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In physiology education, students must learn to recognize and construct causal explanations. This challenges students, in part, because causal explanations in biology manifest in different varieties. Unlike other natural sciences, causal mechanisms in physiology support physiological functions and reflect biological adaptations. Therefore, students must distinguish between questions that prompt a proximate or an ultimate explanation. In the present investigation, we aimed to determine how these different varieties of student knowledge coordinate within students' written explanations. Prior research in science education demonstrates that students present specific challenges when distinguishing between proximate and ultimate explanations: students appear to conflate the two or construct other nonmechanistic explanations. This investigation, however, demonstrates that analytic frameworks can distinguish between students' proximate and ultimate explanations when they are provided explanatory scaffolds that contextualize questions. Moreover, these scaffolds and prompts help students distinguish between physiological functions and the cellular and molecular mechanisms that underpin them. Together, these findings deliver insight into the context-sensitive nature of student knowledge in physiology education and offer an analytic framework for identifying and characterizing student knowledge in physiology. NEW & NOTEWORTHY Why ask why? How questions posed in physiology task students with developing their mechanistic reasoning. Why questions sometimes undermine this reasoning. Prior research, however, also illustrates that framing the context of a question explicitly supports students in distinguishing between question types. We further illustrate how providing such context in the form of explanatory scaffolds and prompts allows students to tap different and useful varieties of knowledge when constructing written explanations.

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Journal of Medicine and Philosophy Inc.)

Understanding molecular processes and coordinating the various activities across levels of organization in biological systems is a complicated task, yet many curricular guidelines indicate that undergraduate students should master it. Employing mechanistic reasoning can facilitate describing and investigating biological phenomena. Biofilms are an important system in microbiology and biology education. However, few empirical studies have been conducted on student learning of biofilms or how students utilize mechanistic reasoning related to systems thinking to explain biofilm formation. Using mechanistic reasoning and the theory of knowledge integration as conceptual and analytical frameworks, we examined the features of 9 undergraduate biology students' mechanistic models of a specific transition point in biofilm development. From these data, we constructed a model of knowledge integration in the context of biofilms, which categorizes students' knowledge based on features of their descriptions (e.g., entities, correct connections, and the nature of connections). We found that 4 of 9 students produced a fragmented model, 4 of 9 students produced a transitional model, and 1 student produced a connected model. Overall, students often did not discuss cell-cell communication mechanics in their mechanistic models and rarely included the role of gene regulation. Most connections were considered nonnormative and lacked important entities, leading to an abundance of unspecified causal connections. We recommend increasing instructional support of mechanistic reasoning within systems (e.g., identifying entities across levels of organization and their relevant activities) and creating opportunities for students to grapple with their understanding of various biological concepts and to explore how processes interact and connect in a complex system., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Flowers et al.)

Mechanistic complexity is an important property that affects how we interact with and learn from artifacts. Although highly complex artifacts have only recently become part of human material culture, they are ever-present in contemporary life. In previous research, children successfully detected complexity contrasts when given information about the functions of simple and complex objects. However, whether children spontaneously favor relevant information about an object's causal mechanisms and functions when trying to determine an object's complexity remains an open question. In Study 1, 7- to 9-year-olds and adults, but not 5- and 6-year-olds, rated information about relevant actions (e.g., the difficulty in fixing an object) as more helpful than information about irrelevant actions (e.g., the difficulty in spelling an object's name) for making determinations of mechanistic complexity. Only in Study 2, in which the relevance contrasts were extreme, did the youngest age group rate relevant actions as more helpful than irrelevant actions. In Study 3, in which participants rated the complexity of the actions themselves, participants performed differently than in the previous studies, suggesting that children in the prior studies did not misinterpret the study instructions as prompts to rate the actions' complexity. These results suggest that the ability to detect which object properties imply complexity emerges during the early school years. Younger children may be misled by features that are not truly diagnostic of mechanistic complexity, whereas older children more easily disregard such features in favor of relevant information., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Rationale: Exercise interventions and policies are widely prescribed in both sport and healthcare. Research investigating exercise interventions and policies is generally conducted using an Evidence-Based framework, placing an emphasis on evidence gathered from randomised controlled trials (RCTs)., Aims and Objectives: To explore the idea that, in addition to the assessment of evidence from RCTs when investigating exercise interventions, mechanistic studies ought to also be assessed and considered., Methods: This article assesses the rationale supporting the use of RCTs as evidence for exercise interventions, and the use of evidence of mechanisms in establishing efficacy, determining external validity, and tailoring interventions., Results and Conclusions: The article argues that evidence from mechanistic studies ought to be considered alongside evidence from RCTs because: as RCTs investigating exercise interventions tend to be of low quality, mechanistic studies ought to be used to reinforce the evidence base; further, evidence from mechanistic studies is highly useful for both questions of extrapolation and implementation. This article argues for this on theoretical grounds, and also draws on a number of case studies., (© 2022 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

