The evidence that evidence-based medicine omits

Evidence of mechanisms in evidence-based policy

Evidence-based policy has achieved great relevance in policy-making and social research. Nonetheless, over the past few years, several problematic aspects of this approach have been identified. This paper discusses whether, and to what extent, evidence of mechanisms could contribute to addressing certain difficulties faced by evidence-based policy. I argue that it could play a crucial role in the assessment of the efficacy of interventions, the extrapolation of interventions to target populations, and the identification of side effects. For analysing the potential contribution of evidence of mechanisms, the previous debate on the pluralist approach to evidence-based medicine is taken as reference.

Evidence-based Medicine and Mechanistic Evidence: The Case of the Failed Rollout of Efavirenz in Zimbabwe

Evidence-based medicine (EBM) has long deemphasized mechanistic reasoning and pathophysiological rationale in assessing the effectiveness of interventions. The EBM+ movement has challenged this stance, arguing that evidence of mechanisms and comparative studies should both be seen as necessary and complementary. Advocates of EBM+ provide a combination of theoretical arguments and examples of mechanistic reasoning in medical research. However, EBM+ proponents have not provided recent examples of how downplaying mechanistic reasoning resulted in worse medical results than would have occurred otherwise. Such examples are necessary to make the case that EBM+ responds to a problem in clinical practice that urgently demands a solution. In light of this, we examine the failed rollout of efavirenz as a first-line HIV treatment in Zimbabwe as evidence of the importance of mechanistic reasoning in improving clinical practice and public health policy decisions. We suggest that this case is analogous to examples commonly given to support EBM.

Clinical recommendations: The role of mechanisms in the GRADE framework

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework has become one of the most influential frameworks for assessing quality of research and developing clinical recommendations. The GRADE framework has been presented as an evolution in the Evidence-Based Medicine (EBM) movement. Both GRADE and EBM emphasize effect estimates derived from population-level clinical trials and, as a consequence, devalue the role of mechanisms as the basis for clinical decisions. Although mechanisms do not hold the epistemic privilege of rigorous clinical trials in EBM reasoning, this paper will argue that mechanisms appear to be important in the use and application of GRADE, as described in the literature. The seemingly necessary role of mechanisms in the development of clinical recommendations has, so far, received little attention and is not explicitly featured in the literature describing GRADE. The analysis of the GRADE framework presented in this paper reveals an apparent tension between EBM's willingness to downplay mechanisms and what seems their inevitable use in GRADE. In this paper, we take the position that if mechanistic reasoning is inevitable in the use of GRADE, then the instructional literature on the framework would benefit from more explicit discussion of how to consider and integrate mechanisms.

The failure of drug repurposing for COVID-19 as an effect of excessive hypothesis testing and weak mechanistic evidence

The current strategy of searching for an effective treatment for COVID-19 relies mainly on repurposing existing therapies developed to target other diseases. Conflicting results have emerged in regard to the efficacy of several tested compounds but later results were negative. The number of conducted and ongoing trials and the urgent need for a treatment pose the risk that false-positive results will be incorrectly interpreted as evidence for treatments' efficacy and a ground for drug approval. Our purpose is twofold. First, we show that the number of drug-repurposing trials can explain the false-positive results. Second, we assess the evidence for treatments' efficacy from the perspective of evidential pluralism and argue that considering mechanistic evidence is particularly needed in cases when the evidence from clinical trials is conflicting or of low quality. Our analysis is an application of the program of Evidence Based Medicine Plus (EBM+) to the drug repurposing trials for COVID. Our study shows that if decision-makers applied EBM+, authorizing the use of ineffective treatments would be less likely. We analyze the example of trials assessing the efficacy of hydroxychloroquine as a treatment for COVID-19 and mechanistic evidence in favor of and against its therapeutic power to draw a lesson for decision-makers and drug agencies on how excessive hypothesis testing can lead to spurious findings and how studying negative mechanistic evidence can be helpful in discriminating genuine from spurious results.

A New Approach to Disease, Risk, and Boundaries Based on Emergent Probability

The status of risk factors and disease remains a disputed question in the theory and practice of medicine and healthcare, and so does the related question of delineating disease boundaries. I present a framework based on Bernard Lonergan's account of emergent probability for differentiating (1) generically distinct levels of systematic function within organisms and between organisms and their environments and (2) the methods of functional, genetic, and statistical investigation. I then argue on this basis that it is possible to understand disease in terms of biological or higher intra-level dysfunction, risk factors-including genetic risk factors-in terms of statistical inter-level conditioning of a given stage or developmental sequence of systematic functioning, and the empirical boundaries of disease in terms of the limits of both functional categorization (from an epistemic standpoint) and upper-level integration of lower-level processes and events (from an ontological standpoint).

