The Origin and Evolution of Cultures (V)

This will be my last post about the book. Go here for a background post and my overall impression of the book – I’ll limit this post to coverage of the ‘Simple Models of Complex Phenomena’-chapter which I mentioned in that post, as well as a few observations from the introduction to part 5 of the book, which talks a little bit about what the chapter is about in general terms. The stuff they write in the chapter is in a way a sort of overview over the kind of approach to things which you may well end up adopting unconsciously if you’re working in a field like economics or ecology and a defence of such an approach; I’ve as mentioned in the previous post about the book talked about these sorts of things before, but there’s some new stuff in here as well. The chapter is written in the context of Boyd and Richerson’s coverage of their ‘Darwinian approach to evolution’, but many of the observations here are of a much more general nature and relate to the application of statistical and mathematical modelling in a much broader context; and some of those observations that do not directly relate to broader contexts still do as far as I can see have what might be termed ‘generalized analogues’. The chapter coverage was actually interesting enough for me to seriously consider reading a book or two on these topics (books such as this one), despite the amount of work I know may well be required to deal with a book like this.

I exclude a lot of stuff from the chapter in this post, and there are a lot of other good chapters in the book. Again, you should read this book.

Here’s the stuff from the introduction:

“Chapter 19 is directed at those in the social sciences unfamiliar with a style of deploying mathematical models that is second nature to economists, evolutionary biologists, engineers, and others. Much science in many disciplines consists of a toolkit of very simple mathematical models. To many not familiar with the subtle art of the simple model, such formal exercises have two seemingly deadly flaws. First, they are not easy to follow. […] Second, motivation to follow the math is often wanting because the model is so cartoonishly simple relative to the real world being analyzed. Critics often level the charge ‘‘reductionism’’ with what they take to be devastating effect. The modeler’s reply is that these two criticisms actually point in opposite directions and sum to nothing. True, the model is quite simple relative to reality, but even so, the analysis is difficult. The real lesson is that complex phenomena like culture require a humble approach. We have to bite off tiny bits of reality to analyze and build up a more global knowledge step by patient step. […] Simple models, simple experiments, and simple observational programs are the best the human mind can do in the face of the awesome complexity of nature. The alternatives to simple models are either complex models or verbal descriptions and analysis. Complex models are sometimes useful for their predictive power, but they have the vice of being difficult or impossible to understand. The heuristic value of simple models in schooling our intuition about natural processes is exceedingly important, even when their predictive power is limited. […] Unaided verbal reasoning can be unreliable […] The lesson, we think, is that all serious students of human behavior need to know enough math to at least appreciate the contributions simple mathematical models make to the understanding of complex phenomena. The idea that social scientists need less math than biologists or other natural scientists is completely mistaken.”

And below I’ve posted the chapter coverage:

“A great deal of the progress in evolutionary biology has resulted from the deployment of relatively simple theoretical models. Staddon’s, Smith’s, and Maynard Smith’s contributions illustrate this point. Despite their success, simple models have been subjected to a steady stream of criticism. The complexity of real social and biological phenomena is compared to the toylike quality of the simple models used to analyze them and their users charged with unwarranted reductionism or plain simplemindedness.
This critique is intuitively appealing—complex phenomena would seem to require complex theories to understand them—but misleading. In this chapter we argue that the study of complex, diverse phenomena like organic evolution requires complex, multilevel theories but that such theories are best built from toolkits made up of a diverse collection of simple models. Because individual models in the toolkit are designed to provide insight into only selected aspects of the more complex whole, they are necessarily incomplete. Nevertheless, students of complex phenomena aim for a reasonably complete theory by studying many related simple models. The neo-Darwinian theory of evolution provides a good example: fitness-optimizing models, one and multiple locus genetic models, and quantitative genetic models all emphasize certain details of the evolutionary process at the expense of others. While any given model is simple, the theory as a whole is much more comprehensive than any one of them.”

“In the last few years, a number of scholars have attempted to understand the processes of cultural evolution in Darwinian terms […] The idea that unifies all this work is that social learning or cultural transmission can be modeled as a system of inheritance; to understand the macroscopic patterns of cultural change we must understand the microscopic processes that increase the frequency of some culturally transmitted variants and reduce the frequency of others. Put another way, to understand cultural evolution we must account for all of the processes by which cultural variation is transmitted and modified. This is the essence of the Darwinian approach to evolution.”

