This paper argues that epistemic errors rooted in group- or identity- based biases, especially those pertaining to disability, are undertheorized in the literature on medical error. After sketching dominant taxonomies of medical error, we turn to the field of social epistemology to understand the role that epistemic schemas play in contributing to medical errors that disproportionately affect patients from marginalized social groups. We examine the effects of this unequal distribution through a detailed case study of (...) ableism. There are four primary mechanisms through which the epistemic schema of ableism distorts communication between nondisabled physicians and disabled patients: testimonial injustice, epistemic overconfidence, epistemic erasure, and epistemic derailing. Measures against epistemic injustices in general and against schema-based medical errors in particular are ultimately issues of justice that must be better addressed at all levels of health care practice. (shrink)

The phenomenon of medical overtesting in general, and specifically in the emergency room, is well-known and regarded as harmful to both the patient and the healthcare system. Although the implications of this problem raise myriad ethical concerns, this paper explores the extent to which overtesting might mitigate race-based health inequalities. Given that medical malpractice and error greatly increase when the patients belong to a racial minority, it is no surprise that the mortality rate similarly increases in proportion (...) to white patients. For these populations, an environment that emphasizes medical overtesting may well be the desirable medical environment until care evens out among races and ethnicities; additionally, efforts to lower overtesting in conjunction with a high rate of racist medical mythology may cause harm by lower testing when it is actually warranted. Furthermore, medical overtesting may help to assuage racial distrust. This paper ultimately concludes that an environment of medical overtesting may be less pernicious than the alternative. (shrink)

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of (...) the QE is akin to functional characteristics of a specific class of retinal ganglion cells (the so-called Y-cells) in the visual systems of the primate and the cat, the sensitivity of the QE surpasses the capacity limits of human visual detection. Here, the quantization error in the SOM is found to reliably signal changes in contrast or colour when contrast information is removed from or added to the image, but not when the amount and relative weight of contrast information is constant and only the local spatial position of contrast elements in the pattern changes. While the RGB Mean reflects coarser changes in colour or contrast well enough, the SOM-QE is shown to outperform the RGB Mean in the detection of single-pixel changes in images with up to five million pixels. This could have important implications in the context of unsupervised image learning and computational building block approaches to large sets of image data (big data). (shrink)

Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential mistakes. We demon­strate the methodology by outlining the different types of mistakes that are found when the algorithms are applied to SNOMED-CT. On the basis of the results, we argue that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.

Medical diagnosis has been traditionally recognized as a privileged field of application for so called probabilistic induction. Consequently, the Bayesian theorem, which mathematically formalizes this form of inference, has been seen as the most adequate tool for quantifying the uncertainty surrounding the diagnosis by providing probabilities of different diagnostic hypotheses, given symptomatic or laboratory data. On the other side, it has also been remarked that differential diagnosis rather works by exclusion, e.g. by modus tollens, i.e. deductively. By drawing on (...) a case history, this paper aims at clarifying some points on the issue. Namely: 1) Medical diagnosis does not represent, strictly speaking, a form of induction, but a type, of what in Peircean terms should be called ‘abduction’ (identifying a case as the token of a specific type); 2) in performing the single diagnostic steps, however, different inferential methods are used for both inductive and deductive nature: modus tollens, hypothetical-deductive method, abduction; 3) Bayes’ theorem is a probabilized form of abduction which uses mathematics in order to justify the degree of confidence which can be entertained on a hypothesis given the available evidence; 4) although theoretically irreconcilable, in practice, both the hypothetical- deductive method and the Bayesian one, are used in the same diagnosis with no serious compromise for its correctness; 5) Medical diagnosis, especially differential diagnosis, also uses a kind of “probabilistic modus tollens”, in that, signs (symptoms or laboratory data) are taken as strong evidence for a given hypothesis not to be true: the focus is not on hypothesis confirmation, but instead on its refutation [Pr (¬ H/E1, E2, …, En)]. Especially at the beginning of a complicated case, odds are between the hypothesis that is potentially being excluded and a vague “other”. This procedure has the advantage of providing a clue of what evidence to look for and to eventually reduce the set of candidate hypotheses if conclusive negative evidence is found. 6) Bayes’ theorem in the hypothesis-confirmation form can more faithfully, although idealistically, represent the medical diagnosis when the diagnostic itinerary has come to a reduced set of plausible hypotheses after a process of progressive elimination of candidate hypotheses; 7) Bayes’ theorem is however indispensable in the case of litigation in order to assess doctor’s responsibility for medical error by taking into account the weight of the evidence at his disposal. (shrink)

