essay on importance of medicine

What is medicine? Why it’s so important to answer this question

Executive Dean, Faculty of Humanities and Director, African Centre for Epistemology and Philosophy of Science, University of Johannesburg

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What is medicine? We recognise it in all societies past and present. But the nature of medicine differs so greatly from place to place and time to time that it’s difficult to offer a single answer. So what is it that we see in common between a traditional healer’s throwing of bones and the cardiologist’s incisions?

One of the answers that often seems to be implicit in what we say and think about medicine is a curative thesis : medicine’s goal is to cure the sick. Curing the sick is the core medical competence, whose exercise is medicine’s core business.

But if the curative thesis is true, then most medicine throughout history – as well as much contemporary medicine – isn’t medicine at all. Much medicine was and is ineffective, or at best partially effective. The curative thesis leads to a dismissive attitude towards the past efforts upon which any current medicine is built, as well as failing to promote profitable collaboration between traditions.

A second idea is an inquiry thesis about medicine: although the goal of medicine is to cure, its core business is something quite different. It’s this thesis I explore in my latest article .

That “something” has to do with inquiring into the nature and causes of health and disease. The idea is that we don’t necessarily expect someone to be able to cure us. We will accept that they are a medical expert if they can show an understanding of our ailment, often by issuing an accurate prognosis. Perhaps they won’t have a complete understanding, but they should somehow be engaged with the larger project of inquiry into the nature and causes of health and disease.

The inquiry thesis offers a way to understand the history of medicine that makes it more than a tale of quackery and gullibility. It also provides a way to understand medical traditions that practised outside the West, or in the West in defiance of the mainstream. They may offer or at least engage with a project of obtaining; a kind of understanding that Western medicine cannot.

The inquiry model of medicine lays the ground for fruitful and respectful discussions between medical traditions that doesn’t descend into an untenable relativism about what works.

Towards understanding

The curative thesis faces a difficulty that I believe it cannot overcome.

We do not define an activity by its goal alone, unless it has at least some success in that respect. A blacksmith cannot be defined as one who makes horseshoes if he simply throws lumps of hot metal onto his anvil and hammers them randomly – occasionally producing something horseshoe-like, but more often producing a mess.

Yet, taking a historical perspective, something of this kind has been true of medicine for much of its history, before it developed a serious curative arsenal. Historian of medicine Roy Porter has remarked that

the prominence of medicine has lain only in small measure in its ability to make the sick well. This was always true, and remains so today.

What, then, could be the business of medicine – the thing in which we recognise expertise, even when we accept that there is no cure to be had?

This is where the inquiry model enters the picture. I propose that the business of medicine is understanding the nature and causes of health and disease, for the purpose of cure.

The core of the argument is simple: what could medical persons be good at doing, that relates to the goal of cure without achieving it? The most likely candidate is understanding. Understanding is something that we can gain without corresponding curative success.

Tackling objections

As with the curative thesis, there are several objections to the inquiry model. First, it is obvious that many doctors either don’t (fully) understand what they treat or, if they do, don’t (successfully) communicate this understanding to the patient. Who, then, understands? In what sense is the doctor’s competence understanding?

The answer is that understanding isn’t a binary. You can partially understand something. You can be one the road to understanding it better, by inquiring into it. Hence the inquiry model of medicine. The idea is not that medicine is a sack full of answers, but rather that it is an ongoing effort to find answers.

Another objection is that so-called understanding is often bogus, and that medicine is as unsuccessful in this regard as in cure. This fails to account for the historical record, which – at least for Western medicine –- is precisely a case of understanding without curative success.

And, just as false scientific theories have contributed to developing scientific understanding , so false medical theories have provided a foundation for what we now accept.

Medicine is an ancient and complex social phenomenon, variously seen as art, science and witchcraft. These visions share the goal of curing disease. But it is too crude to think medicine as only the business of curing, since in that case, few doctors would be in business.

The distinctive feature of medicine is that it tries to cure by obtaining some understanding of the nature and causes of health and disease: by inquiry, in short. This understanding of medicine permits a much healthier dialogue between proponents of different traditions, and enables a non-defensive perspective on areas where we remain sadly lacking in curative ability.

This is an edited, shortened version of an article that first appeared in the Canadian Medical Association Journal, ‘The inquiry model of medicine’ , accompanied by a podcast available on the article’s page and also here .

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“So, why medicine? Why do you want to be a doctor?”

“Why do you want to be a doctor?” It’s a question that many medical applicants are urged to have an answer for as they prepare to be interviewed for entry into medical school. In our 50th episode of Sharp Scratch, host Nikki Nabavi was joined by two past Sharp Scratch hosts, Laura Nunez-Mulder and Anna Harvey, as well as expert guest Declan Hyland, a consultant psychiatrist who is involved with medical schools admissions at the University of Liverpool, to discuss their own motivations for wanting to study medicine, and how this might change throughout medical school and life as a doctor. The team also heard from some very special guests about why they chose medicine.

The episode kicked off by discussing some of the “cliches” associated with answering the question of “why study medicine?” Nikki cited themes she had come across on Twitter , including the statements all medical applicants are advised to avoid such as “wanting to help people” and “being interested in science.” But why shouldn’t these be excellent reasons for wanting to become a doctor? Laura agreed that when she was applying to medical school, these were big reasons for her own interest in medicine as a career, but felt that “they weren’t good enough reasons,” and that to stand out in applications and interviews, she had to come up with “something that was still true, but was a bit different.” Many of our expert guests agreed that these two “cliches” were still true for them, with BMJ Editorial Registrar and surgical trainee Clara Munro reflecting that part of what keeps her in clinical medicine is “having a real curiosity and genuinely caring about patients, and enjoying engaging with them on a human level.”

Another theme Nikki raised from responses on social media was another “cliche,” of medical students “wanting to be a doctor since they came out of the womb.” Portfolio GP and media doctor Zoe Williams wanted to be a doctor since her third birthday, when she was bought a toy doctor’s kit by her grandmother, who was a midwife. “One of the reasons I wanted to be a doctor is because I found the human body fascinating.” But those dreams weren’t realised until many years later, when Zoe was one of the first students to have the opportunity to transfer from her Biomedical Sciences degree to Medicine. Declan discussed his similar pathway, highlighting that medicine isn’t just for those who learnt the brachial plexus before they could walk!

So with applications for medical courses up by nearly 5000 in the 2020-21 application cycle , what are admissions tutors looking for when asking applicants these questions at interview? The panel turned to Declan, who has had experience interviewing potential medical students, for his take. “Enthusiasm is one of the key things, and that usually comes across pretty quickly,” he said, continuing, “[applicants should] justify why medicine is more suited to your interests, rather than any other particular career within the health service.” 

The variety of work that is available to those with a medical degree is another factor that guided our panel members towards medicine, with Laura explaining that one of the reasons she chose medicine over other careers she considered was that “there’s not just one job within being a doctor that would interest me, there’s lots of jobs, and I would leave that choice open for longer.” Anna agreed, remembering comments she had received when applying for medical school from people concerned that making such a decision during her last years of school meant she was boxing herself into medicine, and not leaving enough options open. “But I don’t think I could have picked something at the age of 18 that could have given me more options.” Declan, speaking on his own career, said: “I’m fortunate that I have a lot of involvement with Liverpool medical school, which is entirely different and separate from my clinical work. It’s something like that that keeps you really inspired, refreshed and stops you burning out.” 

Anna also spoke about her desire to be part of something that is bigger than just herself: “I think a big part of it for me was wanting to have that community, and that agency; because you do have agency as a doctor that you might not in other professions.” Declan agreed: “It goes without saying that it’s still a very respected profession.” And it can’t be ignored that, for all the debate over hours and training, the career is one that can be lifelong, and provides reasonable stability in terms of salary.

For our final reflections, we turned to perhaps the most famous doctor in the UK – the Chief Medical Officer Chris Whitty. Reflecting on his career, he said: “like many others, I have changed my mind multiple times, and the only thing that ran through all of it was clinical practice, which I still do. I’ve done various jobs, all of which I have enjoyed, many of which I would only do once, but I enjoyed that once.” He continued, “the reason to do a job is because you wish to do it, and if you manage to do that, medicine is a fantastic career.”

As Zoe highlighted, being a doctor is not just about the skills and knowledge you acquire throughout your career, but also how you feel: “It’s not just what I do, it’s who I am.”

To readers and listeners: why did you want to study medicine? Have your reasons changed throughout your career? We’d love to hear from you on social media using the hashtag #SharpScratch

Anna Harvey is a final year medical student and soon-to-be junior doctor in North Cumbria. She is a past Editorial Scholar at the BMJ and sits on the Steering Committee of the MedEd Research Collaborative , where her interest in identity is indulged through qualitative clinical education research.

Listen to the episode on spotify or apple pods .

The Sharp Scratch Panel:

Nikki Nabavi, The BMJ, University of Manchester

Anna Harvey , Final year medical student, King’s College London, past editorial scholar, The BMJ.

Laura Nunez-Mulder, Final year medical student, Cambridge University, past editorial scholar, The BMJ. 

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Panel: @nikkixnabavi   @a_c_harvey @lnm_rugby  

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Home — Essay Samples — Life — Compassion — Importance Of My Passion For Medicine

Importance of My Passion for Medicine

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Medicine and the future of health: reflecting on the past to forge ahead

• Dale Fisher 1 , 2 ,
• Paul Wicks 3 &
• Zaheer-Ud-Din Babar 4 , 5  

BMC Medicine volume  14 , Article number:  169 ( 2016 ) Cite this article

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The development of new therapies has a rich history, evolves quickly with societal trends, and will have an exciting future. The last century has seen an exponential increase in complex interactions between medical practitioners, pharmaceutical companies, governments and patients. We believe technology and societal expectations will open up the opportunity for more individuals to participate as information becomes more freely available and inequality less acceptable. Corporations must recognize that usual market forces do not function ideally in a setting where health is regarded as a human right, and as modern consumers, patients will increasingly take control of their own data, wellbeing, and even the means of production for developing their own treatments. Ethics and legislation will increasingly impact the processes that facilitate drug development, distribution and administration. This article collection is a cross-journal collaboration, between the Journal of Pharmaceutical Policy and Practice ( JoPPP ) and BMC Medicine that seeks to cover recent advances in drug development, medicines use, policy and access with high clinical and public health relevance in the future.

The Medicine and the Future of Health article collection is a joint collection between BMC Medicine and Journal of Pharmaceutical Policy and Practice . Therefore, this Editorial by the guest editors has been published in both journals.

Any vision of the future requires an appreciation of the past. Historically, the availability of medical treatments has paralleled life’s other “luxuries” and so was only available to the few. However, most remedies and would-be cures were not effective prior to the 20 th century, with few notable exceptions. Among these, digitalis, an extract from the purple foxglove ( Digitalis purpurea ) was first used in the dark ages as a poison until its discovery in 1775 for the treatment of heart failure [ 1 ]. More recently, the antimalarial drug artemether was extracted from the herb Qinghao, which had been used in China for over 2000 years [ 2 ].

In the 1920s, the emergence of more consistently effective pharmaceutical agents began, led by analgesics, including aspirin and morphine, insulin, and anti-infective agents such as sulphonamides and penicillin. However, it was soon realised that these potent new chemicals also carried risks. In 1937, investigators discovered, via a spate of reports to the American Medical Association, that an improperly prepared mixture of Elixir Sulfanilamide had killed over 100 people, prompting public outrage. This disaster led to the 1938 Federal Food, Drug, and Cosmetic Act to ensure that new drugs would be tested on animals and reviewed by the Food and Drug Administration. Subsequent amendments to the 1938 act introduced prescriptions for certain drugs (1951) and legislated for clinical trials (1962) [ 3 ]. Today, the post marketing surveillance of new medicines is much more sophisticated, and includes physician reports, patient outreach, Risk Evaluation and Mitigation Strategy programs, and the monitoring of electronic medical records. Such systems allow for enhanced safety via warning systems and the orderly withdrawal of drugs, though even this system suffers global inconsistencies [ 4 ].

As the pharmaceutical industry developed more effective medicines, quality of life of those suffering from many diseases clearly improved, as corticosteroids controlled inflammatory diseases, antihistamines controlled allergies, xanthines aided asthma patients, and options were offered to mental illness sufferers. Indeed, the human life span lengthened as infectious disease, heart disease, lung disease, and increasingly, cancer could be ameliorated through a combination of public health initiatives and better medicines. Today, medicines are intrinsic in all our lives, with almost half of US citizens taking a prescription medicine in the last 30 days [ 5 ]. However, rather than treating true disease pathology, the most intensively treated conditions at the population level in the US are pain, high cholesterol, depression and diabetes, arguably compensating for lifestyle changes brought about by dietary changes and a more sedentary lifestyle and due, in part, to the business models that encourage “blockbuster” drugs.

Medicines need to be readily available and affordable, however, unfortunately, there is an unacceptable level of medicine shortages across countries of all income levels. Currently, over 2 billion people do not have access to medicines [ 6 ]. In this context, the United Nations has set up a high-level forum to find ways to promote access to affordable medicines [ 7 ]. Iyengar et al. describe the driving factors behind access barriers and propose potential mitigation strategies [ 8 ].

There is widespread recognition that the existing global systems for innovation and access to medicines need reform. “Market failures” prevent new drugs from being developed that would primarily benefit the global poor, while factors such as high prices of medicines, weak health systems, corruption, and a lack of transparency, hinder efforts to distribute the medicines already available [ 9 ]. Community pharmacy triage services are emerging as a potential solution in a number of countries [ 10 ].

Access to medicines was once considered an issue confined to low- and middle-income countries; however, it is increasingly clear that access issues are also prevalent in high-income countries. For example, in the European Union, almost 50 % of drug expenditure is on cancer drugs [ 11 , 12 ]. In the future, it is likely that access may be improved for cheaper generic options, but concerns remain over whether “biosimilars” will successfully fill the same role [ 13 ].

Antibiotics, as a class, and their usage warrant consideration in their own right, especially given that their efficacy relies partly upon the extent of their use in other patients. Dyer et al. showed that one-third of US antibiotic prescriptions are inappropriate, and he warns us that we may be taking our once potent antibiotics for granted [ 14 ]. Because the drug targets and the class itself originate from nature, Woon et al. make the case that we must consider the effective use of antibiotics as akin to managing a delicate ecosystem [ 15 ].

Ironically, given their overuse in the US, antibiotics are a case study in the difficulty of access to drugs in low- and middle-income countries. Articles to be included in JoPPP will consider the national and global strategies needed to improve access, raise antibiotic quality, diffuse accurate diagnostics to the point of care, and ensure local stewardship for the sustainable optimisation of antibiotic use [ 16 ]. The understanding of these factors and usefulness of interventions through incentives and legislation will continue to evolve. Further affecting the usage of antibiotics will be more rapid point-of-care diagnostics, vaccines, faecal microbiota transplantation, probiotics and other novel approaches to infectious diseases. It is unlikely, however, that alternative technologies will displace the need for the conventional class and its development.

Looking forward, IMS Health has predicted that global spending on pharmaceuticals will increase by 30 % from 2015 to 2020, to US$ 1.4 trillion, due in part to improved access, breakthrough innovations and cheaper drugs. A large portion of the growth is also occurring in India, China, Brazil and Indonesia, the so called “pharmemerging markets”, in contrast to the US, where they predict more than 90 % of drugs purchased will be generics [ 17 ].

“Unfairness” in health is becoming increasingly unacceptable as a social norm. People should not be treated differently because of individual demographic factors; with the right information in hand, patients will increasingly be treated according to their need as Norheim et al. explore [ 18 ]. Such “unfairness” within countries is easier to address compared to global health corruption, a covert exploitation of people and resources which will require concentrated efforts to expose. In a forum article, Mackey and a multidisciplinary panel discuss the ways in which corruption affects global health at all levels, and explore the potential solutions in a post-2015 development agenda [ 19 ].

In the future, it is very likely that the use of medicines will be greatly influenced by technology, consumer education and self-awareness regarding lifestyle and diseases [ 20 ]. Technology will be a key driver for change in the future, enhancing the medical skillset of healthcare professionals facilitating updates and change in parallel with consumers [ 21 ]. This could influence the way people use medicines and how healthcare professionals manage patients. Novel change could include tailor-made drugs on the basis of pharmacogenomic data or medicines manufactured locally and on demand through 3D printing [ 22 ]. Future personalised sensors could measure clinical parameters and blood biomarkers transmitting data in real time to a cloud or, for instance, sending alerts when a stroke is in its earliest stages [ 23 ].

We will continue to feature articles addressing how medicine might be expected to evolve to be more individual patient centred. A forthcoming Forum article from leading thinkers in the area will consider the opportunity for the “data shadow” of smartphones to aid in detecting depression, for patient-reported outcomes to improve self-management, for clinical trials to make their quantum leap, and for the value perceived by patients to flow back into the learning health system, perhaps supported by new forms of machine learning.

Beyond the colourful history that belongs to the emergence of pharmaceuticals in the last century there will be an ongoing evolution in response to changing needs driven largely by consumer demand and expectations. Implicit in the very commission of this body of work risks supporting a paternalistic notion that “experts” can set the agenda for the future of medicine. The truth is quite the opposite where, in this era of information, “citizen health hackers” [ 24 ] may lead patients to self-experiment at a faster pace than traditional players via digital platforms and a mantra to experiment beyond the traditional confines of medicine. Control of the music industry and the lay press has moved to the consumer. Such a move in the field of medicines might lead to accelerated discoveries and innovation that could theoretically outpace the entrenched players providing consumer benefits via lower prices and more rapid access as well as global equity. If we let patients help, they may well lead us into the future of medicine.

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Authors’ contributions

DF, PW and ZB all participated in the drafting and revision of the manuscript, as well as in editing and soliciting specific articles cited here. All authors read and approved the final manuscript.

Authors’ information

DF is Professor and Head of Infectious Diseases at the National University Hospital in Singapore. He has a strong interest in systems that improve global health, having undertaken consultancies for the World Health Organisation and worked in remote Australia, much of Asia and in outbreak response settings including West Africa. He is an Editorial Board Member for BMC Medicine .

PW is VP of Innovation at PatientsLikeMe, a patient-powered research network that connects over half a million people living with chronic illness to share their health data and contribute their experience to decision-making in healthcare. A neuropsychologist by training he has a strong interest in neurodegenerative movement disorders and improving clinical trials. He is an Editorial Board Member for BMC Medicine .

ZB is currently a Dean and Professor at Lahore Pharmacy College and an Honorary Senior Lecturer at the School of Pharmacy, University of Auckland. His areas of interest are medicines pricing, access to medicines and pharmacoeconomics. He had acted as a consultant for World Bank, Health Action International and for international Union Against Tuberculosis and Lung Disease on issues related to medicines prices. ZB is the Editor in Chief of the Journal of Pharmaceutical Policy and Practice ( JoPPP ).

Competing interests

DF has received consultancy payment by Baxter International for advice and assistance in developing their Outpatient Parenteral Antibiotic Therapy (OPAT) programme. He has also received support from Gangagen Biotechnologies Pvt Ltd. for advice and assistance in developing a bacteriophage for clinical application.