Reframing the environment in data-intensive health sciences

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research.

Is meta-analysis of RCTs assessing the efficacy of interventions a reliable source of evidence for therapeutic decisions?

Literature-based meta-analysis is a standard technique applied to pool results of individual studies used in medicine and social sciences. It has been criticized for being too malleable to constrain results, averaging incomparable values, lacking a measure of evidence's strength, and problems with a systematic bias of individual studies. We argue against using literature-based meta-analysis of RCTs to assess treatment efficacy and show that therapeutic decisions based on meta-analytic average are not optimal given the full scope of existing evidence. The argument proceeds with discussing examples and analyzing the properties of some standard meta-analytic techniques. First, we demonstrate that meta-analysis can lead to reporting statistically significant results despite the treatment's limited efficacy. Second, we show that meta-analytic confidence intervals are too narrow compared to the variability of treatment outcomes reported by individual studies. Third, we argue that literature-based meta-analysis is not a reliable measurement instrument. Finally, we show that meta-analysis averages out the differences among studies and leads to a loss of information. Despite these problems, literature-based meta-analysis is useful for the assessment of harms. We support two alternative approaches to evidence amalgamation: meta-analysis of individual patient data (IPD) and qualitative review employing mechanistic evidence.

A Systematic Review and Recommendations Around Frameworks for Evaluating Scientific Validity in Nutritional Genomics

Background: There is a significant lack of consistency used to determine the scientific validity of nutrigenetic research. The aims of this study were to examine existing frameworks used for determining scientific validity in nutrition and/or genetics and to determine which framework would be most appropriate to evaluate scientific validity in nutrigenetics in the future.

Methods: A systematic review (PROSPERO registration: CRD42021261948) was conducted up until July 2021 using Medline, Embase, and Web of Science, with articles screened in duplicate. Gray literature searches were also conducted (June-July 2021), and reference lists of two relevant review articles were screened. Included articles provided the complete methods for a framework that has been used to evaluate scientific validity in nutrition and/or genetics. Articles were excluded if they provided a framework for evaluating health services/systems more broadly. Citing articles of the included articles were then screened in Google Scholar to determine if the framework had been used in nutrition or genetics, or both; frameworks that had not were excluded. Summary tables were piloted in duplicate and revised accordingly prior to synthesizing all included articles. Frameworks were critically appraised for their applicability to nutrigenetic scientific validity assessment using a predetermined categorization matrix, which included key factors deemed important by an expert panel for assessing scientific validity in nutrigenetics.

Results: Upon screening 3,931 articles, a total of 49 articles representing 41 total frameworks, were included in the final analysis (19 used in genetics, 9 used in nutrition, and 13 used in both). Factors deemed important for evaluating nutrigenetic evidence related to study design and quality, generalizability, directness, consistency, precision, confounding, effect size, biological plausibility, publication/funding bias, allele and nutrient dose-response, and summary levels of evidence. Frameworks varied in the components of their scientific validity assessment, with most assessing study quality. Consideration of biological plausibility was more common in frameworks used in genetics. Dose-response effects were rarely considered. Two included frameworks incorporated all but one predetermined key factor important for nutrigenetic scientific validity assessment.

Discussion/Conclusions: A single existing framework was highlighted as optimal for the rigorous evaluation of scientific validity in nutritional genomics, and minor modifications are proposed to strengthen it further.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=261948, PROSPERO [CRD42021261948].

What is evidence as evidence is used? A case of dualism?

How ‘evidence’ is conceptualised, generated and deployed in meso-level policy implementation on the ground is critical to health delivery. Using the case of a large-scale health service reconfiguration in northwest England, this study began as a narrative investigation into how different data types and sources are prioritised as NHS administrative structures change over time. During the research, one unpopular reconfiguration decision, the downgrading of a hospital, was challenged using judicial review. Suddenly, a key decision was being based not upon ‘facts and data’ type evidence but upon evidence of adherence to administrative procedure. This transferred focus away from the ever-shifting categories and hierarchies of data ‘types’ towards an emphasis on process. By comparing two deliberative contexts—committee and judicial review—this article proposes that evidence can be understood as simultaneously entity and process. As health service reconfigurations continue in response to austerity, integration agendas, evolving organisational landscapes, and demographic and political change, it is increasingly important to recognise the different meanings and uses of evidence.