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., (Copyright © 2021. Published by Elsevier Inc.)

Rationale: Evidence-based medicine (EBM), the dominant approach to assessing the effectiveness of clinical and public health interventions, focuses on the results of association studies. EBM+ is a development of EBM that systematically considers mechanistic studies alongside association studies., Aims and Objectives: To explore examples of the importance of mechanistic evidence to coronavirus research., Methods: We have reviewed the mechanistic evidence in four major areas that are relevant to the management of COVID-19., Results and Conclusions: (a) Assessment of combination therapy for MERS highlights the need for systematic assessment of mechanistic evidence. (b) That hypertension is a risk factor for severe disease in the case of SARS-CoV-2 suggests that altering hypertension treatment might alleviate disease, but the mechanisms are complex, and it is essential to consider and evaluate multiple mechanistic hypotheses. (c) Confidence that public health interventions will be effective requires a detailed assessment of social and psychological components of the mechanisms of their action, in addition to mechanisms of disease. (d) In particular, if vaccination programmes are to be effective, they must be carefully tailored to the social context; again, mechanistic evidence is crucial. We conclude that coronavirus research is best situated within the EBM+ evaluation framework., (© 2020 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Some philosophers have argued that evidence of underlying mechanisms does not provide evidence for the effectiveness of a medical intervention. One such argument appeals to the unreliability of mechanistic reasoning. However, mechanistic reasoning is not the only way that evidence of mechanisms might provide evidence of effectiveness. A more reliable type of reasoning may be distinguished by appealing to recent work on evidential pluralism in the epistemology of medicine. A case study from virology provides an example of this so-called reinforced reasoning in medicine. It is argued that in this case study, the available evidence of underlying mechanisms did in fact play a role in providing evidence in favour of a medical intervention. This paper therefore adds a novel and recent case study to the literature in support of evidential pluralism in medicine., (© 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

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.

Physiology demands systems thinking: reasoning within and between levels of biological organization and across different organ systems. Many physiological mechanisms explain how structures and their properties interact at one level of organization to produce emergent functions at a higher level of organization. Current physiology principles, such as structure-function relations, selectively neglect mechanisms by not mentioning this term explicitly. We explored how students characterized mechanisms and functions to shed light on how students make sense of these terms. Students characterized mechanisms as 1 ) processes that occur at levels of organization lower than that of functions; and 2 ) as detailed events with many steps involved. We also found that students produced more variability in how they characterized functions compared with mechanisms: students characterized functions in relation to multiple levels of organization and multiple definitions. We interpret these results as evidence that students see mechanisms as holding a more narrow definition than used in the biological sciences, and that students struggle to coordinate and distinguish mechanisms from functions due to cognitive processes germane to learning in many domains. We offer the instructional suggestion that we scaffold student learning by affording students opportunities to relate and also distinguish between these terms so central to understanding physiology., (Copyright © 2017 the American Physiological Society.)

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.