PW is an employee of PatientsLikeMe and holds stock options in the company. PW is an associate editor at the Journal of Medical Internet Research and is on the Editorial Boards of BMJ and BMC Medicine . The PatientsLikeMe Research Team has received research funding (including conference support and consulting fees) from Abbvie, Accorda, Actelion, Alexion, Amgen, AstraZeneca, Avanir, Biogen, Boehringer Ingelheim, Celgene, EMD, Genentech, Genzyme, Janssen, Johnson & Johnson, Merck, Neuraltus, Novartis, Otsuka, Sanofi, Takeda, and UCB. The PatientsLikeMe R&D team has received research grant funding from Kaiser Permanente, the Robert Wood Johnson Foundation, Sage Bionetworks, The AKU Society, and the University of Maryland. PW has received speaker fees from Bayer and honoraria from Roche, ARISLA, IMI, AMIA, and the BMJ.

ZB is currently working with the World Bank as a short-term consultant in Dhaka, Bangladesh, on a medicines pricing project. Previously, he has also worked with the International Union Against Tuberculosis and Lung Disease and with Health Action International. He is the Editor in Chief of the Journal of Pharmaceutical Policy and Practice and also on the editorial board of Pharmacoeconomcis Open (An Adis/Springer Journal).

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Infectious Disease Division, Department of Medicine, National University Hospital; National University Health Systems, 1E Kent Ridge Rd, Singapore, 119228, Singapore

Dale Fisher

Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

PatientsLikeMe, Cambridge, MA, USA

School of Pharmacy, University of Auckland, Auckland, New Zealand

Zaheer-Ud-Din Babar

Lahore Pharmacy College, Lahore, Pakistan

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Fisher, D., Wicks, P. & Babar, ZUD. Medicine and the future of health: reflecting on the past to forge ahead. BMC Med 14 , 169 (2016). https://doi.org/10.1186/s12916-016-0717-0

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Received : 30 September 2016

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Published : 25 October 2016

DOI : https://doi.org/10.1186/s12916-016-0717-0

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Philosophy of Medicine

Philosophy of medicine is a field that seeks to explore fundamental issues in theory, research, and practice within the health sciences, particularly metaphysical and epistemological topics. Its historic roots arguably date back to ancient times, to the Hippocratic corpus among other sources, and there have been extended scholarly discussions on key concepts in the philosophy of medicine since at least the 1800s. Debates have occurred in the past over whether there is a distinct field rightly termed “philosophy of medicine” (e.g., Caplan 1992) but as there are now dedicated journals and professional organizations, a relatively well-established canon of scholarly literature, and distinctive questions and problems, it is defensible to claim that philosophy of medicine has now established itself. Although ethics and values are part of many problems addressed within the philosophy of medicine, bioethics is generally considered to be a distinct field, and hence is not explored in this entry (but see the entry on theory and bioethics ). That being said, philosophy of medicine serves as a foundation for many debates within bioethics, given that it analyzes fundamental components of the practice of medicine that frequently arise in bioethics such as concepts of disease. The philosophy of medicine also has made important contributions to general philosophy of science, and particularly to understandings of explanation, causation, and experimentation as well as debates over applications of scientific knowledge. Finally, the philosophy of medicine has contributed to discussions on methods and goals within both research and practice in the medical and health sciences. This entry focuses primarily on philosophy of medicine in the Western tradition, although there are growing literatures on philosophy of non-Western and alternative medical practices. It emphasizes philosophical literature while utilizing relevant scholarly publications from other disciplinary perspectives.

1. Introduction: How Should We Define Health and Disease?

2. contested and controversial disease categories, 3. theories, causes, and explanations in medicine, 4. reductionism and holism in medicine, 5. randomized controlled trials and evidence-based medicine, 6. animal models, 7. observational studies and case reports, 8. diagnosis, 9. clinician judgement and the role of expertise, 10. how are collective expert judgments made in medicine, 11. values in medical research, 12. measuring medical outcomes, other internet resources, related entries.

One of the fundamental and most long-standing debates in the philosophy of medicine relates to the basic concepts of health and disease (see concepts of health and disease ). It may seem obvious what we mean by such statements: people seek treatment from medical professionals when they are feeling unwell, and clinicians treat patients in order to help them restore or maintain their health. But people seek advice and assistance from medical professionals for other reasons, such as pregnancy which cannot be construed as a disease state, and high blood pressure which is asymptomatic. Thus the dividing line between disease and health is notoriously vague, due in part to the wide range of variations present in the human population and to debates over whether many concepts of disease are socially constructed. One of the further complicating factors is that both the concepts of health and disease typically involve both descriptive and evaluatory aspects (Engelhardt 1975), both in common usage among lay persons and members of the medical profession.

Exploring these distinctions remains epistemologically and morally important as these definitions influence when and where people seek medical treatment, and whether society regards them as “ill”, including in some health systems whether they are permitted to receive treatment. As Tristram Engelhardt has argued,

the concept of disease acts not only to describe and explain, but also to enjoin to action. It indicates a state of affairs as undesirable and to be overcome. (1975: 127)

Hence how we define disease, health, and related concepts is not a matter of mere philosophical or theoretical interest, but critical for ethical reasons, particularly to make certain that medicine contributes to people’s well-being, and for social reasons, as one’s well-being is critically related to whether one can live a good life.

The terms “disease” and “illness” often are used interchangeably, particularly by the general public but also by medical professionals. “Disease” is generally held to refer to any condition that literally causes “dis-ease” or “lack of ease” in an area of the body or the body as a whole. Such a condition can be caused by internal dysfunctions such as autoimmune diseases, by external factors such as infectious or environmentally-induced diseases, or by a combination of these factors as is the case with many so-called “genetic” diseases (on the idea of genetic disease and associated problems, see for instance Hesslow 1984, Ankeny 2002, Juengst 2004). It has been argued that there is no philosophically or scientifically compelling distinction between diseases and other types of complaints that many would not consider to be diseases such as small stature, obesity, or migraine headaches (Reznek 1987). The notion of “disease” is common among most cultures, and may even be a universal concept (Fabrega 1979). It is a useful concept as it allows a clear focus on problems that afflict particular human beings and suggests that medicine can help to control or ameliorate such problems. In contrast, “illness” is usually used to describe the more non-objective features of a condition, such as subjective feelings of pain and discomfort. It often refers to behavioral changes which are judged as undesirable and unwanted within a particular culture, and hence lead members of that culture to seek help, often from professionals identified as health providers of some type within that culture (on some of the complexities relating to the triad of concepts “disease, illness, sickness”, see Hofmann 2002).

The term “sickness” emphasizes the more social aspects of ill health, and typically highlights the lack of value placed on a particular condition by society. Disease conditions are investigated not only to be understood scientifically, but in hopes of correcting, preventing, or caring for the states that are disvalued, or that make people sick. The classic work of the sociologist Talcott Parsons (1951) showed how the “sick role” relieves one of certain social responsibilities (for example, allows one to take time off work or to avoid family responsibilities) and also relieves blame for being ill (though not necessarily from having become ill in the first place). Although there are exceptions and counterexamples to this model (for example, some chronic diseases), it does fit our generally accepted societal notions of what it means to be sick (and healthy), and the moral duties and responsibilities that accompany the designation of someone as sick.

The dominant approach in much of the recent philosophical scholarship on the philosophy of medicine views disease concepts as involving empirical judgments about human physiology (Boorse 1975, 1977, 1997; Scadding 1990; Wachbroit 1994; Thagard 1999; Ereshefsky 2009). These so-called “naturalists” (sometimes called “objectivists”, for example see Kitcher 1997, or “descriptivists”) focus on what is biologically natural and normal functioning for all human beings (or more precisely human beings who are members of relevant classes such as those within a particular age group or of the same sex). They argue that medicine should aim to discover and describe the underlying biological criteria which allow us to define various diseases. Christopher Boorse’s revised account has been the most influential in the literature, claiming that health is the absence of disease, where a disease is an internal state which either impairs normal functional ability or else a limitation on functional ability caused by the environment (Boorse 1997). “Normal functioning” is defined in terms of a reference class which is a natural class of organisms of uniform functional design (i.e., within a specific age group and sex), so that when a process or a part (such as an organ) functions in a normal way, it makes a contribution that is statistically typical to the survival and reproduction of the individual whose body contains that process or part. His definition includes specific reference to the environment so as not to rule out environmentally-induced conditions which are so common as to be statistically normal such as dental caries.

Many have criticized these approaches (to name just a few, Goosens 1980; Reznek 1987; Wakefield 1992; Amundson 2000; Cooper 2002), as well as naturalistic accounts of disease more generally. As they have noted, naturalistic accounts do not reflect our typical usage of the terms “disease” and “health” because they neglect to take into account any values which shape judgments about whether or not someone is healthy. The usual counterexamples proposed to naturalism are masturbation, which was widely believed to be a serious disease entity in the 18 th and 19 th centuries (Engelhardt 1974), and homosexuality, which for most of the 20 th century was classified as a disease in the Diagnostic and Statistical Manual (DSM) of the American Psychiatric Association. These are counterexamples as their redefinitions as non-disease conditions were due not to new biological information about these states of being but changes in society’s moral values. Naturalists respond to such arguments by pointing out that homosexuality and masturbation were never diseases in the first place but erroneous classifications, and thus these examples do not affect the validity of the definition of disease favored by them when it is applied rigorously.

A more telling criticism of naturalism is that although its advocates claim to rely exclusively on biological science to generate their definitions of health and disease, these rely implicitly on an equation of statistical and theoretical normality (or the “natural state” of the organism), at least in Boorse’s formulation (Ereshefsky 2009). But biology does not give us these norms directly, nor is there anything absolutely standard in “species design” (as many philosophers of biology have argued) despite Boorse’s claims. No particular genes are the “natural” ones for a given population, even if we take a subgroup according to age or gender (Sober 1980). Nor does standard physiology provide these norms (Ereshefsky 2009), not in the least part because physiological accounts typically provide idealized and simplified descriptions of organs and their functions, but not of their natural states (Wachbroit 1994). Rachel Cooper (2002) compellingly argues that coming up with an acceptable conception of normal function (and in turn dysfunction) is the major problem with Boorsian-style accounts, arguing that his analysis should focus on disposition to malfunction instead. This argument utilizes counterexamples such as activities that interfere with normal functioning such as taking contraceptive pills that are not diseases, as well as examples of persons with chronic diseases controlled by drugs who function normally as a result. Elselijn Kingma (2007, 2010) has critiqued Boorse’s appeal to reference classes as objectively discoverable, arguing that these cannot be established without reference to normative judgments. A further issue often noted with regard to naturalistic accounts of disease (for example, that of Lennox 1995) is the underlying assumption that biological fitness (survival and reproduction) is the goal of human life, and along with this that medicine is only considered to be interested in biological fitness, rather than other human goals and values, some of which might indeed run contrary to or make no difference in terms of the goal of biological fitness, such as relief of pain.

An alternative approach in the philosophical literature to naturalist/descriptivist/objectivist definitions of disease and health can roughly be termed “normative” or “constructivist”. Most proponents agree that we must define the terms “disease” and “health” explicitly and that our definitions are a function of our values (Margolis 1976; Goosens 1980; Sedgewick 1982; Engelhardt 1986). Hence defining various disease conditions is not merely a matter of discovering patterns in nature, but requires a series of normative value judgments and invention of appropriate terms to describe such conditions. Conversely, health involves shared judgments about what we value and what we want to be able to do; disease is a divergence from these social norms. Normativists believe that their definitions are valid not only philosophically but also reflect actual usage of the terminology associated with disease and health both in common language and among medical professionals. They also claim that this approach more adequately explains how certain conditions can come to be viewed in different ways over the course of history as our values changed despite relatively few changes in our underlying biological theories about the condition, for example homosexuality. Further, they are able to accommodate examples of so-called folk illnesses or culture-bound syndromes such as ghost sickness among some Native American tribes, the evil eye in many Mediterranean cultures, or susto in Latin and South American cultures, as their theories explicitly allow for cross-cultural differences in understandings of disease and health.

However normativism also generates a series of typical criticisms: it cannot cope adequately with cases where there is general agreement that a state is undesirable (such as alcoholism or morbid obesity) but no similar general agreement that the state is actually a disease condition (Ershefsky 2009). Another classic objection is that normative accounts do not allow us to make retrospective judgments about the validity of disease categories such as “drapetomania” (a disease which was commonly diagnosed among American slaves in the 19 th century, with the main symptom being the tendency to run away) (Cartwright 1851). The normativist can point to changes in values to explain the abandonment of belief in this disease condition, but would not be able to claim that the doctors were in any sense “wrong” to consider drapetomania to be a disease. Hence there is more involved in our everyday usage of the terms “disease” and “health” than just value or normative conditions.

Hybrid theories of health and disease attempt to overcome the gaps in both the naturalistic and normative approaches, by hybridizing aspects of both theories (Reznek 1987; Wakefield 1992; Caplan 1992). For instance Jerome Wakefield (1992, 1996, 2007), writing about psychiatric conditions in particular, notes that a condition should be considered a disease if it both causes harm to the person or otherwise contributes diminished value, and the condition results from some internal mechanism failing to perform its natural function (hence for instance much of what is diagnosed as “depression” would fail to count as a disease condition). Whereas the normativist is committed to calling any undesirable state a disease condition, these hybrid criteria rule out calling conditions “diseases” which are non-biological,. Then various marginal cases might be considered to be healthy rather than potentially described as diseased, and hence might not be eligible for treatment within conventional medicine. Examples include those organs or structures that no longer have a function due to evolutionary processes cannot malfunction and so cannot be diseased. Many hybrid approaches also retain too many assumptions about their naturalistic components, and hence are criticized for relying on a notion of natural function which cannot be supported by biology.

The concept of health has been relatively undertheorized in comparison to those of disease and illness, perhaps in part because it raises even more complicated issues than these concepts describing its absence. One could be a straightforward naturalist about health, and define it as being a product of a functional biology; however this argument would run afoul of the same criticisms of naturalism recounted above (see Hare 1986). The source for the classic definition of health comes from the Constitution of the World Health Organization (WHO) which defines health

a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity. (WHO 1948: preamble)

Notice that according to this formulation, health is not just the absence of disease but a positive state of well-being and flourishing (notoriously ambiguous concepts in themselves). Although quality of life is often cited as critical to definitions and theories of health, many commentators are wary of the expansiveness of a definition similar to the WHO’s terminology, as it seems to encompass many things beyond the health of the individual which could contribute (or diminish) his or her “well-being”.

A more narrow definition of health takes its rightful domain as being the state which medicine aims to restore, and its opposite to be “unhealth” or falling short of being healthy, rather than disease as such (Kass 1975). Under such a definition, medicine should not engage in aesthetic surgery or elective terminations of pregnancy or similar procedures which do not (strictly speaking) seek to restore health. Caroline Whitbeck (1981) has defined health in terms of the psychological and physiological capacities of an individual that allow him or her to pursue a wide range of goals and projects. Hence her account is a type of hybrid approach, since she places biological capacities at the core of her definition of health but only in so far as they help individuals to flourish and live their lives as they wish to do. The concept of health here is much more than the absence of disease; for instance, one could have a high level of health while still suffering from a particular disease condition.

One much discussed philosophical approach to defining health is that of Georges Canguilhem (1991, based on work in the early 1940s), who argued against equating it with normality. He noted that the concept of a norm could not be defined objectively in a manner that could be determined using scientific methods. Physiology deals with the science of norms, but even scientifically-based medical approaches should not focus solely on norms, contrary to for instance the ideal vision of medicine according to Claude Bernard (1865). The history of how the distinction between the normal and pathological became so entrenched is explored in detail in Michel Foucault’s now classic work (1963). Both Foucault and Canguilhem sought to reveal how values have been built into the epistemological framework underlying modern medicine.

One of the key points in Canguilhem’s argument is that our usage of the term “normal” often conflates two distinct meanings: the usual or typical, and that which is as it ought to be. Consequently, he argues that there can be no purely scientific or objective definition of the normal that allows us to take the theories of physiology and apply them in medical practice, and accordingly we cannot define health as normality either. Instead, according to him, health is that which confers a survival value, particularly adaptability within a set of environmental conditions: “to be in good health is being able to fall sick and recover; it is a biological luxury” (1991: 199). Disease, then, is reduction in the levels of tolerance for the vagaries of the environment. As Mary Tiles (1993) has noted, this emphasis on health rather than normality is a particularly useful tool for enriching contemporary debates over preventative medicine and more generally the trend toward the development of a positive conception of health. Havi Carel (2007, 2008) has contributed to this strand of thought, developing a phenomenological notion of health which emphasizes that health should be understood as the lived experience of one's own body rather than as simply statistically normal bodily functioning in abstract biological terms. Hence she develops an expressly revisionist project, emphasizing that a phenomenological perspective accommodates cases where someone is ill (in biological terms) but healthy, such as in chronic illness.

A number of authors have made even more extreme claims, arguing that seeking concepts of disease is bound to be a failed effort. For instance, Peter Schwartz (2007) claims that there is not an underlying general concept of disease within the biomedical sciences that is coherent enough to be analyzed, and that different concepts of disease might be useful within different contexts. Some philosophers have argued that to seek correct definitions for “disease” and “health” is distracting and irrelevant when it comes to clinical decisions: as Germund Hesslow puts it, “the health/disease distinction is irrelevant for most decisions and represents a conceptual straightjacket [sic]” (1993: 1). The key is whether or not a particular state is desirable to its bearer, and not whether the person actually has a disease or defect. For instance, the term “malady” has been proposed as a more appropriate alternative to “disease” (Clouser, Culver, and Gert 1981), and which should be extended to include all illnesses, injuries, handicaps, dysfunctions, and even asymptomatic conditions. A malady is present when there is something wrong with a person; regardless of the cause (mental or physical), to be a malady, the condition must be part of its bearer and not distinct or external to him or her. The clear advantage of this approach is that it unifies a range of phenomena and descriptions that seem intuitively to be related. The disadvantages include that it relies in part on an objectivist approach to disease, and hence suffers from some of the difficulties detailed above that plague some versions of naturalism (for a provocative reaction to this debate, see Worrall and Worrall 2001).

An alternative approach to defining disease and health has been described by Marc Ereshefsky (2009) in terms of making distinct state descriptions (descriptions of physiological or psychological states while avoiding any claims about naturalness, functionality, or normality), and normative claims (explicit judgments about whether we value or disvalue a particular physiological or psychological state). This approach has the advantages of allowing more clarity about controversial “disease” conditions as it avoids the need to apply the term explicitly. It also forces us to pinpoint the key issues that matter to understanding and treating someone suffering from ill health. But perhaps most persuasively, he argues that this approach allows us to distinguish the current state of a human from those we wish to promote or diminish, whereas the terms “disease” and “health” do not adequately highlight this critical distinction.

In short, philosophers of medicine continue to debate a range of accounts: in broad outline, the most vigorous disagreement centers on whether more objective, biologically-based, and generalizable accounts are preferable to those that incorporate social and experiential perspectives. It is clear that none satisfy all of the desiderata of a complete and robust philosophical account that also can be useful for practitioners; although some would dispute whether the latter should be a requirement, many believe that philosophy of medicine should be responsive to and helpful for actual clinical practices.

