Carsten Timmermann

'Just give me the best quality of life questionnaire': the Karnofsky scale and the history of quality of life measurements in cancer trials

Objectives: To use the history of the Karnofsky Performance Scale as a case study illustrating the emergence of interest in the measurement and standardisation of quality of life; to understand the origins of current-day practices. Methods: Articles referring to the Karnofsky scale and quality of life measurements published from the 1940s to the 1990s were identified by searching databases and screening journals, and analysed using close-reading techniques. Secondary literature was consulted to understand the context in which articles were written. Results: The Karnofsky scale was devised for a different purpose than measuring quality of life: as a standardisation device that helped quantify effects of chemotherapeutic agents less easily measurable than survival time. Interest in measuring quality of life only emerged around 1970. Discussion: When quality of life measurements were increasingly widely discussed in the medical press from the late 1970s onwards, a consensus emerged that the Karnofsky scale was not a very good tool. More sophisticated approaches were developed, but Karnofsky continued to be used. I argue that the scale provided a quick and simple, approximate assessment of the ‘soft’ effects of treatment by physicians, overlapping but not identical with quality of life.

Text Mining the History of Medicine.

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform.

Clinical Trials and the Reorganization of Medical Research in post-Second World War Britain

The rise of biomedicine is usually associated with the transformation of biological and medical research in the United States following the vast expansion of funding, both private and public, in the years after the Second World War.1 Along with the other authors in this issue, we are interested in describing this phenomenon in national contexts other than the United States. Our discussion of biomedicine in Britain draws upon many of the same themes as our fellow authors and the existing literature on the US—the new role of the state as scientific entrepreneur; the relationship between experimental medicine and clinical services; and the growing institutionalization of associations between laboratory and clinic—to emphasize the clinical trial as a privileged form of therapeutic evaluation in the post-war years. In particular we are keen to stress that the randomized clinical, or controlled, trial (RCT) in Britain developed within a period of increasing centralization of state policy and planning for health services and medical research. The epistemological success of the RCT in demonstrating the value of the anti-tuberculosis drug streptomycin elevated the technique to international prominence in the late 1940s. The 1948 trials of streptomycin conducted by the British Medical Research Council (MRC), along with similar trials in the United States, are usually recognized as the worlds first randomized controlled trials. Indeed, the streptomycin trials, and the trials of PAS and isoniazid that followed in the early 1950s, did combine the statistical technique of randomization, with new organizational techniques, such as the division of specialist labour, and central review and data collection, across multiple sites of study. As Peter Keating and Alberto Cambrosio, Ilana Lowy, and Harry Marks have shown for the US, the success of the co-operative (that is, multi-centre) clinical trial was intimately related to the new role of the federal government, through the National Institutes of Health, in funding such organized biomedical research.2 Similarly, using treatment trials for tuberculosis and lung cancer as our case studies, we show for Britain that the promotion and organization of co-operative trials was fundamentally part of the MRCs new role within the state. We argue that the Council pursued the trials as a means of unifying a research landscape that was characterized by localism and suspicions about MRC plans to remodel clinical research to resemble the basic sciences. We argue further that a controlled trial must be understood both as a tool to produce knowledge persuasive enough to direct best clinical practice, and as a powerful means to discipline research workers in disparate settings.3 Neither process was particularly straightforward. It took years of clinical trials of anti-tuberculosis chemotherapies before sanatorium treatment and bed-rest were entirely given up by British physicians. The MRCs 1955 trials carried out in the Indian city of Madras (Chennai) are generally regarded as conclusively showing domiciliary care to be redundant in the presence of chemotherapeutic intervention; however, we argue that trials influenced but did not change practice overnight. Similarly, the lung cancer trials initiated by the MRC following a conference in 1957 as part of a broader programme of therapy trials for various types of cancer, proved difficult to run. Furthermore, they did not resolve the controversy as intended, not least because procedures and treatment pathways were well established before the trials. Serious historical attention to the organizational details, reception of such RCTs, and resulting changes in practice, is needed if we are not to be blinded by hindsight. Before we turn to the trials, however, we need to discuss the role of the MRC in the history of biomedicine in Britain and the place of the Council within the post-war socialized National Health Service (NHS).

Appropriating Risk Factors: The Reception of an American Approach to Chronic Disease in the two German States, c. 1950–1990.

