How To Know What Happens To A Spine

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2 I have yet to see a problem, however complicated, which when you looked at it in the right way, did not become still more complicated. Paul Anderson Exactly one hundred and one years ago, the first radiogram of the spine appeared in the literature, coincidentally in a book about thoracolumbar spine fractures (Wagner 1898). Successful imaging of fractured vertebrae is also first mentioned in this book. Since then the matter seems only to have become more and more complicated. Before the invention of imaging techniques, only fractures and dislocations with a gross deformity could be recognized as such. Successful imaging of the bony vertebrae opened the way for the recognition of different patterns of injury. About 30 years later, the first conceptualization of a spine fracture classification appeared in the literature (Böhler 1929). Subsequently, other investigators in the field tried to develop schemes, which, beginning from the pattern recognition process would eventually say something about the future of the injured spine. All this has led to an increasing complexity of the field. Those who had hoped that advancements in the imaging techniques would facilitate this process must have felt betrayed by the proliferation of schemes crowding our literature lists and driving our residents to despair. It is time to take a break and to discuss the theoretical base of this process. The goal of science is to make sense of the diversity of phenomena around us. This goal is actually not much different than the usual way human brains work. To make sense of nature means to make predictions about what will happen next. As Dennet (1996) puts it the purpose of brains is to create futures. Scientific method is only a formalization of our brain processes. But this formalization creates a powerful human machine in that it can incorporate many brains in this process of creating futures across borders and across generations. Scientific method begins with gathering information about the world through observation, with the final aim of testing predictions about how the world will react to changing circumstances. Between this initial and final aim lies the motor of the scientific process. 160

3 Observational data alone is actually nothing else than a long list of what is going on out there. The main task of science is the transformation of these lists of observational data into abbreviated forms by recognition of patterns. The recognition of these patterns allows the information content of the observed events to be replaced by a shorthand formula, which supposedly has the same or nearly the same information content. If any sequence of events can be given an abbreviated representation of this list of observations, then this sequence is called algorithmically compressible. In general the shorter the possible representation of a sequence of observations, the less random it is. If there is no possible way of expressing a sequence of observational data in an abbreviated representation, then these events are totally random. If we follow this argument, we can see the scientific enterprise as a search for algorithmic compressions. We first list sequences of observed data; then we try to formulate ways of compression that can compactly represent the information content of these sequences; and then we test the correctness of our algorithmic compressions to predict the next events in the sequence. Without the development of these algorithmic compressions, all science would be a mindless collection of every available observation. However, if we try to apply this nice, neat, and naïve notion of science to our specific field of thoracolumbar fractures, some serious complications arise. Some of these problems are: If there are rival systems of algorithmic compression how can one decide which is the best one? Theoretically, the system, which can produce the shortest formulation with the lowest loss of information content, would be preferable. However, our list of observational data is not completely independent of the next events we want to predict. In other words, if we want to predict, for example, the final kyphosis angle, the information content of our observational data may not coincide with the set of data when we want to predict the final patient satisfaction. And still worse, these two sets of observational data may not be equally compressible with the same algorithm. The brain is the most powerful algorithmic compression machine that we know of. It reduces complex sequences of 161