Some disease categories are far from straightforward in terms of being recognized, named, classified, and made legitimate both within medicine itself and for the wider society. In recent times there have been long-standing debates over a range of conditions including Lyme disease, fibromyalgia, and chronic fatigue syndrome (CFS), to name just a few (for extended historical discussions of these and related conditions, see Aronowitz 1998, 2001; Shorter 2008). Take CFS as an example: its main symptoms are fatigue after exertion over a period lasting at least six months, but sufferers can have a wide array of complaints in diverse systems of the body; the range of severity is as wide as the range of symptoms. The condition has been associated with several other controversial syndromes and sometimes equated to with them, most notably myalgic encephalitis and fibromyalgia, as well as other illnesses of inexact definition such as multiple chemical sensitivity and irritable bowel syndrome; more popular (and derogatory) labels also have been attached to it such as yuppie flu. Definitive evidence as to the cause or basis of CFS has remained elusive, and in the absence of causal explanations, accurate diagnoses and effective treatments often have been difficult to obtain. Thus the illness has been perceived by many as being illegitimate because of difficulties in proving the existence of a discrete disease condition, given the lack of traditional forms of clinical evidence for it, and it has had different statuses in different locales (see Ankeny and Mackenzie 2016). These issues severely impact on the lives of those affected by this condition, and on the care that is thought to be appropriate to be made available to them.

Mental illnesses (and the term “mental health” itself) also have traditionally posed considerable problems for categorization and conceptualization for both medical practitioners and philosophers of medicine. Many authors advocate the case that it is critical to make a distinction between mental and physical illness (Macklin 1972), particularly because of the moral implications associated with labeling a condition as mental or psychological. Psychiatry is a field which has historically been loaded with value judgments, many of which were quite dubious. There is a long history of using mental illness as a way to categorize behaviors which are socially deviant as well as those conditions of ill health with no apparent organic cause and which do not otherwise fit into our dominant biomedical model. Many scholars (e.g., Ritchie 1989; Gaines 1992; Mezzich et al. 1996; Horwitz and Wakefield 2007; Demazeux and Singy 2015) have critiqued approaches and the underlying assumptions of the various editions of the Diagnostic and Statistical Manual published by the American Psychiatric Association, which is a “bible” for psychiatric conditions for many practitioners and also has considerable public influence for instance on who can seek care. Key examples of contested issues within the DSM include the highly politicized nature of the processes of revision across various editions, various cultural, sexist, and gender biases inherent in specific diagnostic categories, and the relatively weak reliability and validity of the classification system.

One key question is whether the biomedical model is the most appropriate approach to psychological or mental conditions and their treatment. Some theorists have argued in favor of naturalistic accounts of disease, notably Thomas Szasz (1961, 1973, 1987). As a result, he famously claimed that “mental diseases” are a myth and do not exist because they do not result from tissue damage; in his view, all diseases must be correlated with this sort of physical damage. He thus argues that the concept of mental illness is a prescriptive concept used as though it were a merely descriptive one, and also a justificatory concept masquerading as an explanatory one. These conclusions lead him to a highly critical analysis of psychiatric practices, and to reclassifying such forms of suffering as “problems in living” rather than diseases. However it is not always clear in his account what his evidence for these claims is, and in particular whether he is making an in principle objection or one that is grounded in the history of the mistreatment of people with mental illnesses, and the disservice done to them in part because of the adoption of the medical model. In addition, some have noted that some psychiatric conditions do in fact correlate with physiologically detectable and other types of biological abnormalities. For instance twin studies have demonstrated that genetics is a major factor in the etiology of schizophrenia among other conditions typically considered to be psychiatric, although clearly not all conditions that are diagnosable according to contemporary psychiatric standards fit this model.

A prominent functionalist approach to mental disorders more recently has been that of Wakefield (1992, 1996, 2007), as discussed above, who argues that mental disorders are best understood as “harmful” dysfunctions, which permits a supposedly value-free foundation in terms of biological function gauged in evolutionary terms) with judgments coming in only in terms of the judgment of whether certain dysfunctions are harmful to their bearers. Such accounts have been criticized along lines similar to analyses of Boorsian accounts by emphasizing that function and dysfunction cannot in fact be defined independently of value terms, but Wakefield’s account also has been questioned in terms of its practical implications (e.g., Sadler and Agich 1995) and whether malfunction is a necessary component of mental disorder (Murphy and Woolfolk 2000).

Other authors, notably George Engel (1977), have argued for the need to unify our understandings of mental and physical illness under a broader, biopsychosocial model. Such a model would focus clinicians to take account of both the physical, psychological, and social factors that contribute to ill health, in contrast to the traditional biomedical model which is faulted for being overly reductionistic rather than holistic. Such an account, it is claimed, would be more effective in dealing with borderline cases including people who are told they are in need of treatment due to abnormal lab results or similar but who are feeling well, as well as those who appear to have no underlying somatic disease condition but are feeling unwell. Hence this type of account does not draw any sharp distinction between the physical and the mental (or even the social), leaving the question of appropriate therapies or approaches as a matter to be decided by the doctor and his or her patient. Engel compellingly defended this type of account as more appropriate not only for clinical work but for research and teaching in medicine. It is arguable that it has implicitly (and often explicitly) been adopted in much of current-day medical practice and teaching, although it is less clear whether it has had much influence in biomedical research, much of which tends to remain more reductionistic in its nature.

There is no widely accepted notion of what a scientific theory is. The logical positivists thought that theories are sets of propositions, formalizable in first-order logic, at one point, and as classes of set-theoretic models at another. For our purposes here one can distinguish two senses of theory, a narrower and a broader sense. In the narrower sense, a theory comprises a set of symbols and concepts used to represent the entities in a domain of discourse as well as a set of simple general-purpose principles that describe the behavior of these entities in abstract terms. In the broader sense, theory refers to any statement or set of statements used to explain the phenomena of a given domain.

In medicine one can find theories in both the narrower and the broader sense. Humorism, for instance, holds that the human body is filled with four basic substances or “humors”: black bile, yellow bile, phlegm, and blood. The humors are in balance in a healthy person; diseases are explained by excesses or deficiencies in one or more humors. Humorism has ancient origins and influenced Western medicine well into the 18th century. Eastern medicine has analogous systems of thought. Indian Ayurveda medicine, for example, is a theory of the three primary humors wind, bile, and phlegm, and diseases are similarly understood as imbalances in humors (Magner 2002).

In contemporary Western medicine, such highly unifying and general theories play a limited role, however. Evolutionary and Darwinian medicine may well constitute exceptions but these are at best emergent fields at present (see Méthot 2011). Contemporary Western medical researchers and practitioners instead seek to explain medical outcomes using mechanistic hypotheses about their causes—symptoms by hypotheses about diseases, diseases by hypotheses about antecedents, epidemics by hypotheses about changes in environmental or behavioral conditions (Thagard 2006). What distinguishes these contemporary medical theories from the ancient approaches is that the causes of symptoms, diseases, and epidemics can in principle be as multifarious as the outcomes themselves; in the ancient approaches, lack of humoral balance was the only possible cause. In contemporary Western medicine, there is no presupposition concerning number, form, or mode of action of the causes that explain the outcome other than there being some cause or set of causes responsible.

Not every cause is equally explanatory. A given person’s death can be described as one by cardiac arrest, pulmonary embolism or lung cancer, for instance. The lung cancer may have had a genetic mutation, the deposition of carcinogens in lung tissue and smoking in its causal history. The smoking, in turn, was caused by the smoker’s proneness to addictive behavior, peer pressure and socio-economic environment, let us suppose. Which of the many candidate hypotheses of the form “ X causes (or caused) Y ”, where Y refers to the patient’s death, does best explain the outcome? There is no absolute answer to this question. The goodness of a medical explanation depends in part on the context in which it is given (see entry on scientific explanation ). When asked “Why did Y happen?” a coroner might refer to the pulmonary embolism, the patient’s physician to the lung cancer and an epidemiologist to the patient’s tobacco consumption. The adequacy of a medical explanation is related to our ability to intervene on the factor in question. A pulmonary embolism can be prevented by screening the patient for blood clots. The accumulation of carcinogens in lung tissue can be prevented by stopping smoking. By contrast, even though certain kinds of genetic mutations are in the causal history of any cancer, the mutation is not at present of much explanatory interest to most clinicians, as this is not a factor on which they can easily intervene. There is considerable current medical research to identify mutations associated with various subtypes of cancer and using these to develop targeted therapies and interventions, as well as to provide more accurate prognostic information. Medical explanation, thus, is closely related to our instrumental interests in controlling, preventing and controlling outcomes (Whitbeck 1977).

One issue that is currently debated in the philosophy of medicine is the desirability (or lack thereof) of citing information about the mechanisms responsible for a medical outcome to explain this outcome. While mechanisms are usually characterized in causal terms (e.g., Glennan 2002; Woodward 2002; Steel 2008), it is not the case that every cause acts through or is a part of some mechanism, which is understood as a more or less complex arrangement of causal factors that are productive of change (e.g., Machamer et al. 2000). Absences, such as lack of sunlight, can cause medical outcomes but are not related to them through continuous mechanisms from cause to effect (Reiss 2012). Neuroscientific explanations are often acceptable despite the lack of knowledge or false assumptions about mechanisms (Weber 2008). However, we may ask whether mechanistic explanations are generally preferable to non-mechanistic causal explanations.

Many medical researchers and philosophers of medicine subscribe to a reductionist paradigm, according to which bottom-up explanations that focus on the generative physiological mechanisms for medical outcomes are the only acceptable ones or at least always preferable. Indeed, macro-level claims such as “Smoking causes lung cancer” seem to raise more questions than they answer: Why does smoking have adverse health consequences? To prevent these consequences, is it necessary to stop smoking? Is it possible to produce cigarettes the smoking of which has fewer or no adverse consequences? What is the best policy to improve morbidity and mortality from lung cancer? Knowing that it is specific carcinogens in tobacco smoke and genetic susceptibility that are jointly responsible for the onset of the disease helps to address many of these questions.

Nevertheless it would be wrong to assume that we cannot explain outcomes without full knowledge of the mechanisms responsible. When, in the mid-1950s, smoking was established as a cause of lung cancer, it was certainly possible to explain lung cancer epidemics in many countries where people had exchanged pipe smoking for cigarette smoking half a century earlier—even though the mechanism of action was not understood at the time. Differences in lung cancer incidence between men and women or between different countries can be explained with reference to different smoking behaviors. Policy interventions, in this case the addition of warning labels to cigarette packets, could not wait until sufficient mechanistic knowledge was available, nor did they have to wait.

For reasons such as these, a number of philosophers of medicine have proposed to adopt an “explanatory pluralism” for medicine (De Vreese et al. 2010; Campaner 2012). If nothing else, this is certainly a position that is consistent with the explanatory practices in the field.

As in many fields, debates over reductionism versus holism are rife in medicine both with reference to medical research and practice, and the terms often are used rather loosely to mean a range of things (for a related discussion see entry on reductionism in biology ). In the broadest terms, reductionistic approaches to disease look for fundamental mechanisms or processes that are the underlying causes of that disease. In recent years in light of large-scale genomic sequencing initiatives notably the Human Genome Project, there has been considerable emphasis on reducing diseases to the genetic or molecular level. Those who advocate more holistic approaches note that reductionism leaves out important information based on the patient’s experiences of the disease at the phenotypic level, and such information is critical to pursuit of effective treatments. Many diseases typically viewed as “genetic” have proven to be extremely difficult in practice to reduce to unified disease entities with singular (or simple) genetic causes, including mental illnesses (Harris and Schaffner 1992), cystic fibrosis (Ankeny 2002), and Alzheimer’s disease (Dekkers and Rikkert 2008). As Catherine Dekeuwer (2015) notes, given that there probably is genetic variation in susceptibility to virtually all diseases, there is no clear demarcation between genetic diseases and diseases for which there are genetic risk factors; hence she argues that our tendency to focus on genetic determinants of disease may reinforce folk notions of the geneticization of both people and of human behavior.

With regard to research, critics of reductionism point out that there has been an overemphasis on the pursuit of genetic or molecular level explanations of disease to the neglect of alternative levels of explanation. Further, such limitations are highly detrimental to patients, especially because there are not likely to be short-term cures or treatments for most genetic diseases, perhaps beyond avoiding having children carrying particular genes in the first instance (see for instance Hubbard and Wald 1999), although this domain of medicine is rapidly changing as new treatments are developed and the understandings of the effects of genomic mutations improve. Focusing overly or solely on the genetic level results in a process which the sociologist Abby Lippmann (1991) terms “geneticization”, namely reducing the differences between individuals to their DNA, and in turn viewing genetics as the most promising approach to curing disease, rather than viewing people and the illnesses that they suffer at a phenotypic and much more environmentally-situated level. In addition, as Elisabeth Lloyd (2002) argues, higher levels of social organization that are culturally sanctioned have unrecognized causal effects on health, and hence medical research should not be restricted solely to the molecular level.

Fred Gifford (1990) claims that although all phenotypic traits are the result of an interaction between genes and the environment within which they are expressed, nonetheless it makes sense to distinguish certain traits as “genetic”; he argues in terms of populations that if it is genetic differences that make the differences in that trait variable in a given population, and if genetic traits can be individuated in a way that matches what some genetic factors cause specifically, then a trait (including a disease trait) can be understood as genetic. Kelly Smith (1992) disputes this, noting that the second condition depends on an extremely problematic distinction between causes (in this case genes) and mere conditions (e.g., epigenetic factors). Lisa Gannett (1999) argues for a “pragmatic” account of genetic explanation, claiming that when a disease is classed as “genetic”, the reasons for singling out genes as causes over other conditions necessarily include pragmatic dimensions inasmuch as they are relative to a given causal background (which includes both genetic and nongenetic factors), relative to a population, and relative to our present state of knowledge. More recently it has been argued that although explanatory reduction cannot be defended on metaphysical grounds, reductive explanations might be indispensable ways to address certain questions in the most accurate, adequate, and efficient ways (van Bouwel et al. 2011).

“Evidence-based medicine” (EBM) describes a movement that was started (under that name) in the early 1990s by a group of epidemiologists at McMaster University in Hamilton, Canada, as a reaction against what was perceived as an over-reliance on clinical judgment and experience in making treatment decisions for patients. According to a widely cited definition:

Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. (Sackett et al. 1996: 312)

Such a definition has bite only when the concept of evidence used is relatively narrow. In particular, it should not allow clinical judgment and experience to count as “best evidence”.

To this effect, proponents of EBM have developed so-called “hierarchies of evidence” that categorize different research methods with respect to their supposed quality. While there is no universally accepted hierarchy, the different proposed hierarchies all agree in the priority they give to randomized controlled trials (RCTs) and reviews thereof. A typical hierarchy looks as follows (Weightman et al. 2005):

Evidence produced by RCTs has thus been called the “gold standard” of evidence in EBM (e.g., by Timmermans and Berg 2003).

In an RCT, a population of individuals who might benefit from a new medical treatment are divided into a treatment group—the group whose members receive the new treatment—and one or several control groups—groups whose members receive either an alternative or “standard” treatment or a placebo. Individual patients are assigned to a group by means of a random process such as the flip of a coin. A placebo is an intervention that resembles the new treatment in all respects except that it has no known ingredients active for the condition under investigation (i.e., it is some kind of “sugar pill”). Patients, researchers, nurses, and analysts are all blinded with respect to treatment status of all patients until after the analysis. After a period of time, a pre-determined outcome variable is observed and the values of the variable are compared between the groups. If the value of the outcome variable differs between different treatment groups at the desired level of statistical significance, the treatment is judged to be effective.

Proponents of EBM regard RCTs as reliable means to judge treatment efficacy because they can help to control for a variety of (though not all) biases and confounders. If, for instance, the symptoms of a patient or group of patients improve after an intervention, this may be due to spontaneous remission rather than the treatment. An experimental design that compares a treatment group with one or several control groups is therefore better able to control for this confounder than a simple “before-and-after” design. Similarly, a design in which the allocation to treatment and control groups is done by a non-random process, it is possible that healthier patients end up in the treatment and less healthy patients in the control group. If so, the measured improvement may be due to the health status of the patients rather than the intervention. Especially if the allocation is done by a medical researcher who has a stake in the matter (for instance because she has developed the new treatment), allocation decisions may consciously or subconsciously be influenced by expectations about who will profit from the intervention and thus create unbalanced groups. Allocation by a random process helps to control this source of bias.

No one denies that RCTs are powerful experimental designs—and that their power stems from the ability to control numerous sources of bias and confounding. However, to refer to RCTs as the “gold standard” of evidence suggests that they are more. Specifically, one may be led to assume that RCTs are necessary for reliable causal inference or that RCTs are guaranteed to deliver reliable results. A number of philosophers of medicine have in the past decade or so argued that these stronger claims do not hold to scrutiny.

In particular, the following claims have been criticized:

• The logic of statistical significance tests requires randomization (Fisher 1935). Ronald Fisher invoked his famous tea lady thought experiment in order to make plausible that significance testing works only with randomized allocation. Suppose an English lady claims that she is able to tell whether tea or milk was poured into the cup first and we would like to test this assertion. If she gets it right each time in a series of eight cups (four “milk first” and four “tea first” cups), this result may be due to her usually sharp sense of taste. But it may also be for indefinitely many other reasons: she may know that milk was poured first in the first four cups and correctly identified the first four as “milk first” cups; the “milk first” cups differ in color or shape from the “tea first” cups or have any other visually identifiable features; a confederate recorded which cups were “milk first” and signals her; and so on. Fisher now argues that only if the allocation of tea to cups was done at random, the probability of the lady getting all eight cups right is correctly identified as the probability of her getting it right if she were to guess, having no discriminatory ability (which in this case is 1/70). Therefore, we can judge that she really does have an unusual discriminatory ability or something very unlikely must have happened (i.e., an event with the probability 1/70). But this is incorrect. In fact, there is still an indefinite number of ways in which she got the result even though she does not have a good sense of taste. If a confederate signals her the correct answer, the probability of her getting it right is very close to 1 independently of her discriminatory ability (Worrall 2007a). A good experiment would prevent this, but this has to do with other aspects of the experimental design, not randomization.
• Randomization controls for all confounders, known and unknown (Fisher 1935; Giere 1984). Many variables affect a patient’s probability of recovery: her gender, age, co-morbidities, genetic factors, compliance with the treatment regime, psychological factors and many more. If we want to judge that an observed difference in recovery rates between treatment groups is due to the intervention rather than these other factors, we have to make sure that the probability distribution of causal factors is the same between the different groups. Randomization is supposed to ensure this. However, for any finite test population size (and many RCTs do indeed have relatively small numbers of patients), it remains possible that treatment groups are unbalanced: old patients ending up in one group, younger in the other etc. While it is the case that the larger the number of patients in the RCT, the less likely it is that the groups are unbalanced with respect to any given factor, if there are many possible factors affecting the outcome it is actually very likely that some of them are unbalanced. Thus, in practice if it is noticed after randomization that the two groups are unbalanced with respect to a variable that is thought to affect the outcome outcome, then the groups are re-randomized or adjusted (Worrall 2002)
• It is possible to “prove” the results of an RCT to be correct (Cartwright 1989; cf. Worrall 2007b). Every scientist, at some point in his career, learns that one cannot judge X to be a cause of Y just because X and Y are correlated. According to a prominent theory of causation, viz. the probabilistic theory, causation is a form of correlation after all. Very roughly, the probabilistic theory holds that X causes Y just in case X and Y are correlated and all sources of confounding have been controlled (Reiss 2007). It can now be shown that under the probabilistic theory and a host of other assumptions (including the assumption that randomization has been successful in that the treatment groups are balanced with respect to prognostic factors), if the treatment status variable is correlated with the outcome variable, then the treatment must cause the outcome (Cartwright 2007). To give RCTs a special status in EBM on the basis of this reasoning would be to commit a logical mistake, however. The argument can only show that if all the assumptions behind an RCT are satisfied , the RCT will give a causally correct result. It does not show that RCTs are the only way to generate provably correct results. Indeed, it can relatively easily be shown that observational studies that identify so-called instrumental variables are similarly provably correct under a certain set of assumptions (Reiss 2005).