“In the face of the complexity of evolutionary processes, the appropriate strategy may seem obvious: to be useful, models must be realistic; they should incorporate all factors that scientists studying the phenomena know to be important. This reasoning is certainly plausible, and many scientists, particularly in economics […] and ecology […], have constructed such models, despite their complexity. On this view, simple models are primitive, things to be replaced as our sophistication about evolution grows. Nevertheless, theorists in such disciplines as evolutionary biology and economics stubbornly continue to use simple models even though improvements in empirical knowledge, analytical mathematics, and computing now enable them to create extremely elaborate models if they care to do so. Theorists of this persuasion eschew more detailed models because (1) they are hard to understand, (2) they are difficult to analyze, and (3) they are often no more useful for prediction than simple models. […] Detailed models usually require very large amounts of data to determine the various parameter values in the model. Such data are rarely available. Moreover, small inaccuracies or errors in the formulation of the model can produce quite erroneous predictions. The temptation is to ‘‘tune’’ the model, making small changes, perhaps well within the error of available data, so that the model produces reasonable answers. When this is done, any predictive power that the model might have is due more to statistical fitting than to the fact that it accurately represents actual causal processes. It is easy to make large sacrifices of understanding for small gains in predictive power.”

“In the face of these difficulties, the most useful strategy will usually be to build a variety of simple models that can be completely understood but that still capture the important properties of the processes of interest. Liebenstein (1976: ch. 2) calls such simple models ‘‘sample theories.’’ Students of complex and diverse subject matters develop a large body of models from which ‘‘samples’’ can be drawn for the purpose at hand. Useful sample theories result from attempts to satisfy two competing desiderata: they should be simple enough to be clearly and completely grasped, and at the same time they should reflect how real processes actually do work, at least to some approximation. A systematically constructed population of sample theories and combinations of them constitutes the theory of how the whole complex process works. […] If they are well designed, they are like good caricatures, capturing a few essential features of the problem in a recognizable but stylized manner and with no attempt to represent features not of immediate interest. […] The user attempts to discover ‘‘robust’’ results, conclusions that are at least qualitatively correct, at least for some range of situations, despite the complexity and diversity of the phenomena they attempt to describe. […] Note that simple models can often be tested for their scientific content via their predictions even when the situation is too complicated to make practical predictions. Experimental or statistical controls often make it possible to expose the variation due to the processes modeled, against the background of ‘‘noise’’ due to other ones, thus allowing a ceteris paribus prediction for purposes of empirical testing.”

“Generalized sample theories are an important subset of the simple sample theories used to understand complex, diverse problems. They are designed to capture the qualitative properties of the whole class of processes that they are used to represent, while more specialized ones are used for closer approximations to narrower classes of cases. […] One might agree with the case for a diverse toolkit of simple models but still doubt the utility of generalized sample theories. Fitness-maximizing calculations are often used as a simple caricature of how selection ought to work most of the time in most organisms to produce adaptations. Does such a generalized sample theory have any serious scientific purpose? Some might argue that their qualitative kind of understanding is, at best, useful for giving nonspecialists a simplified overview of complicated topics and that real scientific progress still occurs entirely in the construction of specialized sample theories that actually predict. A sterner critic might characterize the attempt to construct generalized models as loose speculation that actually inhibits the real work of discovering predictable relationships in particular systems. These kinds of objections implicitly assume that it is possible to do science without any kind of general model. All scientists have mental models of the world. The part of the model that deals with their disciplinary specialty is more detailed than the parts that represent related areas of science. Many aspects of a scientist’s mental model are likely to be vague and never expressed. The real choice is between an intuitive, perhaps covert, general theory and an explicit, often mathematical, one. […] To insist upon empirical science in the style of physics is to insist upon the impossible. However, to give up on empirical tests and prediction would be to abandon science and retreat to speculative philosophy. Generalized sample theories normally make only limited qualitative predictions. The logistic model of population growth is a good elementary example. At best, it is an accurate model only of microbial growth in the laboratory. However, it captures something of the biology of population growth in more complex cases. Moreover, its simplicity makes it a handy general model to incorporate into models that must also represent other processes such as selection, and intra- and interspecific competition. If some sample theory is consistently at variance with the data, then it must be modified. The accumulation of these kinds of modifications can eventually alter general theory […] A generalized model is useful so long as its predictions are qualitatively correct, roughly conforming to the majority of cases. It is helpful if the inevitable limits of the model are understood. It is not necessarily an embarrassment if more than one alternative formulation of a general theory, built from different sample models, is more or less equally correct. In this case, the comparison of theories that are empirically equivalent makes clearer what is at stake in scientific controversies and may suggest empirical and theoretical steps toward a resolution.”