Believing in Black Boxes: Machine Learning for Healthcare Does Not Need Explainability to be Evidence-Based

OBJECTIVE

To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application.

STUDY DESIGN AND SETTING

This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC.

RESULTS

We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating inexplicable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine.

CONCLUSION

Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems.

Forty-eight years of research on psychosocial interventions in the treatment of opioid use disorder: A scoping review

BACKGROUND

Mapped the sources and types of evidence available on psychosocial interventions in the treatment of opioid use disorder (OUD), with and without pharmacotherapies.

METHODS

Six electronic databases were searched for research published until July 1, 2019. Included studies were coded on publication characteristics, evidence sources, treatment settings and modalities, study populations and patient characteristics, intervention(s) offered to patients, research questions addressed in experimental studies, and outcomes investigated.

RESULTS

We identified 305 empirical studies of 54,607 patients. Most studies (64 %; n = 194) compared psychosocial interventions to alternative treatment(s) (183 RCTs and 11 quasi-experiments) while 28 % (n = 86) used observational designs, and 8% (n = 25) used qualitative methods. Trials infrequently investigated effects of stand-alone psychosocial interventions without pharmacotherapies (20% of all RCTs). Regardless of research question or study design, program retention and illicit drug use were the most common outcomes investigated (> 81% of all studies and RCTs), typically among longstanding male heroin users attending specialty outpatient addiction services. Studies rarely examined (a) OUD treatment in general health care or prescription OUD (each < 6 % of all studies and RCTs), (b) effects of social assistance (employment, education, social support) and harm reduction (each < 6 % of studies; < 7 % of RCTs), and (c) health-related quality of life and satisfaction with care (each < 10 % and < 15 % of all studies and RCTs, respectively).

CONCLUSIONS

Scant evidence is available on the putative rehabilitative effects of psychosocial interventions, either as stand-alone treatments or in an adjunct role to pharmacotherapies.

Underdetermination and evidence-based policy

Safety assessment of technologies and interventions is often underdetermined by evidence. For example, scientists have collected evidence concerning genetically modified plants for decades. This evidence was used to ground opposing safety protocols for "stacked genetically modified" plants, in which two or more genetically modified plants are combined. Evidence based policy would thus be rendered more effective by an approach that accounts for underdetermination. Douglas (2012) proposes an explanatory approach, based on the criteria of transparency, empirical competence, internal consistency of explanations, and predictive potency. However, sometimes multiple explanations can satisfy these criteria. We propose an additional criterion based on converse abduction, where explanations are selected on the basis of ontological background assumptions as well as by evidence. We then apply our proposed scheme to the case of the regulation of stacked genetically modified plants. We discuss the implications and suggest follow-up work concerning the generalizability of the approach.

Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal

BACKGROUND

Our purpose is to assess epidemiological agent-based models-or ABMs-of the SARS-CoV-2 pandemic methodologically. The rapid spread of the outbreak requires fast-paced decision-making regarding mitigation measures. However, the evidence for the efficacy of non-pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi-experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS-CoV-2. To address the pressing policy questions that have emerged as a result of COVID-19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent-based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions.

METHODS

To address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID-19 epidemic in Australia) as a case study of the modelling practice.

RESULTS

Our example shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS-CoV-2 are represented sufficiently accurately. This is the case because these modellers treat empirical results as inputs for constructing modelling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables.

CONCLUSIONS

Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference-making evidence. Consequently, they may also adequately describe the effects of possible interventions. Finally, we discuss the limitations of ABMs and put forward policy recommendations.

Causal Evidence and Dispositions in Medicine and Public Health

Since the introduction of evidence-based medicine, there have been discussions about the epistemic primacy of randomised controlled trials (RCTs) for establishing causality in medicine and public health. A growing movement within philosophy of science calls instead for evidential pluralism: that we need more than one single method to investigate health outcomes. How should such evidential pluralism look in practice? How useful are the various methods available for causal inquiry? Further, how should different types of causal evidence be evaluated? This paper proposes a constructive answer and introduces a framework aimed at supporting scientists in developing appropriate methodological approaches for exploring causality. We start from the philosophical tradition that highlights intrinsic properties (dispositions, causal powers or capacities) as essential features of causality. This abstract idea has wide methodological implications. The paper explains how different methods, such as lab experiments, case studies, N-of-1 trials, case control studies, cohort studies, RCTs and patient narratives, all have some strengths and some limitations for picking out intrinsic causal properties. We explain why considering philosophy of causality is crucial for evaluating causality in the health sciences. In our proposal, we combine the various methods in a temporal process, which could then take us from an observed phenomenon (e.g., a correlation) to a causal hypothesis and, finally, to improved theoretical knowledge.