4 sense data to simple abbreviated forms of mental representations. However, our brain is a result of an evolutionary process red in tooth and claw in which the only thing that matters is whether a mechanism helps the survival of the organism involved or not. The consequence of that is that the algorithmic compression machines of our brains may be biased in ways, which we are not even aware of. Another problem is that this pattern recognition apparatus may work sometimes too well. It is known that our brains tend to see patterns where there can be no patterns. The most famous example of this phenomena is the so called gambler s fallacy. A gambler can ruin himself by the idea that he sees a certain pattern in a roulette machine, such as three times red is followed by five times black etc, even if he knows that the probability of getting red or black must be exactly the same each time. Are there really understandable patterns in thoracolumbar fractures or are we just making it up? This scientific method has been so successful in the explanation of the physical world around us, that the style of conventional physics has become a kind of standard for all scientific discourse. The perfectly symmetrical world of a Newtonian universe in which a plethora of observations about masses and planets etc. can be algorithmically compressed into a handful of laws of gravity, perfectly capable of predicting all events, seems to have set the standard in our minds about how real science should work. Although this picture of a perfect Newtonian universe has long been abandoned in the real science of physics, this metaphor keeps exerting a powerful influence on our minds. But the kind of data we are dealing with is probably not so easily compressible at all for reasons I will discuss. Actually, the clinical sciences may have more common ground with the science of meteorology than with Newtonian physics. Like the meteorologists, we, in the clinical sciences, deal with complex systems governed by the rules of chaos. Our territory must be included into the domain of the emerging science of complexity. We have to learn to think in terms of probabilities, expectations, strange attractors, 162

5 sensitivity to initial conditions etc, etc. Why? Let us examine the example of the study object of this thesis. The study object is a system composed of: A human being as patient Forces of trauma Methods of visualization of the resultant injury Systems of classification of these injuries that have been devised before A radiologist and a surgeon deciding on the classification of the injury The surgeon deciding on the treatment options about this specific patient The surgeon treating this patient Measurement of the outcome of the treatment. All of these elements are horribly complex systems in themselves and now they interact with each other, too. And we expect to predict the outcome of such a complex interaction? The system is sensitive to initial conditions in many ways, and these initial conditions are innumerable. For example: the social and work status of the patient, the quality of his/her bones, the mood of the software deities of the imaging machines at that moment, the degree of awareness of the radiologist of the theoretical constructs about this kind of injury, whether the surgeon had enough coffee that particular morning, the IQ of the patient, the IQ of the surgeon, whether the operating nurse is suffering a dreadful headache because of the wine she drank the evening before, whether the patient just had a terrible quarrel with the partner the day you happen to want to measure the patient satisfaction etc, etc, etc. Add to that the fact that all of these elements are constantly changing. The patterns of injury, the capability of visualization, the experience of the surgeon, the expectations of the patient, the salary of the radiologist etc, etc, etc. This is worse than the worst nightmare of a meteorologist who is trying to predict whether it is going to rain the next day. Worse than that, the meteorologist in our example is also allowed to fiddle with clouds and thunders and ionosphere and then is expected to predict not only the rain but also whether the people for whom he is forecasting will like the rain at that particular moment. 163

6 What is an acceptable outcome of this whole process anyway? We are not in the fortunate position of, say, a clinical oncologist for whom the outcome of any treatment can be simply measured with the knowledge of whether the patient is dead or alive. The patient population of Nicoll, in the 1930 s and 1940 s in the coalmines of the United Kingdom could go back to the mines although 60% of them had substantial pain. Is it realistic to expect that the patients in the industrialized societies at the end of the century behave the same way? Why should we accept a higher degree of pain and disablement by the patients with thoracolumbar spine fractures than, say, patients with a fracture of the long bones? What is an acceptable degree of discomfort after a thoracolumbar spine fracture in the first decades of the 21st century in the highly competitive industrial societies where people are expected to be more than 100% healthy if they want to be eligible for any work at all? What is a good clinical outcome after a fracture without neurological involvement or with partial neurological loss of function? Should we despair? Should we say as in the last item in one version of the Murphy s Laws of Perversity : Mother Nature is a witch! Should we say do whatever you find appropriate and then go pray for the mercy of the spine gods? I think not. Rather, we should think about our aims and methods. We should accept that we have only statistical truths about the matter; that we can only make predictions of some not quite high percentages at this moment; that our means of pattern recognition in traumatology are not highly developed; that we probably do not agree with each other well; that we all make wrong decisions quite often. But we should not lose sight of our concrete aim: to develop instruments of communication and education, which can assure that more and more surgeons more and more often make the right decisions so that our patients will end up less disabled and less unhappy. That the Frida s of the 21st century can lead lives free of constant pain and suffering. 164