital importance in the medical field. Cause-effect relationships are everywhere you look, but they are especially evident in this profession. Knowing how to use these relationships could make one’s job a lot easier or even more importantly they could save someone’s life.

Causality is the first method to consider because it is the simplest method and is essential to understanding the rest of the methods. Cause is defined as a set of conditions that bring about an effect. This effect can sometimes be desirable and other times extremely undesirable. The effect however is very dependent on the multiple variables that may be involved in the situation. For example, if you have a car driving along on a very icy road and it goes into a ditch most people would assign blame on the icy road. However, if you start to consider other variables, going into a ditch could be the effect of faulty breaks or of reckless driving. This applies to my desired career just as effectively. If you’re performing surgery and cutting into some with a scalp, there is potential to do a lot of damage. Due to this if something goes wrong it is generally considered the fault is the doctor’s, but it could actually be due to the thickness of the tissue or one of many other variables, so if less pressure was applied to the thinner tissue the same problem may never have occurred.

This network that takes all of the possible variables into account is called a casual network. As you can see from the examples changing the various variables can bring about many different results. So, by applying various amounts of pressure you can figure out the amount of safe pressure on a cadaver before actually performing surgery. As well as all the necessary conditions to the situation there are also many unnecessary conditions. Some unnecessary conditions would include the time of day, or the personality traits of the patient. These are variables that should be eliminated to give you a set of necessary and sufficient conditions.

The doctor with the scalpel is signaled out as being the cause to establish the normal state of a system. A normal state is found through the historical information regarding an object. So in this case the patient’s brain was in its normal state before the scalpel went into it because this is how it always was in the past. After the scalpel stabs the brain, the brain goes into its abnormal state, which is a drastic change in the normal state regarding an object. A change of state is a very common occurrence. For example a rock breaking a window is changing the window from a normal to an abnormal state or when a patient gets a disease they are going from a normal to an abnormal state.

Going back to the idea of cause we add two new terms – precipitating cause and remote cause. A precipitating cause is the object or event directly involved in bringing about an effect. A remote cause is something that is connected to the precipitating cause by a chain of events. In the case of the rock and the window, the precipitating cause is the rock, but the remote cause could be the person who dared the person who hit the window. In surgery, any mistake would be mine, but a remote cause could be lack of sleep if someone kept me up all night. Being able to apply everything involved with causality is a useful tool that can lead to doing your job quicker and with fewer mistakes.

John Stuart Mills furthered the concept of causality with his five methods of experimental inquiry, also known as canons. The five methods are the method of agreement, the method of difference, the joint method of agreement and difference, the method of residues, and the method of concomitant variations. These five principles are what many people basis their causal inductive arguments off of.

The method of agreement looks at two or more instances of an event to see what they have in common. This can be used to illustrate someone’s personal tastes. Say you listen to a piccolo and don’t like it, then you listen to a trombone and like it, then you listen to a cell and dislike it, then you like the French horn. From this set of conditions you can conclude that the person in question likes brass instruments because that’s what all the instruments they like have in common. In the medical field you may have multiple patients come in with the same disease and figuring out what they all have in common could be the key to curing their disease by finding the cause. For instance they may all have eaten the same foods, work at the same place, or take the same pills. By finding out what they all have in common you are able to infer the cause of the disease.

However this method is not conclusive because the three patients might have other things in common that are overlooked. Perhaps they could have eaten with dirty utensils. There is also the possibility that two different things cause the disease, and not the thing – same food – they all had in common. Two of them might take the same pills and the third one works with dangerous chemicals. In this case these two conditions may cause the disease even though the third condition is what they may all have in common. In cases like this background information about the various conditions can help a lot. This is why taking a complete patient history on any new patient that gets admitted is very important.

The method of difference is similar to the method of method agreement but instead of looking for instances of an event in common, you look for instances of an event not in common. If you have one person drive a group of people to the hospital after they got food poisoning from eating at a restaurant you can find out what all of them ate, even the driver who does not have food poisoning. Say they all had steak, vegetables, and red wine, and everyone but the person without food poisoning had fries, you can conclude that the fries was the cause of the food poisoning. This is because you look for a single condition that was present when the effect occurred and that was absent when the effect did not occur. This method provides a sufficient condition but still is not conclusive, due to the fact that other possibilities could have been disregarded or overlooked.