Mechanisms in clinical practice: use and justification

While the importance of mechanisms in determining causality in medicine is currently the subject of active debate, the role of mechanistic reasoning in clinical practice has received far less attention. In this paper we look at this question in the context of the treatment of a particular individual, and argue that evidence of mechanisms is indeed key to various aspects of clinical practice, including assessing population-level research reports, diagnostic as well as therapeutic decision making, and the assessment of treatment effects. We use the pulmonary condition bronchiectasis as a source of examples of the importance of mechanistic reasoning to clinical practice.

From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls "foundational issues." In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor-based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

Evaluating evidential pluralism in epidemiology: mechanistic evidence in exposome research

In current philosophical discussions on evidence in the medical sciences, epidemiology has been used to exemplify a specific version of evidential pluralism. According to this view, known as the Russo-Williamson Thesis, evidence of both difference-making and mechanisms is produced to make causal claims in the health sciences. In this paper, I present an analysis of data and evidence in epidemiological practice, with a special focus on research on the exposome, and I cast doubt on the extent to which evidential pluralism holds in this case. I start by focusing on the claim that molecular data allows for the production of mechanistic evidence. On the basis of a close look at the ways in which molecular data is used in exposome research, I caution against interpretations in terms of mechanistic evidence. Secondly, I expand my critical remarks on the thesis by addressing the conditions under which data is categorised as evidence in exposome research. I argue that these show that the classification of a dataset as a type of evidence is dependent on the ways in which the data is used. This is in contrast with the approach of evidential pluralism, where evidence is classified in different types on the basis of its intrinsic properties. Finally, I come back to what I consider the core of the thesis and suggest that the epidemiological research analysed in the paper indicates different interpretations of evidential pluralism and its applicability in the health sciences.

What is mechanistic evidence, and why do we need it for evidence-based policy?

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration.

The need for a rationalist turn in evidence-based medicine

When evidence-based medicine (EBM) became established, its dominant rhetoric was empiricist, in spite of rationalist elements in its practice. Exploring some of the key statements about EBM down the years, the paper examines the tensions between empiricism and rationalism and argues for a rationalist turn in EBM to help to develop the next generation of scholarship in the field.

The Emperor's New Clothes: a Critical Appraisal of Evidence-based Medicine

Evidence-Based Medicine (EBM) is the way we are expected to deliver our healthcare in the 21st century. It has been described as the integration of information from best available evidence with the doctor's experience and the patient's point of view. Unfortunately, the original meaning of EBM has been lost and the worldwide medical community has shifted the paradigm to Guidelines-Based Medicine, that has displaced the figures of the doctor and the patient from the decision-making process and relegated them to mere executor and final target of decisions taken by someone else. Problems related to the reliability of evidence and to the way guidelines are constructed, implemented and followed are discussed in detail. It is mandatory that the whole medical community takes responsibility and tries to reverse this apparently inexorable process so to re-establish a proper evidence-based care, where patients and their healing relation with practitioners are at the centre and where doctors are able to critically evaluate the available evidence and use it in light of their personal experience and knowledge.

What is nature capable of? Evidence, ontology and speculative medical humanities

Expanding on the recent call for a 'critical medical humanities' to intervene in questions of the ontology of health, this article develops a what we call a 'speculative' orientation to such interventions in relation to some of the ontological commitments on which contemporary biomedical cultures rest. We argue that crucial to this task is an approach to ontology that treats it not as a question of first principles, but as a matter of the consequences of the images of nature that contemporary biomedical research practices espouse when they make claims to evidence, as well as the possible consequences of imagining different worlds in which health and disease processes partake. By attending to the implicit ontological assumptions involved in the method par excellence of biomedical research, namely the randomised controlled trial (RCT), we argue that the mechanistic ontology that tacitly informs evidence-based biomedical research simultaneously authorises a series of problematic consequences for understanding and intervening practically in the concrete realities of health. As a response, we develop an alternative ontological proposition that regards processes of health and disease as always situated achievements. We show that, without disqualifying RCT-based evidence, such a situated ontology enables one to resist the reduction of the realities of health and disease to biomedicine's current forms of explanation. In so doing, we call for medical humanities scholars to actively engage in the speculative question of what nature may be capable of.