“The thorough study of simple models includes pressing them to their extreme limits. This is especially useful at the second step of development, where simple models of basic processes are combined into a candidate generalized model of an interesting question. There are two related purposes in this exercise. First, it is helpful to have all the implications of a given simple model exposed for comparative purposes, if nothing else. A well-understood simple sample theory serves as a useful point of comparison for the results of more complex alternatives, even when some conclusions are utterly ridiculous. Second, models do not usually just fail; they fail for particular reasons that are often very informative. Just what kinds of modifications are required to make the initially ridiculous results more nearly reasonable? […]  The exhaustive analysis of many sample models in various combinations is also the main means of seeking robust results (Wimsatt, 1981). One way to gain confidence in simple models is to build several models embodying different characterizations of the problem of interest and different simplifying assumptions. If the results of a model are robust, the same qualitative results ought to obtain for a whole family of related models in which the supposedly extraneous details differ. […] Similarly, as more complex considerations are introduced into the family of models, simple model results can be considered robust only if it seems that the qualitative conclusion holds for some reasonable range of plausible conditions.”

“A plausibility argument is a hypothetical explanation having three features in common with a traditional hypothesis: (1) a claim of deductive soundness, of in-principle logical sufficiency to explain a body of data; (2) sufficient support from the existing body of empirical data to suggest that it might actually be able to explain a body of data as well as or better than competing plausibility arguments; and (3) a program of research that might distinguish between the claims of competing plausibility arguments. The differences are that competing plausibility arguments (1) are seldom mutually exclusive, (2) can seldom be rejected by a single sharp experimental test (or small set of them), and (3) often end up being revised, limited in their generality or domain of applicability, or combined with competing arguments rather than being rejected. In other words, competing plausibility arguments are based on the claims that a different set of submodels is needed to achieve a given degree of realism and generality, that different parameter values of common submodels are required, or that a given model is correct as far as it goes, but applies with less generality, realism, or predictive power than its proponents claim. […] Human sociobiology provides a good example of a plausibility argument. The basic premise of human sociobiology is that fitness-optimizing models drawn from evolutionary biology can be used to understand human behavior. […] We think that the clearest way to address the controversial questions raised by competing plausibility arguments is to try to formulate models with parameters such that for some values of the critical parameters the results approximate one of the polar positions in such debates, while for others the model approximates the other position.”

“A well-developed plausibility argument differs sharply from another common type of argument that we call a programmatic claim. Most generally, a programmatic claim advocates a plan of research for addressing some outstanding problem without, however, attempting to construct a full plausibility argument. […] An attack on an existing, often widely accepted, plausibility argument on the grounds that the plausibility argument is incomplete is a kind of programmatic claim. Critiques of human sociobiology are commonly of this type. […] The criticism of human sociobiology has far too frequently depended on mere programmatic claims (often invalid ones at that, as when sociobiologists are said to ignore the importance of culture and to depend on genetic variation to explain human differences). These claims are generally accompanied by dubious burden-of-proof arguments. […] We have argued that theory about complex-diverse phenomena is necessarily made up of simple models that omit many details of the phenomena under study. It is very easy to criticize theory of this kind on the grounds that it is incomplete (or defend it on the grounds that it one day will be much more complete). Such criticism and defense is not really very useful because all such models are incomplete in many ways and may be flawed because of it. What is required is a plausibility argument that shows that some factor that is omitted could be sufficiently important to require inclusion in the theory of the phenomenon under consideration, or a plausible case that it really can be neglected for most purposes. […] It seems to us that until very recently, ‘‘nature-nurture’’ debates have been badly confused because plausibility arguments have often been taken to have been successfully countered by programmatic claims. It has proved relatively easy to construct reasonable and increasingly sophisticated Darwinian plausibility arguments about human behavior from the prevailing general theory. It is also relatively easy to spot the programmatic flaws in such arguments […] The problem is that programmatic objections have not been taken to imply a promise to deliver a full plausibility claim. Rather, they have been taken as a kind of declaration of independence of the social sciences from biology. Having shown that the biological theory is in principle incomplete, the conclusion is drawn that it can safely be ignored.”