A final but very important issue is that of the external validity of the RCT results. Even under ideal conditions (i.e., when medical researchers have very strong reasons to presume the assumptions under which an RCT works to be satisfied), the RCT can only establish that the treatment is effective in the test population . Typical test populations differ from the target populations (i.e., those populations for whom the treatment has been developed and who will eventually receive the treatment) in more or less systematic ways. For example, many RCTs will exclude elderly patients or patients with co-morbidities but the treatment will be marketed to these patients. For financial reasons, many RCTs are nowadays conducted in developing countries whereas the treatments are mainly or exclusively marketed to patients in developed countries. Whereas the protocols for conducting an RCT are very strict and detailed, there are no good guidelines how to make treatment decisions when the patient at hand belongs to a population that differs from the population in which the RCT was conducted (e.g., Cartwright 2011).

There are in fact two problems of external validity in the application of RCT results. On the one hand there is the population-level problem of making an inference from test to target population . On the other hand, there is the problem of making an inference from population to individual . The RCT provides evidence for a population-level claim: “In population p (the test population) intervention X is effective in the treatment of condition Y ”. For this claim to be true, the treatment must be on average effective, which allows the effectiveness to vary among the individuals in the population. Indeed, it is possible that the intervention is effective (and beneficial) on average but ineffective or positively harmful in some individuals (i.e., members of some subpopulations). Proponents of EBM to some extent oversell their case when they write that EBM

de-emphasizes intuition, unsystematic clinical experience, and pathophysiologic rationale… and [instead] stresses the examination of evidence from clinical research. (Evidence-Based Medicine Working Group 1992)

because inferences from test to target population and from any population to the individual receiving the treatment are necessarily based on clinical judgment.

John Worrall argues that, at the end of the day, RCTs are a powerful means to control selection bias, but no more than that (Worrall 2002, 2007a,b). As he uses the term, selection bias occurs when treatment and control group are unbalanced with respect to some prognostic factors because a medical researcher has selected which patients will receive which treatment. Selection bias in this sense obviously cannot occur in an RCT because in an RCT the allocation is made by a random process. But it is also clear that randomization is at best sufficient but not necessary to achieve the result. A large number of alternative designs may be used to the same effect: allocation can be made by a strict, albeit non-random protocol; allocation is made by non-experts who are unrelated to the treatment development and therefore have no expectations concerning outcomes; treatment and control groups are deliberately matched (again by persons who have nothing at stake or according to some protocol); and so on.

A controversial issue is the role of mechanistic knowledge, that is, knowledge about the biological and physiological mechanisms responsible for medical outcomes (and thus treatment efficacy) should play in EBM. As mentioned above, the RCT provides evidence for black-box causal claims of the form “In population p , intervention X is effective in the treatment of condition Y ”. As we have seen, proponents of EBM also believe EBM to de-emphasize patho-physiologic rationale (a different term for “mechanistic knowledge”). Nevertheless, a number of philosophers of medicine have pointed out that mechanistic knowledge is in fact important in EBM or that it should receive more attention. Federica Russo and Jon Williamson have, for instance, argued that causal claims need both statistical evidence as well as evidence about the mechanisms that connect an intervention with the outcome variable in order to be established (Russo and Williamson 2007). Others disagree (Reiss 2012) or qualify the claim (Gillies 2011; Howick 2011a; Illari 2011). Further, it has been pointed out that mechanistic knowledge plays an important role in the design and preparation of an RCT, as well as in the interpretation and application of RCT results (La Caze 2011; Solomon 2015). Especially when it comes to extrapolating research results from a test to another population, mechanistic knowledge is supposed to be vital (Steel 2008; see also next section). On the other hand, knowledge about mechanisms is often highly problematic and should not be relied on too heavily in applications (Andersen 2012).

New therapies are often trialed using animal models before they are tested on humans in a randomized trial. Animal models also play important roles in establishing whether or not a substance is toxic for humans. The International Agency for Research on Cancer (IARC), for example, classifies substances with respect to the quality of the evidence for their carcinogenicity into five groups. Evidence from animal models is referred to in the characterization of each group (IARC 2006). This raises questions about how such extrapolations from animal models to humans work, and how reliable they are.

Animal models are widely used in biomedical research because experimental interventions on animals are easier to conduct and cheaper than experiments on humans. Both kinds of experiments involve ethical dilemmas, but animal experimentation is usually regarded as less problematic from an ethical point of view than experimentation with humans. At any rate, the number of animals killed, maimed, or made sick in biomedical research is much higher than the number of humans adversely affected in this research.

There is a fundamental inferential problem in transferring what has been learned in any model (whether human, animal, or whatever) to some target population of interest has been described as the “experimenter’s circle” (Steel 2008). The problem is essentially this. What is true of a model can be presumed to be true of the target only to the extent that the model is similar to the target in relevant respects. The reason we experiment on models in the first place is, however, that the model differs in important respects from the target (if animals were just like humans, we would not find experiments on the former to be ethically less problematic than experiments on the latter). Extrapolation—the inference from model to target—is therefore only worthwhile to the extent that there are significant limitations in our ability to study the target directly. If so, there can be no good grounds to decide whether a model is a good one for the target. To do so, we would have to investigate whether the target is relevantly similar to the model; but if we could do so, there would be no reason to study the model in the first place.

This inferential problem has led some commentators to maintain highly skeptical views concerning our ability to use animals as models for humans in biomedical research. Hugh LaFollette and Niall Shanks argue that animal models cannot be reliably used for extrapolation at all, but at best only heuristically, as sources of hypotheses that have to be tested on humans (LaFollette and Shanks 1997). They introduce two terms to make their argument: causal analogue model (CAM) and hypothetical analogue model (HAM). The former can be used to make reliable predictions about target populations of interest; the latter only heuristically. The main premise in their argument that animal models in biomedical research are at best HAMs but not CAMs is that for a model to be a CAM there cannot be causally relevant disanalogies between model and target—a condition which is rarely if ever met by animal models (again, this is why we study animals in the laboratory in the first place).

Daniel Steel (2008: ch. 5) argues that LaFollette and Shanks’ condition for reliable extrapolation is too stringent. Whether a claim about a model can be extrapolated depends, he argues, also on the strength of the claim to be exported. It is one thing, say, to reason from

x % of the members of population p will show symptoms of poisoning after ingesting substance S

x % of the members of population \(q \ne p\) will show symptoms of poisoning after ingesting substance S ,

quite another to reason from the quantitative claim to a qualitative claim such as “Substance S is poisonous for the members of q ”.

Steel’s own reconstruction of how extrapolation works in the biomedical sciences is called comparative process tracing . He assumes that causes C (such as medical interventions or the ingestion of toxic substances) bring about their effects E (such as the appearance of symptoms or improvements or deteriorations of symptoms) through a series of steps or stages. To trace a causal process means to investigate through what set of stages C brings about E . Process tracing is comparative when the set of stages through which C brings about E in one species or population is compared to the set through which it does so (if it does so indeed) in another.

Comparative process tracing would be futile if, in order to know that C causes E in the target species or population, we would have to compare all the stages of the process between model and target. This is because in order to do so, we would have to know all stages of the process through which C causes E , but if we did, we would already know that C causes E . This brings us back to the extrapolator’s circle. Steel now argues that comparative process tracing avoids the extrapolator’s circle by demanding processes to be compared only at stages where they are likely to differ and assuming that differences between model and target matter only to stages that are downstream from where they obtain. Thus, if we compare an intermediate stage of the process which obtains in the model with that stage in the target and find them to be relevantly similar, then the only differences that may still obtain will be downstream from this stage. We therefore do not require knowledge of the entire process from C to E in the target, and the extrapolator’s circle is successfully avoided.

How useful comparative process tracing is as a method for extrapolation for the biomedical sciences depends on how reliable the assumption that only downstream differences matter to extrapolation is, the reliability with which stages where there might be differences between model and target can be identified and the reliability of our mechanistic knowledge more generally. If, say, our reasons for supposing that C causes E through a series of stages X , Y , Z in the model, or that X and Z are the stages where model and target are likely to differ, are not very strong, then the method does not get off the ground. This is an issue that depends on the quality of the existing knowledge about a given case and cannot be addressed for the biomedical sciences as a whole. There are certainly some examples of well-established causal claims where it is known only that C causes E but the details of the causal process are entirely beyond our current grasp (Reiss forthcoming-a).

An alternative to comparative process tracing that has been proposed is extrapolation by knowledge of causal capacities . If C has a causal capacity to bring about E , then C causes E in a somewhat stable or invariant manner. Specifically, C will then continue to contribute to the production of E even when disturbing factors are present (Cartwright 1989). To establish that C has the causal capacity to cause E therefore means to show that C ’s causing E is independent of the background in which C and E occur to some extent. And therefore, if C causes E in a model species or population and C has the causal capacity to bring about E , then there is some reason to believe that C causes E also in the target species or population (for a defense, see Cartwright 2011).

The usefulness of the method of extrapolation by causal capacities depends, among other things, on the extent to which biomedical factors can be characterized as having capacities. Many biomedical causes do indeed have some degree of stability. The sickle cell trait is 50% protective against mild clinical malaria, 75% protective against admission to the hospital for malaria, and almost 90% protective against severe or complicated malaria (Williams et al. 2005). These figures suggest a reading along the lines of,

in the presence of the sickle cell trait (a preventer of/disturbing factor for malaria), infection with Plasmodium malaria continues to affect outcomes consistently. (Reiss 2015b: 19)

But there is a high degree of interaction with other factors as well. Whether or not a substance is toxic for an organism depends on minute details of its metabolic system, and unless the conditions are just right, the organism may not be affected by the substance at all. To what extent this method will be successful therefore similarly case-dependent as comparative process tracing.

As we can see, there is no general answer to the question whether or not animal studies are valuable from a purely epistemic (as opposed to ethical, economic, or combined) view. Other authors have developed a practice-based taxonomy of animal modes to allow more accurate assessment of the epistemic merits and shortcomings, and predictive capacities of specific modeling practices (Degeling and Johnson 2013). There is much evidence that species differ enormously with respect to their susceptibility to have toxic reactions to substances. Thus, while it is very likely that for any one toxin, there is some species that is predictive of the human response, it is often hard to tell which one is most appropriate for any particular toxin. A species that predicts the human response well for one substance may be a bad model for another. However, some authors suggest that extrapolations from animal models have been made successfully in at least some cases (Steel 2008 discusses the extrapolation of claims concerning the carcinogenicity of aflatoxin from Fisher rats to humans; see Reiss 2010a for a critical appraisal and Steel 2013 for a response).

Frequently, in the biomedical sciences, reliable animal or other non-human models are not available and RCTs on humans are infeasible for ethical or practical reasons. In these and other cases, biomedical hypotheses can be established using observational methods. As we have seen in Section 5 , evidence-based medicine regards observational methods as generally less reliable than RCTs and other experimental methods. This is because observational studies are subject to a host of confounders and biases that can be controlled when the hypothesis is tested by a—well-designed and well-conducted—RCT. But it is not the case that observational methods cannot deliver reliable results. In fact, it is well possible that the medical knowledge that has been established observationally by far exceeds the knowledge that comes from RCTs. Here are some examples of medical interventions that are widely accepted as effective but whose effectiveness has not been tested using RCTs: penicillin in the treatment of pneumonia, aspirin for mild headache, diuretics for heart failure, appendectomy for acute appendicitis and cholecystectomy for gallstone disease (Worrall 2007a: 986); automatic external defibrillation to start a stopped heart, tracheostomy to open a blocked air passage, the Heimlich maneuver to dislodge an obstruction in the breathing passages, rabies vaccines and epinephrine in the treatment of anaphylactic shock (Howick 2011b, 40).

Observational studies often begin by reporting a recorded correlation between a medical outcome of interest and one or a set of independent variables: lung cancer rates are higher in groups of smokers than in groups of non-smokers, liver cancer rates are higher in populations that tend to consume food that has been contaminated with aflatoxin than in populations whose food is uncontaminated, to give a few examples. That smoking causes lung cancer, or aflatoxin cancer of the liver, would indeed account for the observed correlations. But so would a variety of other hypotheses. Generally, if two variables X and Y are correlated, it may be the case that X causes Y , Y causes X or a common factor Z causes both X and Y (or a combination of these). In the smoking/lung cancer case, all three hypotheses were invoked as possible accounts of the data. Ronald Fisher famously proposed that it may be the case that early stages of bronchial carcinoma cause an individual to crave cigarettes, and he provided some evidence that both smoking behavior and susceptibility to lung cancer have a common genetic basis (Fisher 1958). Moreover, it is possible that the correlation itself is spurious—that the data are correlated as per some measure of correlation such as Pearson’s coefficient, but that the underlying variables are not in fact correlated in the population of interest. Selection bias is normally understood as the bias that obtains when individuals self-select into the observed population and the reasons for which they do so are correlated with the outcome variable. If an observational study examines only hospitalized patients and smokers are more likely to be in hospital for reasons that have nothing to do with lung cancer, then smoking and lung cancer can be correlated in the data even if the variables are independent in the general population. Mismeasurement and diagnostic error provide another account of spurious correlation. Suppose tuberculosis was on the rise a generation or so after many people traded pipe smoking for cigarette smoking. Then, if it was difficult to distinguish a death from tuberculosis from a death from lung cancer because necropsy techniques were not sufficiently well developed, the data might again show a correlation even though the population variables are uncorrelated.

Retrospective observational studies work by ruling out alternative hypotheses such as these ex post rather than controlling for them ex ante as RCTs do (Reiss 2015a). In an RCT, mismeasurement should not obtain because the protocol specifies measurement procedures for the outcome variables in great detail in advance. Selection bias should not obtain because patients are randomized into treatment groups. Once allocated to a group, they are prevented from obtaining another treatment elsewhere, and researchers make sure that patients comply with the treatment regime. But there are equivalent means to rule out these possibilities in observational settings. While it may well be the case that early stages of cancer cause a craving for cigarettes, this hypothesis cannot explain the protective effect that stopping smoking has. At the time of the smoking/lung cancer controversy in the mid-1950s, misdiagnosis was indeed a problem. However, it could be shown that in order to account for the observed rise in lung cancer incidence, the diagnostic error at autopsy among older people would have to have been an order of magnitude higher than the diagnostic error among younger people (Gilliam 1955). Mismeasurement could therefore also be ruled out. Similar considerations helped to rule out other alternative hypotheses (Cornfield et al. 1959).

Even if one were to believe, with the proponents of EBM, that observational studies are generally less reliable than RCTs, medicine could—fairly obviously—not do without them. There are large numbers of pressing questions that could not be addressed by an RCT for ethical, financial and other practical reasons. No-one would seriously consider testing a proposition such as “Aflatoxin causes cancer of the liver (in humans)” by an RCT. This is not merely because of the straightforward ethical issues involved in deliberately exposing humans to a potential carcinogen for the sake of medical progress. It is also because exposure to low levels of aflatoxin can take many years or even decades to produce symptoms. The ability of researchers to control food intake in a large group of experimental subjects for a very long has evident practical financial and limitations. RCTs can also not be used when researchers or patients or both cannot be blinded, and many medical interventions do require the doctor’s or the patient’s knowledge of details about the intervention.

Moreover, it is not clear that RCTs are always more reliable than observational studies to answer questions both methods are able to address. Whether or not a study is reliable depends on whether or not confounders and biases have in fact been eliminated, not by which method they have been eliminated. Issues concerning the reliability of a method can be entangled with issues concerning its ability to address the research question the biomedical scientist seeks to answer. Both RCTs and observational studies in the biomedical sciences are typically employed to test rather complex hypotheses about the safety and efficacy of medical interventions. It may well be that some of the issues are more reliably treated by one method and others by the other.

A famous controversy in which the results from observational studies and those from RCTs conflicted was that over the benefits and safety of hormone replacement therapy (HRT) in the early 2000s (Vandenbroucke 2009). HRT seemed protective for coronary heart disease in observational studies, whereas RCTs indicated an increase in the first years of use. For breast cancer, combined hormone preparations showed a smaller risk in an RCT than in observational studies. In the end it turned out that the timescale of the effects was responsible, and that because of the way they are typically run, observational studies got some issues right and RCTs others:

The observational studies had picked up a true signal for the women closer to menopause. In the randomised trial, that signal was diluted because fewer women close to menopause were enrolled… The randomised trials had it right for coronary heart disease but failed to sufficiently focus on women close to menopause for breast cancer. The main reasons for the discrepancies were changes of the effects of HRT over different times… (Vandenbroucke 2009: 1234)

Case reports remain extremely popular in medicine both as publications to communicate within the field and for pedagogical purposes. In short, a case report describes a medical problem experienced by one or more patient, usually involving the presentation of an illness or similar that in some way difficult to explain or categorize based on existing understandings of disease or understandings of physiology and pathology. Cases in medicine take highly standardized forms of presentation which are inculcated in health care professionals during their education, and many have commented on their highly standardized narrative structure and its epistemic and other implications (Hunter 1991; Hurwitz 2006). Cases typically provide details on the presentation of the disease, diagnosis, treatment, and outcomes for the patient, with a focus on practice-based observations and clinical care (rather than the results of randomized controlled trials or other experimental methodologies). One of the purposes of cases is to gather detailed information including facts that may not be immediately relevant, but that could prove to be (Ankeny 2011). Thus the information contained in the case and the case itself can be useful over the long term particularly if it can be systematically combined with other cases into larger datasets.

Single cases are seen by some as problematic as a form of evidence particularly in the era of EBM, because they often focus on highly unusual manifestations of illness and disease, rather than typical or repeatedly observed conditions that might support generalizable rules. This feature has led some to describe medicine as a “science of particulars” (Gorovitz and MacIntyre 1976), or as an art rather than a science (Pellegrino 1979), particularly in processes of diagnosis (see Section 9 ). However standard accounts of EBM include the case series as a type of evidence, which involves the aggregation of individual cases of patients with similar attributes (e.g., who received the same treatment or therapy) who are tracked over time using descriptive data and without utilizing particular hypotheses to look for evidence of cause and effect. EBM does place the case series quite low in its hierarchy of evidence but nonetheless it is acknowledged that cases have potential usefulness especially where forms of evidence that rate more highly are not available, as may often be the case where human patients are concerned due to practical or ethical reasons, or where the available evidence at higher levels has been produced in a manner that is methodologically or otherwise flawed.