Summary Risk factors have become a dominant approach to the aetiology of chronic disease worldwide. The concept emerged in the new field of chronic disease epidemiology in the United States in the 1950s, around near-iconic projects such as the Framingham Heart Study. In this article I examine how chronic disease epidemiology and the risk factor concept were adopted and adapted in the two German states. I draw on case studies that illuminate the characteristics of the different contexts and different take on traditions in social hygiene, social medicine and epidemiology. I also look at critics of the risk factor approach in East and West Germany, who viewed risk factors as intellectually dishonest and a new surveillance tool.

As depressing as it was predictable? Lung cancer, clinical trials, and the Medical Research Council in postwar Britain.

In recent years lung cancer specialists have complained that due to stigma resulting from the association of the disease with smoking, theirs is a neglected field. This paper demonstrates that in the 1950s and 1960s, when the British Medical Research Council (MRC) started to organize clinical trials for various forms of cancer, this was not the case. Rather, the organizers of these trials saw lung cancer as a particularly promising object of research, for much was known about the disease. The cancer trials were part of a strategy to use the Randomized Controlled Trial (RCT) technology to cement the role of the MRC as the dominant body overseeing medical research in Britain. The organization of the trials, however, turned out to be very difficult, due to ethical problems and the dominance of one form of therapy, surgery. The trial results were deeply disappointing. I argue that these frustrating results contributed to the notion of hopelessness that has come to surround lung cancer, and to the shift of focus from cure to prevention that was triggered by epidemiologic studies identifying tobacco smoke as the main cause of the disease. The paper deals with an important episode in the history of clinical cancer research in postwar Britain, illustrating the ethical and practical problems faced by the organizers.

Text-Mining and the History of Medicine: Big Data, Big Questions?