Time, space and form: Necessary for causation in health, disease and intervention?

Sir Austin Bradford Hill's 'aspects of causation' represent some of the most influential thoughts on the subject of proximate causation in health and disease. Hill compiled a list of features that, when present and known, indicate an increasing likelihood that exposure to a factor causes-or contributes to the causation of-a disease. The items of Hill's list were not labelled 'criteria', as this would have inferred every item being necessary for causation. Hence, criteria that are necessary for causation in health, disease and intervention processes, whether known, knowable, or not, remain undetermined and deserve exploration. To move beyond this position, this paper aims to explore factors that are necessary in the constitution of causative relationships between health, disease processes, and intervention. To this end, disease is viewed as a causative pathway through the often overlapping stages of aetiology, pathology and patho-physiology. Intervention is viewed as a second, independent causative pathway, capable of causing changes in health for benefit or harm. For the natural course of a disease pathway to change, we argue that intervention must not only occupy the same time and space, but must also share a common form; the point at which the two pathways converge and interact. This improved conceptualisation may be used to facilitate the interpretation of clinical observations and inform future research, particularly enabling predictions of the mechanistic relationship between health, disease and intervention.

Adding Qualitative and Mixed Methods Research to Health Intervention Studies: Interacting With Differences

The purpose of this article is to explain how to improve intervention designs, such as randomized controlled trials (RCTs), in health science research using a process philosophy and theory known as dialectical pluralism (DP). DP views reality as plural and uses dialectical, dialogical, and hermeneutical approaches to knowledge construction. Using DP and its "both/and" logic, and its attempt to produce new creative syntheses, researchers on heterogeneous teams can better dialogue with qualitative and mixed methods approaches, concepts, paradigms, methodologies, and methods to improve their intervention research studies. The concept of reflexivity is utilized but is expanded when it is a component of DP. Examples of strategies for identifying, inviting, and creating divergence and integrative strategies for producing strong mixed methods intervention studies are provided and illustrated using real-life examples.

Evidence, illness, and causation: an epidemiological perspective on the Russo-Williamson Thesis

According to the Russo-Williamson Thesis, causal claims in the health sciences need to be supported by both difference-making and mechanistic evidence. In this article, we attempt to determine whether Evidence-based Medicine (EBM) can be improved through the consideration of mechanistic evidence. We discuss the practical composition and function of each RWT evidence type and propose that exposure-outcome evidence (previously known as difference-making evidence) provides associations that can be explained through a hypothesis of causation, while mechanistic evidence provides finer-grained associations and knowledge of entities that ultimately explains a causal hypothesis. We suggest that mechanistic evidence holds untapped potential to add value to the assessment of evidence quality in EBM and propose initial recommendations for the integration of mechanistic and exposure-outcome evidence to improve EBM by robustly leveraging available evidence in support of good medical decisions.

Negative mechanistic reasoning in medical intervention assessment

Traditionally, mechanistic reasoning has been assigned a negligible role in standard EBM (evidence-based medicine) literature, although some recent authors have argued for an upgrade. Even so, the mechanistic reasoning that has received attention has almost exclusively been positive--both in an epistemic sense of claiming that there is a mechanistic chain and in a health-related sense of there being claimed benefits for the patient. Negative mechanistic reasoning has been neglected, both in the epistemic and in the health-related sense. I distinguish three main types of negative mechanistic reasoning and subsume them under a new definition of mechanistic reasoning in the context of assessing medical interventions. This definition is wider than a previous suggestion in the literature. Each negative type corresponds to a range of evidential strengths, and it is argued that there are differences with respect to typical evidential strengths. The variety of negative mechanistic reasoning should be acknowledged in EBM, and it presents a serious challenge to proponents of so-called medical hierarchies of evidence.

Using a matrix-analytical approach to synthesizing evidence solved incompatibility problem in the hierarchy of evidence

OBJECTIVES

The hierarchy of evidence presupposes linearity and additivity of effects, as well as commutativity of knowledge structures. It thereby implicitly assumes a classical theoretical model.

STUDY DESIGN AND SETTING

This is an argumentative article that uses theoretical analysis based on pertinent literature and known facts to examine the standard view of methodology.