Cases can serve other purposes: for instance analyses of cases can provide working hypotheses about casual attribution that can ground further tests of causal relations (Ankeny 2014), which in turn allows use of more traditional methodologies such as RCTs, cohort studies, and so on to explore these causal hypotheses. In the context of clinical care, cases can allow health care providers to identify a cause that can be manipulated to cure (or prevent) the condition in question, in order to treat ill patients, even in the absence of more rigorous forms of evidence.

Diagnosis is the process through which a clinician determines what is wrong with a patient who is ill or ailing in some way. Although a critical part of the practice of medicine, it has been relatively neglected in the literature of philosophy of medicine particularly in comparison to more statistically-based methods for evaluating evidence in other fields (Stanley and Campos 2013). The key philosophical issues that arise in this context relate to how such determinations can be made in a manner that is accurate given the high amount of uncertainty and complexity often associated with the human condition, and hence involve logical, epistemological, and ontological issues. The usual way of proceeding in a clinical setting is to ask the patient to articulate what is ailing him or her, and thus to use a standardized reporting format to detail various symptoms which represent subjective manifestations of the illness or disease. In addition, clinicians perform various tests and examinations that allow more objective manifestations or signs to be recorded, such as heart rate, blood pressure and count, reflexes, and so on. A perennial debate in the philosophy of medicine is what constitutes symptoms and signs and whether they are in fact distinct, which relates to deeper issues about the realism of disease conditions as discussed above ( Section 2 ).

The tricky part of the process is to find a means for mapping these symptoms and signs onto a particular disease condition. Some would advocate that this process is no different than usual methods in philosophy of science for hypothesis generation and testing based on evidence, and this type of model fits with what is termed differential diagnosis. Differential diagnosis involves a set of hypothetical explanations for a particular condition which come to be ruled in (or out) based on the evidence together with additional data that is collected, hence relying on a form of reasoning via decision nodes or algorithmic pathways (Stanley and Campos 2013). However, the details of the rules of reasoning that underlie this sort of process remain largely unarticulated, as does the amount of “tacit” knowledge that may contribute to diagnostic reasoning.

There are various ways in which diagnosis is taught and operationalized in clinical settings: in some subspecialties in particular, “pattern” recognition often using pictorial representations seems to be common, and hence diagnosis is a form of recognizing repeated patterns. However this approach can be dangerous particularly among novices, given the large number of similar patterns among common diseases. Some have claimed that the making of a diagnosis is both a deontic act and computable, and that diagnoses are relative only inasmuch as they occur in a complex context which in turn makes them a social practice (Sadegh-Zadeh 2011). Computer-assisted diagnostic techniques have improved and are used increasingly in clinical settings; Kenneth Schaffner (1981) provided an early analysis of the criteria which an ideal diagnostic logic would need to satisfy (for updated discussions see Schaffner 1993, 2010, and for arguments about the limitations of such types of diagnosis see Wartofsky 1986). In recent years there is a relative consensus among medical professionals and those involved in medical informatics that medical diagnosis almost certainly relies on some form of “fuzzy logic” (e.g., Sadegh-Zadeh 2000; Barro and Marin 2002).

As we have seen in Section 5 , hierarchies of evidence in evidence-based medicine rank study results from “systematic” clinical research such as RCTs and observational studies higher than “unsystematic” expert opinion. The epidemiologists who initiated the formal EBM movement in the early 1990s had good reason to be skeptical about expert opinion. When therapies are subjected to systematic tests, tradition and expert opinion are sometimes shown to be flawed. John Worrall discusses three examples: grommets for glue ear, ventricular ectopic beats repressing substances such as encainide or flecainide for cardiac arrest, and routine fetal heart rate monitoring to prevent infant death (Worrall 2007a: 985). In each case we have a procedure the effectiveness of which is indicated by common sense and knowledge about the patho-physiological pathways—glue ear is a condition produced by a build-up of fluid in the middle ear that is unable to drain away because of pressure differentials, grommets act by letting air into the middle ear and thereby equalizing pressure, for instance—but which, when tested by a randomized trial, turns out to be ineffective at best and positively harmful in the worst case.

Misjudgments concerning the efficacy of therapies for purely epistemic reasons are not the only worry that one might have about expert opinion. Medical experts and patients are in what economists call a principal-agent relationship. The principal—in this case, the patient—desires the delivery of a certain good or service—in this case, his health. He instructs an agent—in this case, the doctor—with it, because he lacks the expertise to produce the good himself. The good can only be produced with uncertainty: no therapy is 100% effective. Moreover, the success at delivering the good depends in part on the agent’s effort. The doctor may not always choose the optimal therapy for a patient (we can suppose that it takes some effort to select the optimal therapy for a patient), and any therapy can be implemented sloppily. Moreover, lacking expertise, the patient cannot observe the level of effort a doctor puts in. He therefore cannot design a contract that makes payments dependent on level of effort (much less on success, as success is in part influenced by factors outside of either party’s control). Agents therefore have an incentive to cheat: not to put in the level of effort required to select and deliver the optimal therapy from a patient’s point of view.

If patients and doctors were perfectly rational and motivated only by their own material welfare, and in the absence of regulation, there simply wouldn’t be a market for health services. Doctors would choose therapies that are best for them and not for patients, and patients would anticipate this behavior and stop seeking doctor’s services in the first place. In our world, neither patients nor doctors are particularly rational, nor are they motivated purely by self-interest, there are ethical codes such as the modern form of the Hippocratic Oath, and the health sector is one of the most regulated industries of all. All this does not, however, change the incentive structure in which doctors and other providers of health services operate. Because they and not the patients are experts, they have incentives to choose therapies that are in their best interests and not in the patients’ interests.

There is a further complication. Many, probably most, doctors have connections to the pharmaceutical industry in one form or another. According to one study, 94% of U.S. physicians receive financial benefits from the pharmaceutical industry (Bekelman et al. 2003). Even if we suppose that doctors do not prescribe a therapy because they are paid to do so, marketing efforts directed at them will influence treatment recommendations, if only because they know certain pills better than others, or because some treatments are at the top of their heads.

For all these reasons, the EBM principle that treatment decisions should be based on the best available evidence from systematic research does not come out of nowhere. If, say, there is an RCT or an observational study that reports that treatment X is more effective at relieving symptoms S than treatment Y , it would seem bad to recommend a patient who suffers from S to take Y because his GP doesn’t know about X , doesn’t know the study result, personally profits from prescribing Y or is inattentive. However, while these are all bad reasons to recommend Y over X in the light of the study result, there may be a variety of good reasons.

As discussed in Section 5 , RCTs and many observational studies are population-level studies, which produce average results that are not straightforwardly applicable to individuals. If, say, treatment X reduces the risk of suffering from some adverse event over a period of time by 50% in population p , that is, the risk ratio (RR) for this treatment is 50%, then there may be no individual in p for whom the treatment halves the risk. Instead, the RR may vary dramatically among the subpopulations of p , and it may well be the case that Y is more effective than X for some subpopulations.

The same is true of side effects. Tonelli (2006) discusses a case where a patient who suffers from multiple sclerosis receives a treatment that does seem to alleviate her symptoms, but since she has started taking it, she has been plagued by severe episodes of depression. Clinical trial results indicate that the drug is effective in treating multiple sclerosis, and no adverse psychiatric effects have been reported. Her GP and her psychiatrist now debate whether to continue the treatment. There are various reasons why the clinical studies do not show evidence of mental health effects: the trial subjects weren’t properly screened for depression; adverse effects were found but not reported; the adverse effects were not statistically significant—but they may have been clinically significant for some subpopulations; the side effects only obtain in populations that differ from the trial populations.

This case shows that a treatment’s effectiveness in relieving the symptoms of the disease for which it was prescribed is not the only consideration when making a treatment decision. The goal of the treatment is to improve the patient’s wellbeing, which is well recognized by the proponents of EBM. A patient’s wellbeing has many components, of course, and the symptoms of any given disease are at best one element in its determination. This is another reason why clinical judgment must be exercised in the derivation of a treatment recommendation.

Unfortunately, experts—like all humans—are notoriously bad decision makers. Cognitive psychologists have established a large number of cognitive biases to which human experts are subject: they suffer from overconfidence (e.g., Dawes and Mulford 1996) and hindsight bias (e.g., Fischhoff 1975; Hugh and Dekker 2009); are regularly outperformed by simple mechanical algorithms (e.g., Grove and Meehl 1996); commit the conjunction fallacy (Tversky and Kahneman 1983; Rao 2009), and many others.

To give an example for a simple mechanical algorithm outperforming experts, consider the Goldberg Rule, according to which a patient is to be qualified as neurotic if \(x = (\textrm{L} + \textrm{Pa}+ \textrm{Sc}) - (\textrm{Hy} + \textrm{Pt}) > 45\) (where L is a validity scale and Pa, Sc, Hy, and Pt are clinical scales of a Minnesota Multiphasic Personality Inventory or MMPI test) and as psychotic otherwise. Lewis Goldberg tested the rule on a set of MMPI profiles from 861 patients who had been diagnosed by the psychiatric staff in their hospital or clinic and found it to be 70% accurate; clinicians’ accuracy ranged from 55% to 67% (Goldberg 1968; for a discussion, see Bishop and Trout 2005).

There is no one strategy to deal with the various biases and interests that affect clinicians’ judgments. Better numeracy and statistical training at universities can help to eliminate some cognitive biases (Gigerenzer 2014). Computer-aided medical diagnosis and decision making may alleviate others. No training or computer program can make normative judgments, however, and neither will help with adverse incentive structures and financial interests. These difficulties also beset committees of medical experts to which we are turning next.

One way to help overcome expert bias is by making medical decisions not dependent on individual expert judgments but instead have groups of experts coming to some form of aggregate judgment. The U.S. National Institutes of Health, for instance, used to organize so-called consensus conferences designed to resolve scientific controversy. Panel members are chosen from clinicians, researchers, methodologists and the general public. Federal employees are not eligible, nor are researchers who have published on the subject at hand or have financial conflicts of interest (Solomon 2007). These exclusions are intended to contribute to controlling government influences as well as any biases due to financial or intellectual interests.

Consensus conferences and other mechanisms for reaching group judgments are clearly no panacea. Miriam Solomon (2015), for instance, argues that consensus conferences tend to “miss the window of epistemic opportunity” in that they often take place after the medical community has already settled an issue. More important in the present context is the observation that while these conferences possibly help to control some forms of partiality, they are ineffective in reducing others and may be responsible for the introduction of new biases. One concern is that panel members may read the existing evidence selectively, for instance, because of weighing salient studies or studies that are available to them more heavily. Another is that phenomena such as groupthink (Janis 1982) and peer pressure may influence results. In an NIH consensus conference panel members have to come to a verdict after only two days of hearings and deliberations. Under these conditions it is certainly possible that more outspoken panel members or those who perform well under extreme pressure have a undue influence on results. Moreover, it is not clear that excluding clinicians who have published on the issue at hand is always such a good idea. After all, it is not implausible to maintain that those scientists who actively work on a research topic are those who best understand it and therefore can make the best informed judgments. For these and other reasons, Solomon (2007, 2015) explores the consequences of judgment aggregation. In this process group members typically do not deliberate but instead cast their opinions which are then aggregated using some pre-determined procedure. The majority rule would be a simple example of such a procedure.

Coming to a group judgment using a mechanical procedure such as majority vote has a number of advantages. First, there are epistemic advantages that can be illustrated by Condorcet’s Jury Theorem. This theorem shows that if (a) the judgment concerns a proposition that can either be true or false, (b) jury members have an independent probability \(>.5\) that they get the judgment right, (c) the individual judgments are aggregated using majority vote, then the larger the jury, the more likely it is to reach the correct group judgment. Under these conditions, then, a committee of experts is likely to make a better judgment than a single expert. Moreover, in the absence of deliberation and pressure to come to a unanimous results, and when voting is secret, the influence of groupthink, peer pressure etc. is attenuated or eliminated.

When conditions (a)–(c) do not hold, results are more ambiguous or even negative. When experts are not reliable, i.e., the individual probability of getting the judgment right is \(<.5\), the larger the group, the less likely is it to reach a correct group judgment and the optimal group size is a single expert. When the outcome can have more than two values, inconsistent results can obtain. This can easily be demonstrated with an example in which there are three possible outcomes and three experts. Suppose, for instance, that a panel has to decide which of three treatments A , B and C is the most effective in treating some disease. The individual panel members have the following individual rankings:

Expert I: \(A > B > C\)

Expert II: \(B > C > A\)

Expert III: \(C > A > B\),

where “>” means “more effective”. There is now a majority that holds that A is more effective than B (I&III), a majority that holds that B is more effective than C (I&II) and a majority that holds that C is more effective than A (II&III). More generally, whenever there are logical relations among the propositions to be decided (in this case: \(A > B\) and \(B > C\) implies that \(A > C\)), there are at least three panel members, and votes are aggregated by the majority rule, inconsistencies can arise at the group level (Pettit 2001).

The majority rule is of course only one way to aggregate judgments. The Delphi method (e.g., Dalkey and Helmer 1963; for an application to medicine, see Jones and Hunter 1995) applies to cases where the task is to provide a numerical estimate of some variable of interest (say, the risk difference a new treatment makes). Experts answer questionnaires in several rounds. After each round, a facilitator provides an anonymous summary of the experts’ estimates from the previous round and the reasons given for their judgments. Experts are thus supposed to be encouraged to revise their earlier answers in light of other experts’ estimates and justifications. During this process the range of the estimate will often decrease, and it is hoped that the group will converge towards the correct answer. The process is stopped after a pre-determined stopping criterion such as number of rounds, achievement of consensus, stability of results, and an average of the estimates of the final round is used as result.

Solomon (2011, 2015) raises a fundamental issue concerning group judgments that is entirely independent of the specific method used. She argues that we do not often find group judgment methods to determine the truth of scientific hypotheses or estimates of variables in the natural sciences (though see Staley 2004). If there is uncertainty about, say, which of two alternative hypotheses is true or what value a natural constant has, scientists go out and test, experiment, measure. Controversies, in other words, are settled on the basis of evidence, not (individual or group) opinion. Shouldn’t we, with the advancement of evidence-based medicine, expect the same to happen in medicine? Consequently, she recommends more widespread use of mechanical techniques for amalgamating evidence such as meta-analysis in lieu of consensus conferences and the like.

The frequency of NIH consensus conferences has indeed markedly declined in recent years (Solomon 2011, 2015). But this is of course no reason to maintain that group judgments are no longer needed. Consensus conferences may be the wrong tool for the purposes of the NIH, or the NIH may have a mistaken view about the ability of evidence to settle disputes adequately. Indeed, there are at least two reasons to believe that group judgment procedures are here to stay.

The first reason is that, as we have seen above, medical decisions are always in part decisions about normative matters. No treatment is entirely without side effects and so if judgments about efficacy are to be of practical guidance, they must include a weighing of benefit (alleviation of disease symptoms) against cost (suffering from side effects)—even if economic costs and benefits are not to be taken into consideration. Second, government agencies such as the U.S. Food and Drug Administration (FDA) have to decide whether new treatments should be licensed to be marketed. These decisions often have significant consequences, and democracies tend to prefer to be able to hold someone accountable for making them. Drug approval therefore cannot be determined on the basis of evidence according to some mechanical algorithm.

Biddle (2007) discusses epistemological and moral issues of drug approval in the context of a case study on Vioxx, an analgesic. Vioxx was approved by the FDA in 1999 but five years later pulled from the market by its manufacturer Merck due to safety concerns. It is estimated that some 55,000 people died from taking the drug (Harris 2005). Biddle observes that the FDA is not sufficiently independent of the pharmaceutical industry to make unbiased decisions likely. Many of the members of the FDA’s drug approval committees have financial conflicts of interests (often in the form of receiving benefits from the pharmaceutical company whose drug is to be approved), and a large number of employees of the FDA are dependent on the “user fees” industry pays to help cover the cost of drug approval. To solve these problems of conflicts of interests, Biddle proposes to institute an adversarial system in which two groups of advocates, a group of representatives of the manufacturer and a group of independent scientists, would argue before a panel of judges over whether a drug should be allowed on the market. The panel of judges in this model also consists of independent FDA or university scientists. He argues that the adversarial system would better acknowledge the fact that an increasing number of medical researchers have financial ties to the pharmaceutical industry by treating them as advocates rather than disinterested experts. (See also Reiss and Wieten 2015, Reiss forthcoming-b.)

There is no doubt that medical research is shaped by various external values, in ways similar to the value ladeness that is well-recognized in other areas of science (see entry on scientific objectivity ). Many of these values create a variety of ethical dilemmas relating to equity of access to health care and similar. Even in recent years once medical research has been made more inclusive, this trend has introduced a host of additional philosophical and ethical issues (Epstein 2007). For our purposes, we will focus on the implications of the systematic exclusion of certain types of individuals, groups, or diseases from research for future research as well as clinical medical practice in terms of the validity of evidence produced and decisions made based on that evidence.

In traditional medical research, it was generally assumed that white male participants could be used as the basis of generalizations that in turn could be extrapolated to all other populations, including minorities and females (Dresser 1992). Reviews of the literature indicate that women in particular have been excluded (especially older women), and that research on women has usually been related to reproductive function and capacity (Inborn and Whittle 2001). Such types of research have been argued to fail the ideals of quality medical research as well as evidence-based health care (Dodds 2008). Although some improvements have been made in recent years, there remain certain forms of blanket exclusions for instance of women of childbearing age or pregnant women in many types of medical research. These types of systematic exclusion are highly problematic especially because there is clear evidence of critical differences between men and women with regard to a range of factors relating to receptivity to therapies for both biological and social reasons.

In the case of minorities such as African-Americans in the United States, even when research trials seek to recruit them, a range of factors may contribute to them not being involved in medical and other types of research studies. These include distrust due to historical and institutional racism including research performed without consent; lack of understanding about research and consent; social stigma; financial considerations; and lack of culturally-sensitive recruitment methods by researchers (e.g., Huang and Coker 2010). Such gaps in medical research potentially lead to use of treatments or therapies that may in fact be harmful for particular groups, and may result in the withholding of therapies that might be beneficial.

Medical research also is affected by which conditions or diseases are selected for investigation (Reiss and Kitcher 2008): perhaps most notoriously, “orphan” diseases which are either rare, common only in minority populations, or only present in certain developing world or other lower socioeconomic settings, are often neglected for drug and other therapy development because it is perceived that there will not be a viable commercial market for any products on which research might be done due to the at-risk or affected population (and hence such potential products are often termed “orphan drugs”). In some cases patients may pursue “off-label” use of drugs which are approved for a condition other than the one they have, because approval for an “orphan” disease is unlikely due to cost and demand; however such off-label uses of drugs even when overseen by a physician typically result in lack of consistent collection of evidence and absence of typical risk-benefit regulatory considerations utilized when a drug is approved for particular purposes.