“Scientists should be encouraged to take a sophisticated attitude toward empirical testing of plausibility arguments […] Folk Popperism among scientists has had the very desirable result of reducing the amount of theory-free descriptive empiricism in many complex-diverse disciplines, but it has had the undesirable effect of encouraging a search for simple mutually exclusive hypotheses that can be accepted or rejected by single experiments. By our argument, very few important problems in evolutionary biology or the social sciences can be resolved in this way. Rather, individual empirical investigations should be viewed as weighing marginally for or against plausibility arguments. Often, empirical studies may themselves discover or suggest new plausibility arguments or reconcile old ones.”

“We suspect that most evolutionary biologists and philosophers of biology on both sides of the dispute would pretty much agree with the defense of the simple models strategy presented here. To reject the strategy of building evolutionary theory from collections of simple models is to embrace a kind of scientific nihilism in which there is no hope of achieving an understanding of how evolution works. On the other hand, there is reason to treat any given model skeptically. […] It may be possible to defend the proposition that the complexity and diversity of evolutionary phenomena make any scientific understanding of evolutionary processes impossible. Or, even if we can obtain a satisfactory understanding of particular cases of evolution, any attempt at a general, unified theory may be impossible. Some critics of adaptationism seem to invoke these arguments against adaptationism without fully embracing them. The problem is that alternatives to adaptationism must face the same problem of diversity and complexity that Darwinians use the simple model strategy to finesse. The critics, when they come to construct plausibility arguments, will also have to use relatively simple models that are vulnerable to the same attack. If there is a vulgar sociobiology, there is also a vulgar criticism of sociobiology.”

June 6, 2014 Posted by | Anthropology, Biology, Books, culture, Ecology, Economics, Evolutionary biology, Mathematics, Science | Leave a comment

100 Cases in Acute Medicine

100 Cases in Acute Medicine presents 100 acute conditions commonly seen by medical students and junior doctors in the emergency department, or on the ward, or in the community setting. A succinct summary of the patient’s history, examination, and initial investigations, including photographs where relevant, is followed by questions on the diagnosis and management of each case. The answer includes a detailed discussion of each topic, with further illustration where appropriate, providing an essential revision aid as well as a practical guide for students and junior doctors.

Making clinical decisions and choosing the best course of action is one of the most challenging and difficult parts of training to become a doctor. These cases will teach students and junior doctors to recognize important clinical symptoms and signs, and to develop their diagnostic and management skills.”


The book is quite simple. There are 100 medical cases. Each case has a brief description of symptoms and what we know about the patient, plus a couple of questions. On the next page of the book there are then answers to the questions posed with (semi-?)detailed explanations. In many cases one of, or perhaps the only question, is: ‘what’s wrong with this person?’, but sometimes the management aspect is considered to be the key variable (‘obese hypertensive and hyperlipidemic type 2 diabetic with previous MI has just been admitted with cardiac symptoms. Here are the results of his blood-work and an ECG. How do you proceed?’ – not a quote, but close enough…), and in such cases there are e.g. questions about which particular aspects of this presentation you should be most concerned about, or perhaps an open question related to aspects such as how to optimize the follow-up process. I’ll never diagnose anyone with anything or set up a medical management plan, as that is for doctors to do, but I thought it looked like an interesting book, so I figured I’d give it a shot. Reading a book like this is a little bit like watching House, except that the medicine in here is actually trustworthy and you avoid all the drama (I know that I have remarked upon how reading medical textbooks will change your viewing experience of medical dramas before, but in the context of this book that particular aspect seems perhaps even more relevant than usual – all the patients in this book have presented to the ER because they are sick and we are told about their symptoms and perhaps some of the test results which have come back from the lab; this setting, I believe, is pretty much the default setting for medical dramas…).