Most of us have heard about ‘Big Data’, often as part of discussions about the information collected about us as consumers and citizens, and the increasingly sophisticated tools that analyse such information. But can we as historians of medicine benefit from thinking about our historical sources as ‘Big Data’, and ‘mine’ this data by adapting the tools used by commerce, computing science and intelligence? What possibilities for historical scholarship would such tools open up; what challenges do they present? These questions motivated our involvement in a collaborative project using text mining tools with medical history sources. In early 2014 we joined University of Manchester colleagues from the National Centre for Text Mining (NaCTeM) to work on a project funded by the UK Arts and Humanities Research Council, under their Digital Transformations theme.1 As their name suggests, NaCTeM2 develops text mining tools, mostly for academic use.3 Our team set out to create a semantic search engine, one that would go beyond finding a specific keyword or string of text in a document. Semantic searches consider the context of use in order to locate terms and their variants representing particular concepts. We wanted to explore how such a search could provide new ways of working with series of medical texts covering a long period of major change in medical knowledge, practice and language. We chose two sources to form our corpus, as large-scale collections of structured text are known in digital humanities: the digitised run of the British Medical Journal from 1840 onwards, and the more recently digitised London-area Medical Officer of Health Reports that form the Wellcome Library’s London’s Pulse collection. Text mining (TM) uses digital tools to detect the structure of textual information, then find and recognise patterns and relationships in the structured data.4 For instance, one TM task is to find and compare the number of instances of particular terms over time in a defined corpus, as Google’s N-Gram viewer does using the ‘millions and millions of books’ that Google has digitised or has access to in digital form.5 Another common TM application is finding the relative frequency of the words in a text and then visualising these in a way that makes the different frequencies apparent, for example, by size and position in word clouds. Other TM tools track and compare the relative locations of terms and their variants in texts, or, in the case of topic modelling, identify groups of terms that tend to be representative of a given topic. As Tom Ewing’s use of these approaches demonstrates, they can provide insights that are not readily apparent in traditional reading, however intensive and analytical.6 Collaborating with NaCTeM allowed us to take advantage of even more complex approaches to TM, where systems can be ‘taught’ to recognise textual data as representing entities of different types, such as place names, medical conditions, etc., as well as specific types of relationships between these entities, for example, which symptoms are presented in the text as being caused by a condition. When combined with other tools and approaches used by digital humanities scholars, such as visualisation tools and GIS mapping, TM allows sources to be interrogated in ways that build upon and complement our traditional reading and analysis. Its proponents claim that automated technologies can do this not only faster and more thoroughly with very large data sets, but in ways that reveal new and interesting historical findings. Is this the case with big medical history data? Before we applied TM tools, we needed to make sure that our digitised corpus was sufficiently correct to be effectively mined, and this was no small task. As Tim Hitchcock has pointed out, many historians do not recognise the extent of OCR errors in the digitised texts that our existing search systems query.7 The recently created London’s Pulse is relatively error free, but in the BMJ files, which were digitised and OCRed several years ago, up to thirty per cent of the words have errors. Our NaCTeM colleagues devised a customised approach to correcting OCR errors in medical historical texts,8 which means our system provides a significant improvement on full-text BMJ searches. We then worked with NaCTeM colleagues to analyse sample text, identifying entities and relationships so we could teach our system how to carry out that identification on its own. We began by considering the kinds of entities and relationships historians might want to search for in this corpus. After experimenting with a very large, complex scheme with many subcategories, we decided on a streamlined scheme with seven entity categories (Anatomical; Biological Entity; Condition; Environmental; Sign or Symptom; Subject; and Therapeutic or Investigational) and two relationship categories (Affect and Cause). A team marked up a large sample of text, highlighting where these entities and relationships occurred. We then submitted this sample to a system equipped to ‘learn’ how to recognise annotations of different types, based on language patterns in the text. The ‘trained’ system was able to use these learned patterns to recognise entities and relationships in the un-annotated remainder of the corpus – more than 150 years’ worth of weekly issues of the BMJ, and more than 5000 reports by London-area Medical Officers of Health. Teaching the system to discriminate between the entities historians consider important in historical medical texts proved much more difficult than teaching it to identify simpler entities like named locations. First, terms such as disease names that have been used to describe similar phenomena have changed over time, but using TM techniques the system was able to learn, for instance, that ‘infantile paralysis’ and ‘poliomyelitis’ were different terms used in overlapping time periods for a reasonably similar phenomenon. However, some terms have multiple and changing meanings and uses, depending not only on temporal but also textual context, reflecting the very changes in medical thinking we want to examine. One example is the term ‘inflammation’: as an entity, is it a Condition? a Sign or Symptom? Or is it a characteristic of a body part and thus Anatomical? Any categorisation decision we made, and the rules we devised for making sense of the context, would have to be clear enough for the search system to ‘learn’ and apply. Yet that decision would still need to reflect the term’s changing and indeterminate use in a way that would satisfy historian users of the search system. These tricky and problematic decisions have been built into our system, and we are of course anxious that our users be aware that, thanks to such decisions and to the complexity of the overall task, they need to be critical as they approach the results our system gives. In fact, the system facilitates critical engagement by the ease and speed of making alternative and cross-checking interrogations. As we trial our ‘beta-version’ with our Advisory Group, we are excited by the possibilities that this system, and that TM and indeed digital humanities tools as a whole, open up. First, our system speeds up searches dramatically, and allows more focused searches than would be possible even with fairly sophisticated Boolean searching. By searching for Condition: ‘tuberculosis’, for example, the user gets results where the system has recognised the term as referring to tuberculosis as a condition, rather than finding every instance of the word ‘tuberculosis’ in the text (in phrases like ‘National Tuberculosis Association’, or ‘tuberculosis nurse’). But semantic searching is about much more than convenience. The user can find all instances of a particular entity category: one can, for example, locate all articles published in 1892 where a Biological Entity (including non-human animals and microorganisms) is mentioned, and find the frequency with which each Biological Entity is mentioned. Combining entity searches and relationship searches enables the user to find instances where one entity is said to cause another: by asking what Condition entities are said to cause the entity Sign or Symptom: ‘swelling’ in the entity Anatomical: ‘feet’, the user can find case reports and reviews that discuss which ailments were understood to cause the feet to swell. (By contrast, consider the overwhelming flood of results the searcher would get by searching for the terms ‘feet’ and ‘swelling’.) This capacity is particularly useful for those who want to investigate relatively common, everyday phenomena that would stymie the best intentions of researchers because they are difficult to find in text, too numerous to manage easily, or easily overlooked by the all-too human researchers. We thus expect this tool not only to speed up searching and make it more precise, but also to help us see things that would otherwise be too difficult to see or too easy to miss, or that we might not even have known we were looking for. It will never provide easy and obvious answers to big questions, and it requires that the user know something about how it works. Nevertheless, we hope that as a tool that can facilitate exploration and new ways of encountering existing resources, it will be valuable both as a resource in its own right, and as a means of introducing our colleagues to TM tools and some of the possibilities of digital humanities.