A final way in which our knowledge in medicine generated by research is potentially adversely affected by values is through the funding patterns connected with research. As implied above, pharmaceutical companies sponsor a considerable portion of drug trials and have a variety of interests at stake in these investments well beyond the gathering of evidence for the effectiveness (or lack thereof) of a particular product. There is consistent evidence that negative research results typically are suppressed when sponsored by industry (Lexchin 2012a), leading to a bias in what is reported and thus what evidence is available on which to make prescribing and treatment decisions. Bias also has been found in a number of other areas: within the study itself in the choice of research question or topic of investigation, in the choice of doses or drugs against which the drug under study is to be compared, in the control over trial design and various changes in protocols, and in decisions to terminate clinical trials early, and in the reinterpretation of data, as well as in the publication of data such as restrictions on publication rights, use of fake journals, favoring journal supplements and symposia rather than peer review venues, the use of ghostwriting, and in the details of the reporting of results and outcomes (Sismondo 2008; Reiss 2010b; Lexchin 2012b). All of these issues weaken the evidence base on which clinical care judgments are made, and also lead to potentially adverse effects for patients.

In order to evaluate medical outcomes quantitatively, they have to be measured. There are numerous reasons for aiming to quantify medical outcomes. We may want to compare two or more treatments with respect to their efficacy at relieving certain symptoms or their ability to prevent deaths due to a certain disease. When resources are scarce, we may not only want to invest in treatments that are efficacious (that is, they do improve patient morbidity, mortality or both) but also efficient (that is, it is more efficacious than other treatments relative to the cost of procuring it). For matters of international comparison, development and international justice, we also want to have measures of disease burden : Which of a number of tropical diseases has the highest cost in terms of increased morbidity and mortality? For each research dollar spent on treatments for disease X , how much can we expect to reduce the morbidity and mortality it causes?

Clinical trials now often report so-called patient-reported outcome measures or PROMs. A PROM is a questionnaire given to patients to evaluate certain aspects of their quality of life, functioning or health status after a medical intervention without interpretation of the patient’s response by a clinician or other people. It might ask, for example, how difficult patients find it to climb up a flight of stairs after hip surgery or whether or not a cancer treatment helps them to pursue their hobbies. The main goal of a PROM is the assessment of treatment benefit or risk in cases where the medical outcome is best known by the patient or best measured from the patient perspective.

PROMs can vary considerably in length and complexity depending on the concept that is being measured. In simple, straightforward cases (e.g., intensity of a certain kind of pain), a single question might suffice. In others, it may be necessary to address several aspects of a more complex functioning with a number of questions each. Either way, the design of the questionnaire should make sure that the instrument reliably measures the concept of interest. The FDA distinguishes the following six measurement properties or “tests” (FDA 2009: 11):

• Test-retest or intra-interviewer reliability (“Are the scores stable over time when no change is expected in the concept of interest?”)
• Internal consistency (“Is there a high correlation between the responses that purport to measure the same concept?”)
• Inter-interviewer reliability (“Is there agreement among responses when the PROM is administered by two or more different interviewers?”)
• Content validity (“Is there evidence that the instrument measures the concept of interest?”)
• Construct validity (“Is there evidence that the relationships between responses conform to expectations?”)
• Ability to detect change (“Is there evidence that the instrument can identify differences in scores over time in individuals or groups who have changed with respect to the concept of interest?”).

Despite their plausibility, these tests are not methodologically innocuous. Content validity, for instance, is assessed on the basis of qualitative research in the form of patient interviews, focus groups, and qualitative cognitive interviewing (the latter refers to a method that asks respondents to think aloud and describe their thought processes as they answer the instrument questions and involves follow-up questions in a field test interview to gain a better understanding of how patients interpret questions). This qualitative research aims to develop questions with standardized meanings that are shared between patients and clinicians. Arguably, however, there will always be differences in interpreting phrases such as “bodily pain” or “difficulty in lifting one”s arm’ because they refer to a patient’s experiences, and these will differ from patient to patient and, in a given patient, from time to time (Rapkin and Schwartz 2004). Moreover, there may be good philosophical reasons to allow for the expression of a sufficient array of legitimate perspectives on health and quality of life instead of insisting on a standardization of meaning across patients and contexts (McClimans 2010). Similarly, internal consistency can be desirable only to the extent that the concept is a relatively simple one and different questions really do address the same concept. It is of less relevance when the disorder is heterogeneous (McClimans and Browne 2011). These kinds of worries can be raised with respect to each of the measurement tests. Finally, there is an issue when several PROMs that address a given disorder or treatment exist. Different PROMs will score differently with respect to the different tests, and there is no universally valid schema to weigh their relative importance ( ibid .).

Disability-adjusted life years or DALYs aim to measure burden of disease. The measure was originally developed by Harvard University for the World Bank and World Health Organization (WHO) in 1990 and is now widely used by heath policy researchers for comparisons between countries and over time and as a tool for policy making. It can also be used to measure the effectiveness of interventions, though these are usually health policy rather than medical interventions narrowly construed. The WHO makes regular global disease burden estimates in terms of DALYs at regional and global level for more than 135 causes of disease and injury (Mathers et al. 2002).

The principal idea behind DALYs is simple. If a woman in Guatemala dies of Chagas disease at age 63, this adds 20 DALYs to the global disease burden because her death is 20 years “premature” as compared to Japanese life expectancy (which is taken as the standard because it is the highest world wide). If a man in Hamburg has an accident that confines him to the wheelchair for the rest of his life, this contributes 0.57 DALYs for each of his remaining life years because the weight for paraplegia is 0.57. Every kind of disease or impairment is thus given a number between 0 and 1 (where 0 = full health and 1 = death) that makes it comparable to other conditions. For example, blindness has a weight of 0.43. As blindness contributes less to the burden of disease than paraplegia, this means that blindness is regarded as the less severe of the two in terms of its reduction of functional capacity (Prüss-Üstün et al. 2003).

The simple idea is complicated by two adjustments, however. Typical burden of disease studies weigh an impairment differently depending on the age of the person whose functional capacity is impaired by the disease or disability. Blindness, say, has a greater impact on the burden of disease if it occurs at age 20 than if it occurs at very young or older ages (Prüss-Üstün et al. 2003). Moreover, if the man who has the accident now can be expected to live with the disease for 30 years, future years of disability are discounted by a factor. The further into the future a disability occurs, the less it contributes to the disease burden ( ibid .).

The adequacy of any socio-economic indicator has to be evaluated in the light of the purpose that it is meant to serve (Reiss 2008). If DALYs are supposed to measure the everyday concept “burden of disease”, we may criticize, for instance, that the indicator fails to take account of societal, cultural, climatic and other variations within which the disease or disability occurs. Being paraplegic, say, is less burdensome when it occurs in societies that spend more resources on making public buildings and transport wheelchair accessible, that display more tolerance towards the handicapped, or in flatter than in hillier regions. Arguably, therefore, DALYs measure ill-health rather than disease burden (Anand and Hanson 1997). Similarly, because ill-health is measured as a percentage, a disease occurring in a person who is already handicapped contributes less to the measure than the same disease occurring in an otherwise comparable but not handicapped person. If DALYs are used to make public health decisions, however, it might be better to prioritize those individuals who are least well off instead of those who are relatively better off ( ibid .).

The WHO is very explicit that numerous choices made in the construction of the DALY measure are value-based (Murray 1994; Prüss-Üstün et al. 2003). Clearly, there is no matter of fact whether paraplegia constitutes a more severe impairment of someone’s functional capacity than blindness, much less the precise extent to which it contributes to the burden of disease. The same is true of the duration of the time lost due to premature death, age weights and time preference. While any given choice will, due to its value-laden nature, be controversial, the WHO makes some efforts to represent societal preferences instead of, say, a priori philosophical arguments. For example, the disability weights used in the 2003 World Health Survey were based on health state valuations from large representative population samples in over 70 countries (Prüss-Üstün et al. 2003: Ch. 3). Similarly, age weights are based on empirical studies that have indicated there is a broad social preference to value a year lived by a young adult more highly than a year lived by a young child, or lived at older ages (Murray 1996).

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.cls-1{fill:#00a6d4;}.cls-2{fill:#fff;} discoveries magazine Discoveries

The history of medicine can inform the future.

Oct 21, 2022 Sarah Spivack LaRosa

Even if the letters "PhD" or "MD" follow your name, your training in science or medicine may not be complete. Not only is the history of medicine fascinating in its own right, it can uproot assumptions, disrupt bias and point the way to a better future, says medical historian Gideon Manning, PhD . 

" Cedars-Sinai houses the only substantive history of medicine program embedded within an independent academic medical center," says Dr. Manning, director of the History of Medicine Program in the Department of Biomedical Sciences . "We are a research group that in a few short years has become a scholarly destination welcoming speakers and researchers from around the world."

History of medicine is one of many areas in the humanities and the arts that promotes the well-being of someone at work, but also more generally."

– Gideon Manning, PhD

The program was recently accepted into the Consortium for History of Science, Technology and Medicine, a Philadelphia-based organization that brings together a select group of educational, cultural and scientific institutions to promote public understanding of the history of science, technology and medicine. Inclusion is a rare honor and Cedars-Sinai is the only academic hospital to be a member institution.

The History of Medicine Program offers a biweekly speakers’ series that hosts notable historians of medicine and science along with six-week courses focused on topics as varied as the Jewish medical tradition, the history of pathology and the history of women in medicine. Cedars-Sinai also recently launched the Center for Medicine, Holocaust and Genocide Studies under the auspices of the History of Medicine Program. 

"Part of what’s unique about the way we do history of medicine here at Cedars-Sinai is that we are constantly rubbing shoulders with our colleagues on the clinical and research sides to the benefit of all of us," Dr. Manning says.

Why should clinicians or scientists invest in learning about the history of medicine?

Gideon Manning: It’s a good question. But the question assumes something my own experience makes me want to resist. It assumes the practice of medicine is somehow distinct from the history of the field and one’s identity as a physician or professional. Those identities are not static; they have a history, and to assume them in the present is to implicitly endorse their history.

The question also unnecessarily separates the practice of medicine from broader quality of life questions. There’s now an important movement in medical education to recognize real phenomena like burnout and the long-term well-being of medical providers.

The history of medicine is unique among the humanities and the arts in that it simultaneously promotes a deeper understanding of one’s professional identity while also enriching one’s life more generally. It is the history of something uniquely human, namely our struggle to understand and maintain what it is to live a healthy life and have a comfortable death.

Read: Curating the Weird History of Medicine

So the history of medicine shouldn’t be considered a separate topic from medicine itself?

GM: Medicine has long been called an art and a science, though what that means precisely has changed over time. In the last few decades, the emphasis has been so heavily on the science, however, that something of the art has arguably been lost. 

The history of medicine can remind us of the ways in which the science and the art of medicine have evolved and the ways in which both have always been relevant. We can see moments in the past where one or the other haven’t been respected to the detriment of patients and society.

For example, take the current COVID-19 pandemic. Knowledge of the history of epidemics and pandemics, if it was more on the surface of people’s awareness, would have allowed us to anticipate some of what was going to happen and avoid some of the social marketing missteps related to vaccines. 

There were also economic lessons to be taken, as well as awareness of the intersection of health and ethnicity in the presence of new pathogens. Pursuing medicine without the history of medicine raises the risk of blinding us to things we ought to know and are best not relearned the hard way.

Read: What’s It Like to Be a Woman in Medicine?

How can we make sure we don’t lose the lessons we are learning from this pandemic?

GM: We have established a COVID-19 archive to document how our hospital has responded to and been affected by the pandemic. It includes oral histories and a document archive comprising interviews with physicians and staff, describing what their life at the hospital was like before and how it changed.

This will be a scholarly repository for people around the world and an opportunity for Cedars-Sinai to memorialize the efforts of so many, including what worked and how the pandemic was accommodated. Right now we are all pandemic historians, still living this experience, but many of us are trying to move on and over time we will forget. The historians of medicine are making sure the knowledge is not lost. 

Outside of the pandemic, how else can our knowledge of history improve our understanding of medicine today?

GM: There are a lot of things we take for granted concerning the organization of medical knowledge, who should be a physician and who qualifies as being ill. These assumptions have histories and when you look carefully at the emergence of a scientific medical culture, you see it is a human creation that took shape over a long period of time.

Let me offer an analogy. Medicine is like a building that has taken generations to complete though it’s never really done. If you look carefully at such a building, with the right training, you will find evidence of builders’ hesitation or incompetence, and you’ll likely notice some of the parts don’t quite fit together for the simple reason that there were different builders over time with different priorities and interests. 

All to say, studying the history of the building can help us understand and explain why the building looks as it does and why the floors creak and the ceiling leaks. In medicine, history can illuminate why some things don’t work so well today and what might be changed in the future because it needn’t be the way it is. 

Read: My Favorite Innovation

Can you provide an example of how 'builders'' assumptions and interests have influenced the course of medicine in an unfortunate way?

GM: Take the case of sickle cell anemia: we knew all the genetic information we needed to know about the disease even before the human genome project was complete. But research into the condition wasn’t pursued because it predominantly affects people of recent African descent, and they haven’t had a sizable voice in medical research. We need more diversity and inclusion in medical research to fix the problem.

There are long-standing disparities in heart health between men and women. Assumptions about whose voice matters, who gets sick with what disease, or simply who can be a scientist or a physician are at the root of many of these disparities. The history of medicine is, in part, about bringing attention to the contexts in which these assumptions have taken hold, and in so doing directing us to those places where the floors creak and the roof leaks in modern medicine. Historians of medicine, like the rest of us, are out to fix these disparities, we just carry slightly different tools in our toolbox.

Read: Age: A Biological Guide to Longevity

What is your research focus?

GM: I study primarily 15th-18th century medicine, which represents a pivotal moment in the development of our scientific medical culture. At the beginning of the period I study there was no scientific medicine, at least not in our sense, but at the end of the period there surely was.

It was a time of scientific revolutions: the establishment of colleges and medical societies, the appearance of journal publications and books with images of anatomy, the rise of quantification and data analysis. Those advances created our world: journals became the standard for how scientific knowledge is shared, with short readable peer-reviewed research becoming the norm.

The laboratory shaped how science is conducted, with divisions of labor within a hierarchical and specially trained group, creating a perceived separation between scientists, clinicians and the public at large. Counting things became the way data is understood, with consequences still being felt in clinical studies and computational medicine. 

Is there a revelation you’ve experienced as a historian of medicine that you would like to share with others?

GM: One of the projects I’m involved in is a book about the history of death in a broad sense. During a research year in medical school, I came to believe that death is best seen as a historical event in the fullest sense of that term. It’s a biological event, it’s a medical event, it’s an economic event, it’s a cultural event, it’s an artistic event, it’s a social event. It’s even a political event—think of the way governments use the moment of death to change laws and exercise power.

All this is easiest to see when death happens on a massive scale, as during wars or pandemics, but it’s also true on an individual level. I’m exploring what it is to understand death in this broad sense, and how knowledge from all sides could influence patients and doctors alike to have conversations at the end of life.

Who’s teaching in the History of Medicine Program?

The History of Medicine Program offers instruction from a group of faculty members with widely varied training and expertise.

"One of the great strengths of our program is we were each trained at different institutions and have a range of interests," Dr. Manning says. 

Meet the team:

Leon Fine, MD

Research focus: Arts and humanities in medicine.

Latest passion: Origins of gastronomy and food culture in France.

Favorite rare book: Out of the many rare books in his collection, Dr. Fine is particularly fond of Jean-Anthelme Brillat-Savarin’s La Physiologie du Gout (The Physiology of Taste), 1826. This first edition is in its original state.

Kirsten Moore-Sheeley, PhD

Research focus: History of disease control, biomedical science and technology, and global health.

Work in progress: Project about the history of failed and difficult-to-produce vaccines.

Productivity playlist: Dr. Moore-Sheeley vibes out to Marcus Miller and other jazz bassists while she works.

Rena Selya, PhD, MLIS

Research focus: 20th-century American biomedicine, focusing on the history of genetics, molecular biology, cancer research and neuroscience.

Work in progress: Book on Salvador Luria, an Italian Jewish refugee microbiologist who won the 1969 Nobel Prize in Physiology or Medicine for “discoveries concerning the replication mechanism and the genetic structure of viruses.” He was a passionate political activist throughout his life in the United States.  

Cool climber: Dr. Selya recently hiked Exit Glacier in Alaska.

Sari J. Siegel, PhD

Founding director of the Center for Medicine, Holocaust and Genocide Studies

Research focus: Medicine and the Holocaust.

Work in progress: Book on Jewish prisoner-physicians in Nazi camps; contributor to the Lancet Commission on Medicine and the Holocaust: Historical Evidence, Implications for Today, Teaching for Tomorrow

Past passion: Dr. Siegel is a former medical student with a passion for sports medicine.

Gideon Manning, PhD

Research focus: Changing conceptions of human and animal bodies, life, death, health, and nature—along with the emergence of our scientific medical culture.

Work in progress: Papers discussing the history of anatomical illustration, evolving conceptions of surgical success, early bio-mechanical theories in physiology, and a book on the many perspectives from which people experience and understand death.

PhD of pie: During the initial two years of the pandemic, Dr. Manning learned to bake nearly 50 different pie recipes.

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Think It Through: Managing the Benefits and Risks of Medicines

For many people, taking medication is a regular part of their daily routine, and these medicines are relied upon to treat disease and improve health.  Although medicines can make you feel better and help you get well, it's important to know that all medicines, both prescription and over-the-counter, have risks as well as benefits

The benefits of medicines are the helpful effects you get when you use them, such as lowering blood pressure, curing infection, or relieving pain. The risks of medicines are the chances that something unwanted or unexpected could happen to you when you use them. Risks could be less serious things, such as an upset stomach, or more serious things, such as liver damage. Here are some tips from the Food and Drug Administration and some of its public health partners to help you weigh the risks and benefits when you make decisions about the medicines you use.

Managing Risk

When a medicine's benefits outweigh its known risks, the FDA considers it safe enough to approve. But before using any medicine--as with many things that you do every day--you should think through the benefits and the risks in order to make the best choice for you.

There are several types of risks from medicine use:

• The possibility of a harmful interaction between the medicine and a food, beverage, dietary supplement (including vitamins and herbals), or another medicine. Combinations of any of these products could increase the chance that there may be interactions.
• The chance that the medicine may not work as expected.
• The possibility that the medicine may cause additional problems.

For example, every time you get into a car, there are risks. You could have an accident, causing costly damage to your car, or injury to yourself or a loved one. But there are also benefits to riding in a car: You can travel farther and faster than walking, bring home more groceries from the store, and travel in cold or wet weather in greater comfort.

To obtain the benefits of riding in a car, you think through the risks. You consider the condition of your car and the road, for instance, before deciding to make that trip to the store.

The same is true before using any medicine. Every choice to take a medicine involves thinking through the helpful effects as well as the possible unwanted effects.