The blog currently has 118 posts related to the topic of medicine so I have read some stuff and watched some lectures on these topics; I figured it’d be interesting to see if I could figure out some of the cases, and I felt reasonably sure I’d learn from both the ones I could figure out and the ones I couldn’t (as I considered them likely to add details I didn’t know, e.g. about differential diagnoses, in the anwers). I also thought more generally that it’d be nice to have a book with some ‘common/standard’ health complaint cases presented. Diagnostics is often more difficult than you’d think from reading about specific diseases, because people in many cases don’t present with all the textbook symptoms, and because certain symptoms present in a lot of very different situations. A confused old person with altered mental status might for example ‘just’ be dehydrated with nothing else going on (severe dehydration can be quite dangerous, thus the ‘just’) – but it could also be a brain tumour, or a subarachnoid hemorrhage, or a urinary tract infection (“Elderly people, particularly females, are more prone to urinary tract infections and often present with confusion”), or… Severe abdominal pain and vomiting in a young person isn’t always appendicitis; this book had a young woman with familial Mediterranean fever present that way.

There were more than a few cases where I ‘got it right’, including some quite obscure ones like a case of Stevens-Johnson syndrome (-SJS – this one is really rare, something like 1 in 200.000 rare – I only guessed it because I read the wiki on that one a while back and it stuck) and a patient with an insulinoma (this one also has a very low incidence, “estimated at 1 to 4 new cases per million persons per year” – my knowledge of diabetes helped here, as did my recall of the coverage of this condition in McPhee et al (at least I think that was where I read about it). There were quite a few more common ones I got right, for example cases of pre-eclampsia (Hall covered that one in quite a bit of detail, so I had no problems figuring out what was going on there), mumps, diabetic ketoacidosis, hyperosmolar hyperglycaemic state (I found it interesting that they included both a DKA case and a HHS case, and/but I had of course no problem recognizing either of these), malaria, alcohol withdrawal syndrome (obvious from the patient history, but not if you don’t know about the risk of seizures and -progression to DT associated with alcohol withdrawal – which the patient obviously didn’t…), Lyme disease, trypanosomiasis (well, I couldn’t remember that that’s what it was called, but I did guess ‘sleeping sickness’, which is good enough, I think – though of course I’d have no idea how to treat someone with that disease…), anorexia nervosa, and pulmonary oedema. There were a lot of them I didn’t get right or didn’t know the answer to, which is in a way to be expected (the insulinoma and SJS cases were not the only quite rare ones – who’s ever heard about Goodpasture syndrome anyway?). In more than a few cases you need, in order to get the diagnosis right, to be able to read and understand the results of an electrocardiogram, a CT scan, an MRI or a chest X-ray; I’ve seen these before in textbooks, but I’ve never received formal training in interpreting them – however at least in the case of the pulmonary oedema the X-ray results were obvious. ‘He’s having a heart attack’ was a sort of a diagnosis in a couple of cases, but not what they were going for – if they thought his heart was fine they probably wouldn’t have asked the lab for troponin levels or ordered an ECG..

I have added some observations from the book below, most of them from the ‘answer sections’. As I didn’t assume anyone reading along here would be likely to read the book later on I have not tried very hard to avoid ‘spoilers’:

“[Neurocysticercosis] is the most common parasitic infection of the central nervous system and the leading cause of adult-onset seizures in the developing world.”

“Mumps is the most common cause of unilateral acquired sensorineural hearing loss in children and young adults worldwide […] Suspect mumps in a patient who presents with parotitis and fever.” (I did. The included vaccination history helped.).

“A 19-year-old woman has presented to the emergency department complaining of fevers and malaise after returning from a holiday in South Africa two weeks earlier. Over the preceding 3–4 days she noticed a rash and sore throat and is now feeling generally tired and unwell. She has no significant medical history and does not take any regular medications or recreational drugs. She does not smoke, nor drink alcohol. She admits to several episodes of unprotected sexual intercourse with a man she met in South Africa.”

My first thought when reading the case history above: Immediate psychiatric consult and an IQ test. If you’re having unprotected sex with a South African whom you don’t know well on multiple occasions you’re either insane or a moron. More seriously, this one was one of several really depressing presentations. There were ways to make the patient history even worse (‘when she came back to receive the results of her (positive) HIV test she mentioned during the followup that she’d been gaining a bit of weight lately and that she had been feeling nauseous occasionally, especially during the morning hours…’), but this was quite bad enough. Do note however that there could be other explanations for her illness than just HIV, and that these should be considered as well: “This woman is likely to have a viral illness, considering her history of fevers, rash and sore throat. Infectious mononucleosis (glandular fever) secondary to Epstein–Barr virus is a common illness in young adults, presenting with fever, rash and lymphadenopathy following on from a sore throat.”