Standards, scales and chronic illness: A brief introduction

The articles in September 2013 issue by Stephanie Snow, Carsten Timmermann and Michael Worboys deal with the histories of different clinical scales that have been important in shaping current understandings of chronic disease. As Chronic Illness is not a journal dedicated to the history of medicine, the papers warrant a brief general introduction.Chronic Illness publishes articles that are aimed at enabling practitioners, policy makers and patients to understand and manage chronic diseases more effectively. Our papers aim to do so by drawing the attention of readers to important historical developments, which have contributed to the transformation of acute into chronic conditions (Timmermann, Worboys) and vice versa (Snow): the adoption of standardized scales for quality of life in cancer trials, for depression, and stroke. We tend to take for granted that standards are a useful thing. We are also used to the idea that we live in an era where communicable and acute diseases have given way to non-communicable and chronic disorders as the main causes of mortality and morbidity. Our papers discuss the role that standardization has played in creating the modern notion of chronic illness. Studying the history of issues that we take for granted can help us understand how things have come to be the way they are. If we use ametaphor and view a fact like a ship in a bottle, history helps us to unpack such facts, to understand how the ship got into the bottle. Both standardization and chronic disease have received some attention by historians of science, technology and medicine in recent years. Standardization is an important feature of our modern age. In medicine and elsewhere, acts of standardization have been associated with redistributions of labor, risk and responsibility. Standardization enabled non-experts to do work that used to be done by experts, and machines to do the work of humans. Importantly, acts of standardization made changes look natural and necessary which were, in fact, the consequences of political decisions and social processes. How does this apply to medicine and especially chronic illness? Historians have shown how the availability of reliably standardized insulin, for example, has not only turned diabetes from an acutely lifethreatening disease into a chronic condition, but also enabled patients to manage it themselves. Similar arguments have been made for dialysis and kidney disease. In our papers we are concerned with attempts to standardize complex acts of clinical judgement in the shape of relatively simple, often Chronic Illness 2014, Vol. 10(1) 3–4 ! The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1742395312468459 chi.sagepub.com

Running Out of Options: Surgery, Hope and Progress in the Management of Lung Cancer, 1950s to 1990s

When Frank Craig died in March 1994, almost exactly four years after a third of his right lung had been removed by a surgeon at St Anthony’s Hospital in Cheam, south of London, his death was sadly representative of the fate of many lung cancer patients in Britain.’ About half of the patients operated on for lung cancer die within five years after the operation. This figure is lower than it used to be, but not because of new, innovative treatments. The percentage of longterm survivors after surgery is higher now than in previous decades mostly due to the introduction, since the 1970s, of more rigorous methods of distinguishing patients for whom surgery may bring longterm survival from those to whom an operation would be of no use. In recent years, less than a quarter of patients diagnosed with lung cancer have been undergoing lung resections, the removal of parts or all of one lung. Around the time of Frank Craig’s illness, about 10 per cent survived their lung cancer diagnosis for five years or longer — usually following surgery.2

John Pickstone: a personal tribute

I met John in 1995. I had enrolled for the University of Manchester’s unique M.A. in History and Social Anthropology of Science, Technology and Medicine, which was jointly run by the Centre for the History of Science, Technology and Medicine and the Department of Social Anthropology. This degree was one of many interdisciplinary activities that John had initiated. Soon after my arrival, during one of several drink receptions that form an integral part of induction weeks at British universities, John treated me like an old friend. He seemed genuinely interested in what I had to say and offered me a funded Ph.D. project: he had been approached by an organisation of Swiss orthopaedic surgeons who wanted their history written, the AO (Arbeitsgemeinschaft fur Osteosynthesefragen). It was necessary to read German to do this. I decided that I was not ready to work on the history of the AO. The project went to the University of Freiburg, where Thomas Schlich turned it into into a fine book, published in the book series that John edited 1. The project John had in mind for me was a typical example of how his work on local Manchester issues extended outwards and eventually went global. He had developed an interest in orthopaedic surgery when researching his book on Medicine and industrial society while based at the institution down the road, University of Manchester Institute of Science and Technology

The Management of Stigma: Lung Cancer and Charity, circa 1990 to 2000

The association with smoking made lung cancer different from other cancers which may have confronted doctors with similar clinical challenges. Lung cancer increasingly came to be perceived as a disease that could not be treated and had to be prevented by persuading people not to take up smoking. This meant that lung cancer sufferers who had never smoked (10 to 15 percent) found themselves associated with a habit which used to be normal but was increasingly seen as a sign of personal weakness and a danger to others.1 This made lung cancer comparable to tuberculosis and venereal disease, the traditional targets of public health campaigns. The association with smoking led to a common assumption that patients brought the illness upon themselves. But was it ethically acceptable to hold it against smokers that they had conducted a disease that was associated with the habit? How was smoking different from other behaviours which caused illness? In this chapter I will discuss the question of stigma in relation to lung cancer. I will look at the history of the Roy Castle Foundation, a charity dedicated to lung cancer, as a case study of ways of managing such stigma and its consequences. I will also discuss, in this context, some recent developments in lung cancer screening.