Here are some specific ways to lower the risks and obtain the full benefits of medicines:

Talk With Your Doctor, Pharmacist, or Other Health Care Professionals

• Keep an up-to-date, written list of all the medicines (prescription and over-the-counter) and dietary supplements, including vitamins and herbals, that you use--even those you only use occasionally.
• Share this list with all of your health care professionals.
• Tell them about any allergies or sensitivities that you may have.
• Tell them about anything that could affect your ability to take medicines, such as difficulty swallowing or remembering to take them.
• Tell them if you are or might become pregnant, or if you are nursing a baby.
• Always ask your health care professional questions about any concerns or thoughts that you may have.

Know Your Medicines--Prescription and Over-the-Counter

• the brand and generic names
• what they look like
• how to store them properly
• when, how, and how long to use them
• how and under what conditions you should stop using them
• what to do if you miss a dose
• what they are supposed to do and when to expect results
• side effects and interactions
• whether you need any tests or monitoring
• always ask for written information to take with you.

Read the Label and Follow Directions

• Make sure you understand the directions; ask if you have questions or concerns.
• Always double-check that you have the right medicine.
• Keep medicines in their original labeled containers, whenever possible.
• Never combine different medicines in the same bottle.
• Read and follow the directions on the label and the directions from your doctor, pharmacist, or other health care professional. If you stop the medicine or want to use the medicine differently than directed, consult with your health care professional.

Avoid Interactions

• Ask whether there are interactions with any other medicines or dietary supplements (including vitamins or herbal supplements), beverages, or foods.
• Use the same pharmacy for all of your medicine needs, whenever possible.
• Before starting any new medicine or dietary supplement (including vitamins or herbal supplements), ask again whether there are possible interactions with what you are currently using.

Monitor Your Medicines' Effects--And the Effects of Other Products That You Use

• Ask whether there is anything you can do to minimize side effects, such as eating before you take a medicine to reduce stomach upset.
• Pay attention to how you are feeling; note any changes. Write down the changes so that you can remember to tell your doctor, pharmacist, or other health care professional.
• Know what to do if you experience side effects and when to notify your doctor.
• Know when you should notice an improvement and when to report back.

Weighing the Risks, Making the Choice

The benefit-risk decision is sometimes difficult to make. The best choice depends on your particular situation.

You must decide what risks you can and will accept in order to get the benefits you want. For example, if facing a life-threatening illness, you might choose to accept more risk in the hope of getting the benefits of a cure or living a longer life. On the other hand, if you are facing a minor illness, you might decide that you want to take very little risk. In many situations, the expert advice of your doctor, pharmacist, or other health care professionals can help you make the decision.

Essay on Health for Students and Children

500+ words essay on health.

Essay on Health: Health was earlier said to be the ability of the body functioning well. However, as time evolved, the definition of health also evolved. It cannot be stressed enough that health is the primary thing after which everything else follows. When you maintain good health , everything else falls into place.

Similarly, maintaining good health is dependent on a lot of factors. It ranges from the air you breathe to the type of people you choose to spend your time with. Health has a lot of components that carry equal importance. If even one of them is missing, a person cannot be completely healthy.

Constituents of Good Health

First, we have our physical health. This means being fit physically and in the absence of any kind of disease or illness . When you have good physical health, you will have a longer life span. One may maintain their physical health by having a balanced diet . Do not miss out on the essential nutrients; take each of them in appropriate quantities.

Secondly, you must exercise daily. It may be for ten minutes only but never miss it. It will help your body maintain physical fitness. Moreover, do not consume junk food all the time. Do not smoke or drink as it has serious harmful consequences. Lastly, try to take adequate sleep regularly instead of using your phone.

Next, we talk about our mental health . Mental health refers to the psychological and emotional well-being of a person. The mental health of a person impacts their feelings and way of handling situations. We must maintain our mental health by being positive and meditating.

Subsequently, social health and cognitive health are equally important for the overall well-being of a person. A person can maintain their social health when they effectively communicate well with others. Moreover, when a person us friendly and attends social gatherings, he will definitely have good social health. Similarly, our cognitive health refers to performing mental processes effectively. To do that well, one must always eat healthily and play brain games like Chess, puzzles and more to sharpen the brain.

Get the huge list of more than 500 Essay Topics and Ideas

Physical Health Alone is Not Everything

There is this stigma that surrounds mental health. People do not take mental illnesses seriously. To be completely fit, one must also be mentally fit. When people completely discredit mental illnesses, it creates a negative impact.

For instance, you never tell a person with cancer to get over it and that it’s all in their head in comparison to someone dealing with depression . Similarly, we should treat mental health the same as physical health.

Parents always take care of their children’s physical needs. They feed them with nutritious foods and always dress up their wounds immediately. However, they fail to notice the deteriorating mental health of their child. Mostly so, because they do not give it that much importance. It is due to a lack of awareness amongst people. Even amongst adults, you never know what a person is going through mentally.

Thus, we need to be able to recognize the signs of mental illnesses . A laughing person does not equal a happy person. We must not consider mental illnesses as a taboo and give it the attention it deserves to save people’s lives.

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History of Medicine between tradition and modernity

History of medicine is an extensive and very complex science. In a simple and classical understanding, it has an informative and associative role. Although it is not easy for students to understand the multiple implications of the history of medicine, its importance becomes more evident during their academic formation. The students must be persuaded particularly about the ethical and cultural values that history of medicine has in their training. Furthermore, history of medicine participates in creating the necessary perspective for shaping the future of medicine in the next decades. This is, perhaps, the most interesting role that the history of medicine should play from the modern point of view of students and young physicians. This paper presents different ways of understanding the roles of the history of medicine regarded from the traditional perspective to the contemporary point of view.

Introduction

History of medicine is an extensive and a very complex science, with many interesting and even fascinating aspects, which should be studied carefully and with no partisan bias. This paper is a plea for studying history of medicine in the higher medical education.

As 2017 marks 125 years since Valeriu Lucian Bologa (1892–1971) – the first Romanian professor of history of medicine – was born, we bring thus a homage to his memory.

We publish this paper in “Clujul Medical” journal, because Bologa was professor and head of Department of the History of Medicine at the Cluj Faculty of Medicine for more than three decades (1930–1962) and also a member of the editorial board of this publication.

The history of medicine between positive and negative understanding

The evolution of medicine has interested many historians of medicine in the past and new arguments continue to be brought about the need of its study [ 1 ].

It is necessary to show that the formative role of the history of medicine has been discussed since the second half of the nineteenth century. After a century, emphasizing the significance of the history of medicine in the training of the future doctors, V. L. Bologa evidenced several objectives: to give to physician the possibility to refresh and enlarge his general culture; to focus his attention on one of the most beautiful chapters from the history of civilization and to promote respect for the past of medicine for its outstanding protagonists [ 2 ].

However, it is strange to observe that the interest of some students is not sufficiently developed for learning the history of medicine. There could be different reasons for this situation.

One reason is the fact that the present time has its focus on “what is” – the immediate present – and “what is to be” – the future. In this context, Farokh Erach Udwadia put the question “it is therefore worthwhile to give the reader a glimpse of the recent past?” His answer is significant: “I do believe so, for the past in any field of endeavour permeates the present and lies buried within the future” [ 3 ]. Referring to history of medicine, he added: “to gain a proper perspective, the never-ending canvas of medicine is best viewed in its entirety – the past, the present, the changing unfinished future” [ 3 ].

The misunderstanding regarding the formative role of history of medicine for students can be explained in another way. The period of accelerated progress involves the appearance of many professional notions, new conceptions etc. Their consequence is the need to introduce new topics or types of lectures in the academic curricula. Implicitly, they lead to a compression of classical subjects of study, although they could be important for the professional training of students or for their general culture.

Referring directly to the history of medicine, the study of the past of medicine permits a better understanding of its present and gives the possibility to do develop strategies for its future.

Studying history of medicine, students learn how to understand and to think different medical events from various perspectives: how to correlate various medical profiles apparently without connection with each other, or how the same discovery may occur several times at intervals of centuries and without continuity in time. For example, students can understand how the important anatomist Giovanni Battista Morgagni (1682–1771) can be regarded as the father of modern pathology. Another significant example is that students learn that the cataract surgery – which is considered an operation specific for modern times – was practiced in antiquity and mentioned by Aulus Cornelius Celsus (c. 25 BC – c. 50 AD) [ 4 ] and later, in the Middle Ages, by Abulcasis (936–1013). Learning the history of medicine, students reach a certain level of understanding, like how it was possible that Galen’s influence on European medicine lasted nearly fifteen centuries after his death.

The correct analysis of the past of medical science allows us to understand not only the progressive phases of medicine, but also the periods of stagnation or regression. This is a significant advantage, because knowing the negative experiences of the past, future errors can be avoided.

An interesting point of view was discussed by Jacalyn Duffin (b. 1950): the history of medicine offers a “conceptual tool for learning about medicine”. She added: “medical students are intelligent. Even if they last studied humanities in high school, they soon grasp the thrill and an adventure of a debate over questions and context. In reaching for this modest goal, students learn something about the past; however, they can select the events that seem more relevant for their own personal lives and career goal” [ 5 ].

Why should students memorize different names and data from the past of medicine? The effort to memorize is useful, because it will help students to learn easier some diseases and syndromes having proper names. Certainly, not all historical data have the same significance. It is more useful to remember the century or the historical period in which different personalities lived, rather than their years of birth and death. Also, not all titles of books they wrote are important to be kept in mind, but only those that marked the progress of medicine. For example, is very useful to memorize the title “De humani corporis fabrica” of Andreas Vesalius (sixteen century), because it marked a turning point in the evolution of anatomy.

Although very few, there are students who consider history as a boring and unimportant subject. This is due to the fact that they are not convinced by what means history. This is a consequence that during school years, history is presented in a thematic approach. Thus, it is difficult for future students to understand that the correct study of the history is “the past of mankind since ancient times till today, according to the specifics of geographic areas and of communities” [ 6 ]. Ioan-Aurel Pop (b. 1955) shows that: “the facts of the past, removed from space and time have no historical relevance. Being dispersed, they serve the political discourse, the writer, the musician, filmmaker, essayist, philosopher, etc., but these are not history” [ 6 ].

How medical history should be presented in order to be clearly understood?

To teach the history of medicine is a great responsibility, being necessary to analyze every medico-historical aspect in various ethical, socio-economic, cultural etc. perspectives. As Giorgio Zanchin (b. 1945) puts into evidence: “if history is understood as a succession of events determined by specific causes, with specific consequences that vary according to social, economic, and political conditions, a historical analysis is essential for a dynamic interpretation of scientific theories in a social-cultural context of reference” [ 7 ].

In a book exploring the continuities and discontinuities in medical thought and practice, Keir Waddington shows that “this approach encouraged readers to think about how medicine has been used to fashion and refashion views of the body and disease; how it informed access to healthcare and welfare policies; and how this was related to different political, cultural, intellectual and socioeconomic contexts” [ 8 ]. About his volume entitled “An Introduction to the Social History of Medicine” he noted that it “focuses not on individuals, institutions or discoveries, but on a comparative examination of key theme in the social history in Europe” [ 8 ].

There is also the approach of history of medicine in terms of the conditions in which the discoveries were made. Michael T. Kennedy (b. 1938) noted in the introduction of his book entitled “A brief history of disease, science and medicine” that much of what medical students learned from the past has now been shown to be in error. For that reason, his concept of history of medicine includes other subjects than those in a «classical» account. Thus, he gave explanations about his interest “in how infectious diseases evolved and [I] think it important to understand this aspect of science to make sense of the story of smallpox in the New World and syphilis in the old” [ 9 ].

Regarding the history of medicine presented in essays we consider that it can be correctly understood only by those who have a solid knowledge of history. For example, this type of approach can be used with certain intentions, as Olivier Faure (b. 1953) did, gathering his articles previously published in various journals. However, this approach is limited only for shorter periods of time and is focused on some social aspects of medicine. Faure used a certain style of presentation, as he noted: “an absence of mastery of academic codes or contempt for them, this propensity to direct language is the sign of the enthusiasm and passion with which I have always approached the subjects I have dealt with” [ 10 ].

A book of history of medicine that aims to include more subjects, such as the evolution of different medical discoveries, the evolution of techniques and medical innovations, controversies in medicine etc. is difficult to be elaborated and published in only one comprehensive volume. It should be written by several authors. This gives the authors the responsibility, but also the opportunity to approach the problems in their own way. This multiple approach can have, as result, a book which is it not unitarily written. Moreover, this type of book exceeds the requirements of medical students in higher education. Furthermore it could give rise to heated debates on issues related to these thematic problems [ 11 ].

Discussing the importance of the scientific research in history of medicine, John L. Thorthon reveals that “The history of medicine has been studied for centuries, but remains a fluid subject. Fresh facts can reveal new fields of research, and even result in a re-evaluation of the subject. A misinterpretation may have led to false assumptions which in turn have misled later writers, resulting in errors which have been perpetuated for centuries. Only comparatively recently have professional medical historians, armed with an appreciation of both medical knowledge and a background of social history, attempted to unravel the intricacies of the development of medical progress” [ 12 ].

At the end of our paper, we consider adequate to remember some ideas of Nicolae Vătămanu (1897–1977) and Gheorghe Brătescu (b. 1923): “knowing the past of this exciting science [history of medicine] is meant to attract alike the young man who strives to embrace the medical profession, and the one that deepens it with passion: it [history of medicine] is useful for the physician who needs a [...] quick and safe orientation, as for the inexperienced scholar, sensitive to all what is noble, profound and useful in human activity” [ 13 ].

Conclusions

• The study of the past of medicine permits a better understanding of its present and gives the possibility to develop adequate strategies for its future.
• The study of the history of medicine offers the students the possibility to correlate various medical profiles seemingly without connection with each other.
• To teach history of medicine is a great responsibility, being necessary to analyze medico-historical aspects in various ethical, socio-economic and cultural perspectives.
• There are different ways of understanding the roles of the history of medicine regarded from the traditional perspective to the contemporary point of view.
• Fresh medico-historical facts can reveal new fields of research, and even a re-evaluation of the same subject.

Writing a strong scientific paper in medicine and the biomedical sciences: a checklist and recommendations for early career researchers

• Open access
• Published: 28 July 2021
• Volume 72 , pages 395–407, ( 2021 )

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• Payam Behzadi 1 &
• Márió Gajdács   ORCID: orcid.org/0000-0003-1270-0365 2 , 3  

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Scientific writing is an important skill in both academia and clinical practice. The skills for writing a strong scientific paper are necessary for researchers (comprising academic staff and health-care professionals). The process of a scientific research will be completed by reporting the obtained results in the form of a strong scholarly publication. Therefore, an insufficiency in scientific writing skills may lead to consequential rejections. This feature results in undesirable impact for their academic careers, promotions and credits. Although there are different types of papers, the original article is normally the outcome of experimental/epidemiological research. On the one hand, scientific writing is part of the curricula for many medical programs. On the other hand, not every physician may have adequate knowledge on formulating research results for publication adequately. Hence, the present review aimed to introduce the details of creating a strong original article for publication (especially for novice or early career researchers).

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Avoid common mistakes on your manuscript.

Introduction

The writing and editing of scientific papers should be done in parallel with the collection and analysis of epidemiological data or during the performance of laboratory experiments, as it is an integral step of practical research. Indeed, a scholar paper is the figurative product of scientific investigations (Behzadi and Behzadi 2011 ; Singh and Mayer 2014 ). Moreover, the publication of scholarly papers is important from the standpoint of providing relevant information—both locally and internationally—that may influence clinical practice, while in academia, national and international academic metrics (in which the number and quality of papers determine the score and rank of the scientists) are relevant to fulfill employment criteria and to apply for scientific grants (Grech and Cuschieri 2018 ; Singer and Hollander 2009 ). Thus, scientific writing and the publication of quality peer-reviewed papers in prestigious academic journals are an important challenge for medical professionals and biomedical scientists (Ahlstrom 2017 ). Writing a strong scholarly paper is a multi-procedure task, which may be achieved in a right manner by using a balanced and well-designed framework or blueprint (Gemayel 2016 ; Tóth et al. 2020 ). All in all, time needs to be spent of writing a well-designed and thoughtful scientific proposal to support the research, which will subsequently end in the publication of a paper in a prestigious, peer-reviewed, indexed and scholarly journal with an impact factor (IF). A well-designed scientific project encompasses well-supported and strong hypotheses and up-to-date methodology, which may lead to the collection of remarkable (and reproducible!) data. When a study is based on a strong hypothesis, suitable methodology and our studies result in usable data, the next step is the analysis and interpretation of the said data to present a valuable conclusion at the end of our studies. These criteria give you an influent confidence to prepare a robust and prestigious scholarly paper (Ahlstrom 2017 ; Behzadi 2021 ; Kallet 2004 ; Stenson et al. 2019 ). The aim of this review is to highlight all the necessary details for publication of a strong scientific writing of original article, which may especially be useful for novice or early career researchers.

Approaches for writing and formatting manuscripts before submission

In the presence of effective and appropriate items for writing a strong scientific paper, the author must know the key points and the main core of the study. Thus, preparing a blueprint for the paper will be much easier. The blueprint enables you to draft your work in a logical order (Gemayel 2016 ). In this regard, employment of a mass of charge, free or pay-per-use online and offline software tools can be particularly useful (Gemayel 2016 ; Behzadi and Gajdács 2020 ; Behzadi et al. 2021 ; Ebrahim 2018 ; Issakhanian and Behzadi 2019 ; O'Connor and Holmquist 2009 ; Petkau et al. 2012 ; Singh and Mayer 2014 ; Tomasello et al. 2020 ). Today, there are a wide range of diverse software tools which can be used for design and organization of different parts of your manuscript in the correct form and order. Although traditionally, many scientist do not use these softwares to help formulate their paper and deliver their message in the manuscript, they can indeed facilitate some stages of the manuscript preparation process. Some of these online and offline software facilities are shown in Table 1 .

The first step of writing any scientific manuscript is the writing of the first draft. When writing the first draft, the authors do not need to push themselves to write it in it’s determined order (Behzadi and Gajdács 2020 ; Gemayel 2016 ); however, the finalized manuscript should be organized and structured, according to the publisher’s expectations (Berman et al. 2000 ; Behzadi et al. 2016 ). Based on the contents of the manuscripts, there are different types of papers including original articles, review articles, systematic reviews, short communications, case reports, comments and letters to the editor (Behzadi and Gajdács 2020 ; Gemayel 2016 ), but the present paper will only focus on the original articles structured in the IMRAD (Introduction, Methods, Results and Discussion) structure. Materials and methods, results, discussion or introduction sections are all suitable target sections to begin writing the primary draft of the manuscript, although in most cases, the methods section is the one written first, as authors already have a clear sense and grasp on the methodologies utilized during their studies (Ebrahim 2018 ). The final sections of IMRAD papers which should be completed are the abstract (which is basically the mini-version of the paper) and conclusion (Liumbruno et al. 2013 ; Paróczai et al. 2021 ; Ranjbar et al. 2016 ). The authors should be aware that the final draft of the manuscript should clearly express: the reason of performing the study, the individuality (novelty and uniqueness) of the work, the methodology of the study, the specific outcomes examined in this work, the importance, meaning and worth of the study. The lack of any of the items in the manuscript will usually lead to the direct rejection of the manuscript from the journals. During the composition of the manuscript (which corresponds to any and all sections of the IMRAD), some basics of scientific writing should be taken into consideration: scientific language is characterized by short, crisp sentences, as the goal of the publication is to deliver the main message concisely and without confusion. It is a common misconception that scientific writing needs to be “colorful” and “artistic,” which may have the opposite effect on the clarity of the message. As the main goal of publishing is to deliver the message (i.e., the results) of our study, it is preferred that scientific or technical terms (once defined) are used uniformly, with avoiding synonyms. If young scientists have linguistic difficulties (i.e., English is not their first language), it is desirable to seek the help of professional proofreading services to ensure the correct grammar use and clarity. Traditionally, the passive voice was expected to be used in scientific communication, which was intended to strengthen the sense of generalization and universality of research; however, nowadays the active voice is preferred (symbolizing that authors take ownership and accountability of their work) and sentences in passive voice should take up < 10% of the paper (Berman et al. 2000 ; Behzadi et al. 2016 ).