RESULTS

We show that the assumptions of the hierarchical model are wrong. The knowledge structures gained by various types of studies are not sequentially indifferent, that is, do not commute. External validity and internal validity are at least partially incompatible concepts. Therefore, one needs a different theoretical structure, typical of quantum-type theories, to model this situation. The consequence of this situation is that the implicit assumptions of the hierarchical model are wrong, if generalized to the concept of evidence in total.

CONCLUSION

The problem can be solved by using a matrix-analytical approach to synthesizing evidence. Here, research methods that produce different types of evidence that complement each other are synthesized to yield the full knowledge. We show by an example how this might work. We conclude that the hierarchical model should be complemented by a broader reasoning in methodology.

Causation in evidence-based medicine: in reply to Kerry et al

Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.

10 years of mindlines: a systematic review and commentary

Background

In 2004, Gabbay and le May showed that clinicians generally base their decisions on mindlines—internalised and collectively reinforced tacit guidelines—rather than consulting written clinical guidelines. We considered how the concept of mindlines has been taken forward since.

Methods

We searched databases from 2004 to 2014 for the term ‘mindline(s)’ and tracked all sources citing Gabbay and le May’s 2004 article. We read and re-read papers to gain familiarity and developed an interpretive analysis and taxonomy by drawing on the principles of meta-narrative systematic review.

Results

In our synthesis of 340 papers, distinguished between authors who used mindlines purely in name (‘nominal’ view) sometimes dismissing them as a harmful phenomenon, and authors who appeared to have understood the term’s philosophical foundations. The latter took an ‘in-practice’ view (studying how mindlines emerge and spread in real-world settings), a ‘theoretical and philosophical’ view (extending theory) or a ‘solution focused’ view (exploring how to promote and support mindline development). We found that it is not just clinicians who develop mindlines: so do patients, in face-to-face and (potentially) online communities.

Theoretical publications on mindlines have continued to challenge the rationalist assumptions of evidence-based medicine (EBM). Conventional EBM assumes a single, knowable reality and seeks to strip away context to generate universal predictive rules. In contrast, mindlines are predicated on a more fluid, embodied and intersubjective view of knowledge; they accommodate context and acknowledge multiple realities. When considering how knowledge spreads, the concept of mindlines requires us to go beyond the constraining notions of ‘dissemination’ and ‘translation’ to study tacit knowledge and the interactive human processes by which such knowledge is created, enacted and shared. Solution-focused publications described mindline-promoting initiatives such as relationship-building, collaborative learning and thought leadership.

Conclusions

The concept of mindlines challenges the naïve rationalist view of knowledge implicit in some EBM publications, but the term appears to have been misunderstood (and prematurely dismissed) by some authors. By further studying mindlines empirically and theoretically, there is potential to expand EBM’s conceptual toolkit to produce richer forms of ‘evidence-based’ knowledge. We outline a suggested research agenda for achieving this goal.

Electronic supplementary material

The online version of this article (doi:10.1186/s13012-015-0229-x) contains supplementary material, which is available to authorized users.

Unsuitability of the epidemiological approach to bicycle transportation injuries and traffic engineering problems

Bicyclists and transportation professionals would do better to decline advice drawn from characteristically epidemiological studies. The faults of epidemiology are both accidental (unpreparedness for the task) and essential (unsuitability of the methods). Characteristically epidemiological methods are known to be error-prone, and when applied to bicycle transportation suffer from diversion bias, inappropriately broad-brush categorisations, a focus on undifferentiated risk rather than on danger, a bias towards unsafe behaviour, and an overly narrow perspective. To the extent that there is a role for characteristically epidemiological methods, it should be the same as anywhere else: as a preliminary or adjunct to the scientific method, for which there is no substitute.

How can the scientific community support the generation of the evidence needed to improve the quality of guidelines for micronutrient interventions?

The process used by the WHO to generate nutrition recommendations relies on high-quality research evidence, and this makes new demands on the research questions that nutrition scientists address. As a researcher involved in WHO nutrition guidelines development, my objective is to suggest ways in which our research can adapt to meet these demands. Randomized controlled trials and systematic reviews generate the highest quality of evidence to support strong recommendations, yet even these methods leave controversies in which judgments must be made. Using examples from recent research and guidelines, 4 issues are highlighted that illustrate ways in which nutrition research can adapt to become more useful and informative to global nutrition guidelines. These issues include embedding mechanistic research within trials, explicit choice of design along the efficacy or effectiveness spectrum, anticipation of heterogeneity of effects, and the need for research on consumer or community values and preferences.