The joint method of agreement and difference combines the method of agreement and the method of difference. If two or more instances of an event have only one thing in common, while the instances in which it does not occur all share the absence of that thing, then the item is a likely cause. This method could be very useful in surgery. If you’re operating on a patient with a problem and you need to figure out which of the five different nerves fixes the problem. In this case you would think back to patients in the past with the same and similar problems. Then you could make a chart of the various patients from the past and which nerves when tampered with fixed the problem and which nerves did not. From this chart you would look for the people that had the same disease and which nerve could be used to fix the problem that they all had in common. Then you’d look for all the people with other diseases and check to see if all of their diseases could not be fixed by the nerve used to fix the other people’s disease. If there turns out to be an instance of this you would know which nerve you have to manipulate to fix the disease. The joint has method has a far higher possibility of being correct than if you use either of the first two methods alone. The joint method allows us to assert that the problem with the nerve was a necessary condition for the disease, not just a sufficient one.

The fourth canon by Mill’s is the method of residues, which subtracts from a complex set of events those parts that already have known causes. The theory is that whatever remains – the “residue” – is a likely cause of the remaining effect. This method is highly effective in the medical field, and is why patient medical history is taken. Suppose a patient that comes in has a headache, is vomiting, and has a rash, the first thing to do would be to find out what they ate last. Say they ate a rare steak, fries, vegetables, cake, ice cream, and coffee, you can use this list of food and background knowledge to figure out what food caused which problem. If the patients sometimes get headaches from ice cream you could infer that the headache is from the ice cream. Then you may find out that the patient has never eaten cake before, due to this finding the best conclusion is probably that the patient is allergic to an ingredient in the cake, which is most likely causing the rash. Then as to the vomiting, out of the list of food the patient eating rare steak is most likely the cause of the vomiting because it may have been undercooked and caused food poisoning. This method is mostly based on inferences but can however be useful tool in determining what to test for and how to test for it.

The fifth and final canon is the method of concomitant variations. This method looks for two factors that vary together. This is basically saying that if a variation of one part of an event accompanies a variation in another part of the event, then the two parts are probably casually connected. To do this we look for correlations – a correspondence between two sets of objects, events, or sets of data. A good way of symbolizing this would be to say A B C occur together with X Y Z. A± B C results in X± Y Z, with the ± representing a shift. Therefore A and X are causally connected. In other words say you’re in a car and you’re quickly accelerating, you may notice a funny noise. As you take your foot off the pedal you would notice that the sound goes away. Then you may decide to vary the pressure on the pedal to see how the noise varies in loudness and intensity depending on the amount of pressure. This would be concomitant variation. This could also be very effectively useful as a doctor. If one patient starts taking a set dose of pills to get better, and other patients take other set does to try and get better, you can then compare who got better the quickest to see which dose is the most effective dose for a quick recovery. In the medical field, the method of concomitant variation is best used as a testing method to improve things for the future rather than get immediate results.

As useful and versatile as Mill’s method is, it does have its weaknesses. Say you wear a red shirt on Monday and get hit by a car. Then on Tuesday you wear a red shirt and get hit by a bike. Then on Wednesday, you wear a red shirt again and get hit by a motorcycle. You conclude using the method agreement that wearing a red shirt is what caused you to get hit, when in fact it was probably due to your own or the driver’s stupidity. There are similar fallacies for all of Mill’s methods. Despite these fallacies Mill’s methods are still helpful in discovering correlations and potential causes. However getting started can sometimes be difficult because they rely quite heavily on background knowledge. Due to this it’s difficult to discover new cause and effect relationships and easier to verify already existing relationships. For example, if you’ve never seen a factor before, you can easily overlook it. Finally, Mill’s methods only have the ability to show a correlation, but correlation does not guarantee causation. The very best it can do is reveal a probable case of causality. Revealing probable causes is helpful, but provides no real explanatory power, which takes us beyond cause-effect relationships. You have to develop theories and hypotheses to provide any real explanation and this takes us into the realm of scientific reasoning.

All of this teaches us that logic is not useless and can in fact be an extremely useful tool in everyday life as well as every possible work field. I can see myself using all of these methods once I’m out in the work field as well as during the time I’m working towards the work field. I believe that knowing how to effectively use these methods will allow me to become a better physician/ surgeon than I would be able to be without knowing these methods. It is important to always keep in mind the limitations of these methods, relying too heavily on them could be dangerous. They are a great basis for beginning any type of scientific inquiry and should be usefully amply in this type of situation.