“Urinary tract infections can often present with non-specific symptoms, such as confusion and general malaise, particularly in elderly patients. […] Early treatment according to the Surviving Sepsis protocol is key to ensuring patients have the best chance of surviving a serious infection.”

I include this one at least in part because people reading my comments above about confusion perhaps being the result of a urinary tract infection may have thought that ‘okay, so not all of these cases are all that severe’, as a urinary tract infection is probably perceived of as belonging on ‘the opposite side of the scale’ as brain cancer. In the specific case that would be an incorrect way to think about the situation: “The patient is haemodynamically unstable […] The patient’s daughter should be informed that her mother is very unwell and may not survive.” Yes, this was another one of the depressing ones. Here’s a related quote from another case: “Most women will experience a urinary tract infection (UTI) at some time in their life, so education towards UTI prevention is important (e.g. wipe from front to back after a bowel movement or after urinating, and try to empty the bladder before and after sexual intercourse).”

“Tuberculosis should be suspected in anyone presenting with shortness of breath, fever, haemoptysis and weight loss. […] An important differential diagnosis to consider is lung malignancy.”

“Alcohol misuse increases the risk of intracerebral bleeds, because head injury is more likely to be sustained or as a result of deranged liver function. Sustained alcohol misuse can lead to deranged liver function and therefore reduced production of vitamin K, which is essential for normal blood clotting properties. […] Seizures are a common way for patients with alcohol withdrawal to present.”

“In patients who are vomiting and develop signs of a chest infection, an aspiration pneumonia should be considered.”

“Angiodysplasia is a condition where the small vessels in the bowel are dilated, very fragile and prone to bleeding. […] Angiodysplasia of the colon is the second most common cause of GI bleeding in patients over the age of 60 years (diverticular disease being the most common in that age group). The most common presentation is intermittent bleeding without pain.”

“There are common steps in the management of acute intoxication and poisoning. As with most medical emergencies, the airway, breathing and circulation (ABC) should be assessed and managed appropriately in the first instance. Neurological examinations should be carried out to look for lateralizing and/or cerebellar signs. It is also important to examine for abnormal ocular movements and papillary changes as it helps to give clues to the common drugs/toxins involved. […] Often a ‘drug screen’ is requested but this is rarely necessary. A typical drug screen is expensive and difficult to interpret. The results may take 1–2 weeks to become available and it is not possible to screen for all possible toxins. Therefore it does not alter immediate patient management in most instances. Neuroimaging, such as CT of brain, is only necessary when patients are suspected to have a structural brain lesion or significant head injury. A provoked seizure from poisoning or substance abuse does not necessitate neuroimaging in most circumstances. […] In most cases the treatment of poisonings requires supportive therapy only as specific antidotes are often not available.” (ABC arguably isn’t enough – in a different answer they add on D and E as well:) “The approach to any critically ill person should start with ABCDE (airway, breathing, circulation, disability, exposure). Each step should consist of an assessment and appropriate management before moving on to subsequent stages. This approach is a logical way of thinking through and dealing with an acutely ill person.”

“[Anorexia nervosa] is a psychiatric diagnosis characterized by a refusal to maintain normal weight for age and height, a fear of gaining weight, body image distortion and amenorrhoea. There are other subtypes, which include ‘restricting’ calorie intake, or ‘binge eating/purging’ behaviours which can include laxative, diuretic or enema use. She has evidence of a low bodyweight (formal diagnosis relies on an ideal body weight <85 per cent, body mass index <17.5 kg/m2). Her body image perception is altered. […] Most people with anorexia nervosa are female, with the onset highest during late adolescence.”

“IgA [Immunoglobulin A] nephropathy is the most common glomerular disease worldwide. It occurs most commonly in those of Asian or Caucasian origin and is more common in males (2:1). Most cases occur between the ages of 20 and 30. Most cases are sporadic and the cause is not identified, but it tends to occur following an upper respiratory tract infection or gastrointestinal infection. […] Cases can present in several ways. About half of all cases present as in this case with frank haematuria and flank pain after an upper respiratory tract infection. A third of patients can present with asymptomatic microscopic haematuria. Ten per cent of patients can present with a more severe process characterized by either the nephrotic syndrome or an acute rapidly progressive glomerulonephritis (oedema, hypertension, haematuria and renal failure).”