Every scientist should be able to present and discuss their results in their own words, without copy–pasting sentences from other scientists or without referring to the work of others, if it was used in our paper. If an author copies or represents another authors’ intellectual property or words as their own (accidentally or more commonly on purpose) is called plagiarism. Scientific journals use plagiarism checker softwares to cross-check the level of similarity between the submitted works and scientific papers or other materials already published; over a certain threshold of similarity, journals take action to address this issue. Plagiarism is highly unethical and frowned upon in the scientific community, and it is strictly forbidden by all relevant scientific publishers, and if one is caught with plagiarism, the scientific paper is usually rejected immediately (if this occurs during the submission process) or is retracted. There are some freely available online software tools (e.g., iThenticate® ( http://www.ithenticate.com/ ) and SMALL SEO TOOLS ( https://smallseotoolz.net/plagiarism-checker ) for authors to screen their works for similarities with other sources; nevertheless, it is also unethical to use these tools to determine the “acceptable” level of similarity (i.e., cheating) before submitting a paper.

The structure of an IMRAD article includes the title, author’s(s’) name(s), author’s(s’) affiliation(s), author’s(s’) ORCID iD(s) ( https://orcid.org/ ), abstract, keywords, introduction, methods (or materials and methods), results, discussion, conclusion, acknowledgements, conflict of interest and references (Behzadi and Behzadi 2011 ; Singh and Mayer 2014 ). The acronym of ORCID (with a hard pronunciation of C ( https://orcid.org/blog/2013/01/07/how-should-orcid-be-pronounced )) (abbreviation of Open Researcher & Contributor ID) is considered as unique international identifier for researchers (Haak et al. 2012 ; Hoogenboom and Manske 2012 ). The ORCID iD is composed of 16 digits and introduced in the format of https URI ( https://support.orcid.org/hc/en-us/articles/360006897674 ). It is recommended for the authors to register their ORCID iD. The ORCID is important for manuscript submissions, manuscript citations, looking at the works of other researchers among other things (Haak et al. 2012 ; Hoogenboom and Manske 2012 ).

The contents of the IMRAD-structured manuscripts

Although the IMRAD format seems to be a cul-de-sac structure, it can be a suitable mold for both beginners and professional writers and authors. Each manuscript should contain a title page which includes the main and running (shortened) titles, authors’ names, authors’ affiliations (such as research place, e-mail, and academic degree), authors’ ORCID iDs, fund and financial supports (if any), conflicts of interest, corresponding author’s(s’) information, manuscript’s word count and number of figures, tables and graphs (Behzadi and Gajdács 2020 ).

As the title is the first section of your paper which is seen by the readers, it is important for the authors to take time on appropriately formulating it. The nature of title may attract or dismiss the readers (Tullu and Karande 2017 ). In this regard, a title should be the mirror of the paper’s content; hence, a proper title should be attractive, tempting, specific, relevant, simple, readable, clear, brief, concise and comprehensive. Avoid jargons, acronyms, opinions and the introduction of bias . Short and single-sentenced titles have a “magic power” on the readers. Additionally, the use of important and influent keywords could affect the readers and could be easy searchable by the search engines (Cuschieri et al. 2019 ). This can help to increase the citation of a paper. Due to this fact, it is recommended to consider a number of titles for your manuscript and finally select the most appropriate one, which reflects the contents of the paper the best. The number of titles’ and running titles’ characters is limited in a wide range of journals (Cuschieri et al. 2019 ).

The abstract is the vitrine of a manuscript, which should be sequential, arranged, structured and summarized with great effort and special care. This section is the second most important part of a manuscript after title (Behzadi and Gajdács 2020 ). The abstract should be written very carefully, deliberately and comprehensively in perfect English, because a well-written abstract invites the readers (the editors, reviewers, and readers who may cite the paper in the future) to read the paper entirely from A to Z and a rough one discourages readers (the editors and reviewers) from even handling the manuscript (Cuschieri et al. 2019 ). Whether we like it or not, the abstract is the only part of the manuscript that will be read for the most part; thus, the authors should make an effort to show the impressiveness and quality of the paper in this section.

The abstract as an independent structured section of a manuscript stands alone and is the appetizer of your work (Jirge 2017 ). So as mentioned, this part of paper should be written accurately, briefly, clearly, and to be facile and informative. For this section, the word count is often limited (150 to 250/300 words) and includes a format of introduction/background/, aim/goal/objective, methods, results and conclusions. The introduction or background refers to primary observations and the importance of the work, goal/aim/objective should represent the hypothesis of the study (i.e., why did you do what you did?), the methods should cover the experimental procedures (how did you do what you did?), the results should consider the significant and original findings, and finally, the clear message should be reported as the conclusion. It is recommended to use verbs in third person (unless specified by the Journal’s instructions). Moreover, the verbs depicting the facts which already have been recognized should be used in present tense while those verbs describing the outcomes gained by the current work should be used in past tense. For beginners in scientific publishing, it is a common mistake to start the writing of the manuscript with the abstract (which—in fact—should be the finalizing step, after the full text of the paper has already been finished and revised). In fact, abstract ideally is the copy-pasted version of the main messages of the manuscript, until the word limit (defined by the journal) has been reached. Another common mistake by inexperienced authors is forgetting to include/integrate changes in the abstract to reflect the amendments made in the bulk text of the paper. All in all, even a paper with very good contents and significant results may could be rejected because of a poor and weak abstract (Behzadi and Gajdács 2020 ).

Keywords are the key point words and terms of the manuscript which come right after abstract section. The keywords are used for searching papers in the related fields by internet search engines. It is recommended to employ 3 to 10 keywords in this section. The keywords should be selected from the MeSH (Medical Subject Headings) service, NCBI ( https://www.ncbi.nlm.nih.gov/mesh/ ). An appropriate title should involve the most number of keywords (Behzadi and Gajdács 2020 ; Jirge 2017 ).

Introduction section should be framed up to four paragraphs (up to 15% of the paper’s content). This section should be progressed gradually from general to specific information and gaps (in a funnel-formed fashion). In another words, the current condition of the problem and the previous studies should be briefly presented in the first paragraph. More explanation should be brought in discussion section, where the results of the paper should be discussed in light of the other findings in the literature (Ahlstrom 2017 ; Behzadi 2021 ). In this regard, the original articles and some key references should be cited to have a clarified description. The second paragraph should clarify the lack of knowledge regarding the problem at present, the current status of the scientific issue and explain shortly the necessity and the importance of the present investigation. Subsequently, the relevance of this work should be described to fill the current gaps relating to the problem. The questions (hypothesis/purpose) of the study comprising “Why did you do?/What did you do?/So What?” should be clarified as the main goal in the last paragraph (Ahlstrom 2017 ; Behzadi 2021 ; Burian et al. 2010 ; Lilleyman 1995 ; Tahaei et al. 2021 ). A concise and focused introduction lets the readers to have an influent understanding and evaluation for the performance of the study. The importance of the work presented should never be exaggerated, if the readers feel that they have been misled in some form that may damage the credibility of the authors’ reputation. It is recommended to use standard abbreviations in this section by writing the complete word, expression or phrase for the first time and mentioning the related abbreviation within parenthesis in this section. Obviously, the abbreviations will be used in the following sentences throughout the manuscript. The authors should also adhere to international conventions related to writing certain concepts, e.g., taxonomic names or chemical formulas. In brief, the introduction section contains four key points including: previous studies, importance of the subject, the presence of serious gap(s) in current knowledge regarding the subject, the hypothesis of the work (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ). Previously, it was recommended by majority of journals to use verbs in past tense and their passive forms; however, this shows a changing trend, as more and more journals recommend the use of the active voice.

Materials and methods

As the materials and methods section constitutes the skeleton of a paper (being indicative of the quality of the data), this section is known as the keystone of the research. A poor, flawed or incorrect methodology may result in the direct rejection of manuscripts, especially in high IF journals, because it cannot link the introduction section into the results section (Haralambides 2018 ; Meo 2018 ). In other words, the methods are used to test the study’s hypothesis and the readers judge the validity of a research by the released information in this section. This part of manuscript belongs to specialists and researchers; thus, the application of subheadings in a determined and relevant manner will support the readers to follow information in a right order at the earliest. The presentation of the methodologies in a correct and logical order in this section clarifies the direction of the methods used, which can be useful for those who want to replicate these procedures (Haralambides 2018 ; Juhász et al. 2021 ; Meo 2018 ). An effective, accurate, comprehensive and sufficient description guarantees the clarity and transparency of the work and satisfies the skeptical reviewers and readers regarding the basis of the research. The following questions should be answered in this section: “What was done?” and “How was it done?” and “Why was it done?”

The cornerstones of the methods section including defining the type of study, materials (e.g., concentration, dose, generic and manufacturer names of chemicals, antibiotics), participants (e.g., humans, animals, microorganisms), demographic data (e.g., age, gender, race, time, duration, place), the need for and the existence of an ethical approval or waiver (in accordance with the Declaration of Helsinki and its revisions) for humans and animals, experimental designs (e.g., sampling methods, time and duration of the study, place), protocols, procedures, rationale, criteria, devices/tools/techniques (together with their manufacturers and country of origin), calibration plots, measurement parameters, calculations, statistical methods, tests and analyses, statistical software tools and version among many other things should be described here in methods section (Haralambides 2016 ; Stájer et al. 2020 ). If the details of protocols make this section extremely long, mention them in brief and cite the related papers (if they are already published). If the applied protocol was modified by the researcher, the protocol should be mentioned as modified protocol with the related address. Moreover, it is recommended to use flow charts (preferably standard flow charts) and tables to shorten this section, because “a picture paints a thousand words” (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ).

The used online guidelines in accordance with the type of study should be mentioned in the methods section. In this regard, some of these online check lists, including the CONSORT (Consolidated Standards of Reporting Trials) statement ( http://www.consort-statement.org/ ) (to improve the reporting randomized trials), the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement ( http://www.prisma-statement.org/ ) (to improve the reporting of systematic reviews and meta-analyses), the STARD (Standards for Reporting Diagnostic accuracy studies) statement ( http://www.equator-network.org/wp-content/uploads/2015/03/STARD-2015-checklist.pdf ) (to improve the reporting of diagnostic accuracy studies), the STORBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement ( https://www.strobe-statement.org/index.php?id=strobe-home ) (to improve the reporting of observational studies in Epidemiology), should be mentioned and highlighted in medical articles. Normally, the methods section begins with mentioning of exclusion (depicting safe selection) and inclusion (depicting no bias has happened) criteria (regarding the populations studied) and continues by the description of procedures and data collection. This section usually ends by the description of statistical data analyses. As mentioned in a previous section, older recommendations in “Instructions for authors” suggested the use of verbs in past tense, in 3rd person and passive forms, whereas novel guidelines suggest more text written in the active voice (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ).

The results including negative and positive outcomes should be reported clearly in this section with no interpretation (Audisio et al. 2009 ; Behzadi et al. 2013 ). The most original information of an IMRAD paper originates from the results section. Indeed, the reported findings are the main core of the study which answers to the research question (hypothesis) “what was found?” The results section should answer all points brought up in the methods section. Categorization of findings by subheadings from the major to minor results, chronologically or by any logical order, facilitates readers to comprehend the results in an effective and influent manner (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ).

Representing the motive of experiments, the related experimental setups, and the gained outcomes supports the quality and clarity of your results, because these components create logical and influent communications between obtained data, observations and measurements. The results section should represent all types of data (major to minor), variables (dependent and independent), variables effects and even accidental findings. The statistical analyses should be represented at the end of results section. The statistical significance should be represented by an exact amount of p value ( p  < 0.05 is usually recognized and set as the threshold for statistical significance, while p  > 0.05 depicts no statistical significance). Moreover, the mentioning of the 95% confidence intervals and related statistical parameters is also needed, especially in epidemiological studies (Mišak et al. 2005 ).

It is recommended to use tables, figures, graphs and charts in this section to give an influent representation of results to the readers. Using well-structured tables deeply impresses the readers. Usually the limitation of the number of figures, graphs, tables and charts is represented in the section of instructions for authors of the journal. Remember that well-designed tables and figures act as clean mirrors which transfer a clear and sharp illustration of your work and your efforts in preparing the manuscript. Thus, a well-designed graph, table, charts or figure should be understood easily; in other words, they should be represented as self-explanatory compartments. Avoid repeating the represented data in figures, tables, charts and graphs within the text. Citing figures, graphs, charts and tables in right positions within the text increases the impact and quality of your manuscript (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ). Showing the highest and lowest amounts in tables by bolding or highlighting them is very effective. Normally, the legends are placed under graphs and figures and above the tables. It is recommended to begin the figure legends with conclusion and finish it by important technical key points.

Discussion and conclusion

This section represents the interpretations of results. In other words, discussion describes what these results do mean by the help of mechanistic interpretations of causes and effects. This argument should be achieved sharp and strong in a logical manner (Gajdács 2020 ; Rasko et al. 2016 ). The interpretations should be supported by relevant references and evidences. Usually, the first paragraph of discussion involves the key points of results. The represented data in results section should not be repeated within the discussion section. Magnification and exaggeration of data should never occur! “A good wine needs no bush.” Care about the quality of discussion section, because this part of the manuscript is determinative item for the acceptance of the paper (Ahlstrom 2017 ; Behzadi 2021 ).

Avoid representing new data in discussion, which were not mentioned in the results section. The following paragraphs should represent the novelty, differences and/or similarities of the obtained findings. Unusual and findings not predicted should be highlighted (Gajdács 2020 ; Rasko et al. 2016 ). It is important to interpret the obtained results by the strong references and evidences. Remember that citation of strong and relevant references enforces your evaluations and increases the quality of your points of view (Mack 2018 ; Shakeel et al. 2021 ). The probable weaknesses or strengths of the project should be discussed. This critical view of the results supports the discussion of the manuscript. The discussion section is finished by the final paragraph of conclusion. A critical paragraph in which the potential significance of obtained findings should be represented in brief (Ahlstrom 2017 ; Behzadi 2021 ). The bring/take-home message of the study in conclusion section should be highlighted. For writing a conclusion, it is recommended to use non-technical language in perfect English as it should be done in abstract section (Alexandrov 2004 ). It is suggested to use verbs in present tense and passive forms, if not otherwise mandated by the journal’s instructions. In accordance with policy of journals, the conclusion section could be the last part of discussion or presented within a separate section after discussion section (Ahlstrom 2017 ; Behzadi 2021 ).

Acknowledgements

This section is placed right after discussion and/or conclusion section. The unsaid contributors with pale activities who cannot be recognized as the manuscripts’ authors should be mentioned in acknowledgement section. Financial sponsors, coordinators, colleagues, laboratory staff and technical supporters, scientific writing proof readers, institutions and organizations should be appreciated in this section. The names listed in acknowledgements section will be indexed by some databases like US National Library Medicine (NLM) ( https://www.nlm.nih.gov/ ) (Ahlstrom 2017 ).

Conflict of interest

If the authors have any concerns regarding moral or financial interests, they should declare it unambiguously, because the related interests may lead to biases and suspicions of misconducts (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ). This section usually comes right after acknowledgements and before references.

Application of relevant and pertinent references supports the manuscript’s scientific documentary. Moreover, utilization of related references with high citation helps the quality of the manuscript. For searching references, it is recommended to use search engines like Google Scholar ( https://scholar.google.com/ ), databases such as MEDLINE ( https://www.nlm.nih.gov/bsd/medline.html ) and NCBI ( https://www.ncbi.nlm.nih.gov/ ) and Web sites including SCOPUS ( https://www.scopus.com/ ), etc.; in this regard, the keywords are used for a successful and effective search. Each journal has its own bibliographic system; hence, it is recommended to use reference management software tools, e.g., EndNote®. The most common bibliographic styles are APA American Psychological Association, Harvard and Vancouver. Nevertheless, the authors should aware of retracted articles and making sure not to use them as references (Ahlstrom 2017 ; Behzadi 2021 ; Lilleyman 1995 ; Tahaei et al. 2021 ). Depending on the journal, there are different limitations for the number of references. It is recommended to read carefully the instructions for authors section of the journal.

Conclusions for future biology

From the societal standpoint, the publication of scientific results may lead to important advances in technology and innovation. In medicine, patient care—and the biomedical sciences in general—the publication of scientific research may also lead to substantial benefits to advancing the medical practice, as evidence-based medicine (EBM) is based on the available scientific data at the present time. Additionally, academic institutions and many academic centers require young medical professionals to be active in the scientific scene for promotions and many employment prospects. Although scientific writing is part of the curricula for many medical programs, not every physician may have adequate knowledge on formulating research results for publication adequately. The present review aimed to briefly and concisely summarize the details of creating a favorable original article to aid early career researchers in the submission to peer-reviewed journal and subsequent publication. Although not all concepts have been discussed in detail, the paper allows for current and future authors to grasp the basic ideas regarding scientific writing and the authors hope to encourage everyone to take the “leap of faith” into scientific research in medicine and to submit their first article to international journals.

Data accessibility

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Payam Behzadi would like to thank the Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran, for approving the organization of the workshop on “How to write a scientific paper?” Márió Gajdács would also like to acknowledge the support of ESCMID’s “30 under 30” Award.

Open access funding provided by University of Szeged. Márió Gajdács was supported by the János Bolyai Research Scholarship (BO/00144/20/5) of the Hungarian Academy of Sciences and the New National Excellence Programme (ÚNKP-20-5-SZTE-330) of the Ministry of Human Resources.

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Department of Microbiology, College of Basic Sciences, Shahr-e-Qods Branch, Islamic Azad University, Tehran, 37541-374, Iran

Payam Behzadi

Institute of Medical Microbiology, Faculty of Medicine, Semmelweis University, Budapest, Nagyvárad tér 4, 1089, Hungary

Márió Gajdács

Department of Pharmacodynamics and Biopharmacy, Faculty of Pharmacy, University of Szeged, Szeged, Eötvös utca 6., 6720, Hungary

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Behzadi, P., Gajdács, M. Writing a strong scientific paper in medicine and the biomedical sciences: a checklist and recommendations for early career researchers. BIOLOGIA FUTURA 72 , 395–407 (2021). https://doi.org/10.1007/s42977-021-00095-z

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Received : 08 April 2021

Accepted : 16 July 2021

Published : 28 July 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s42977-021-00095-z

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