“Atrial fibrillation becomes more common with increasing age such that more than 10 per cent of those aged over 80 years have AF. The most common causes of AF are hypertension, heart failure, ischaemic heart disease and valvular disease. Hyperthyroidism is another cause and may not have obvious clinical signs in the elderly. […] Stroke risk can be estimated from a score (CHA2DS2VASc: Congestive heart failure, Hypertension, Age ≥75 (doubled), Diabetes, Stroke (doubled), Vascular disease, Age 65–74, and Sex category (female) […] A score of 2 predicts a 2.2 per cent per year adjusted stroke risk […] This is generally accepted to be the cut-off to starting treatment with an oral anticoagulant provided there are no contraindications. […] The main concern with anticoagulants is the risk of bleeding and an assessment of this risk should be made prior to starting treatment. A bleeding risk score such as HAS-BLED can be used to assess risk […] Warfarin is still the anticoagulant of choice.”

“The incidence of stroke after thrombolysis is around 1–1.5 per cent and most strokes occur within five days of the MI, with most cases of haemorrhage within 24 hours of MI and thrombolysis.”

This is a risk it makes sense to be aware of – lots of people die from MIs and understanding the details of the risks involved when treating these may in some cases be helpful; if a person dies from a hemorrhagic stroke shortly after receiving treatment for an MI, this should not be considered a major indication that the doctors screwed up. Medical science has advanced a lot over the years, but ‘the anticoagulant of choice’ they talk about above is rat poison so do be careful not to overestimate how much doctors can really do for you if you get sick.

“In the setting of a positive family history of early death due to chest disease and a history of deranged liver function tests, one should […] consider α1-antitrypsin deficiency. α1-Antitrypsin deficiency (A1AD) is a disease which has various phenotypes […] It is one of the most commonly inherited genetic disorders. […] The severity of lung disease differs even in siblings with the same allele. This is partially explained by environmental factors such as smoking and dust exposure; therefore it is paramount to educate patients with α1-antitrypsin deficiency not to smoke.” (yep, you guessed it – the patient was a smoker. Despite having been diagnosed with COPD 3 years earlier. Again, depressing.)

“CURB 65 is one of the most commonly used tools for assessment of community-acquired pneumonia severity. It is a useful adjunct but should not replace thorough clinical assessment. CURB 65 stands for: C = confusion; U = Urea ≥7 mmol/L; R = Respiratory rate >30/min; B = Blood pressure systolic <90 or diastolic <60 mmHg; 65 = age ≥65 years. Mortality approaches 83 per cent if all four CURB components are present. […] Most if not all atypical pneumonias present with classical pneumonic symptoms (fever, productive cough and shortness of breath), so it is hard to differentiate clinically. Atypical pneumonia is a term used to describe pneumonia caused by (i) Mycoplasma pneumoniae, (ii) Chlamydophila pneumoniae, (iii) Chlamydophila psittaci, (iv) Coxiella burnetii, (v) Legionella spp, or (vi) Francisella tularensi [I talked about this last one before, in a completely different context…]. The term ‘atypical pneumonia’ remains useful to describe these pathogens as their treatment and sometimes duration of antibiotic therapy is different from typical pathogens.”

“Subdural haematomas are bleeds that occur between the dura mater and the arachnoid mater, enveloping the brain. They usually develop following traumatic injury […] Older people are particularly prone to such injuries as the brain naturally atrophies and shrinks with age. Blood collects in the space and draws in water due to osmotic pressures. The area of bleeding increases in size, causing compression of the cerebral tissue. […] Cushing’s triad of systolic hypertension with a wide pulse pressure, bradycardia and irregular or rapid respiratory rate is a major sign of raised intracranial pressure. These features occur due to insufficient blood flow to the brain and compression of arterioles. Subacute and chronic subdural haematomas classically present days to weeks after the insult. Any patient who presents with neurological signs several days after a head injury should be investigated for a subdural bleed.”

“Fever, jaundice and right upper quadrant abdominal pain make up the Charcot’s triad which are the main signs and symptoms of acute cholangitis. If a patient presents with Charcot’s triad and altered mental status and shock, it is called Reynold’s pentad. […] The most common cause of acute cholangitis is gallstone disease. […] Acute cholangitis carries a high mortality.”

I liked the book and gave it three stars on goodreads.

June 6, 2014 Posted by | Books, Cardiology, Diabetes, Infectious disease, Medicine, Neurology | Leave a comment