We present the details of a methodology for quality assurance in large medical terminologies and describe three algorithms that can help terminology developers and users to identify potential mistakes. The methodology is based in part on linguistic criteria and in part on logical and ontological principles governing sound classifications. We conclude by outlining the results of applying the methodology in the form of a taxonomy different types of errors and potential errors detected in SNOMED-CT.

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...) experts. Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

This chapter argues that adequately facing and responding to medical error requires making space for blame. In vindicating blame as a response to medical error, this essay does not advocate a return to a “bad apple” blame culture in which unlucky practitioners are unfairly scapegoated. It does, however, defend the targeted feeling and expression of angry, and even resentful, blaming attitudes toward health-care providers who make at least certain kinds of mistakes. The chapter makes the case (...) that the angry and resentful feelings associated with blame reflect a conception of oneself as a person worthy of consideration, and that expressing these feelings allows victims and families to fight for their dignity and self-respect. Along the way, it distinguishes between shaming and blaming, explaining that blaming, unlike shaming, characteristically aims to draw offenders into a moral dialogue. As a result, a good blaming practice can provide a logic within which providers who make costly errors may seek forgiveness, reconciliation, and healing. (shrink)

In four experiments, we offer evidence that virtues are often judged as uniquely important for some business practices (e.g., hospital management and medical error investigation). Overall, actions done only from virtue (either by organizations or individuals) were judged to feel better, to be more praiseworthy, to be more morally right, and to be associated with more trustworthy leadership and greater personal life satisfaction compared to actions done only to produce the best consequences or to follow the correct moral (...) rule. These results accord with claims made by some virtue ethicists. The current data contribute to a small but growing body of literature emphasizing both empirical soundness and philosophical rigor in the scientific study of virtue. Results also offer some guidance concerning how businesses, employees, and executives are evaluated and perceived. Implications for theory and practice are discussed. (shrink)

Medical scientists employ ‘quality assessment tools’ (QATs) to measure the quality of evidence from clinical studies, especially randomized controlled trials (RCTs). These tools are designed to take into account various methodological details of clinical studies, including randomization, blinding, and other features of studies deemed relevant to minimizing bias and error. There are now dozens available. The various QATs on offer differ widely from each other, and second-order empirical studies show that QATs have low inter-rater reliability and low inter-tool (...) reliability. This is an instance of a more general problem I call the underdetermination of evidential significance. Disagreements about the strength of a particular piece of evidence can be due to different—but in principle equally good—weightings of the fine-grained methodological features which constitute QATs. (shrink)

Healthcare systems need to consider not only how to prevent error, but how to respond to errors when they occur. In the United Kingdom’s National Health Service, one strand of this latter response is the ‘No Blame Culture’, which draws attention from individuals and towards systems in the process of understanding an error. Defences of the No Blame Culture typically fail to distinguish between blaming someone and holding them responsible. This article argues for a ‘responsibility culture’, where healthcare (...) professionals are held responsible in cases of foreseeable and avoidable errors. We demonstrate how healthcare professionals can justifiably be held responsible for their errors even though they work in challenging circumstances. We then review the idea of ‘responsibility without blame’, applying this to cases of error in healthcare. Sensitive to the undesirable effects of blaming healthcare professionals and to the moral significance of holding individuals accountable, we argue that a responsibility culture has significant advantages over a No Blame Culture due to its capacity to enhance patient safety and support medical professionals in learning from their mistakes, while also recognising and validating the legitimate sense of responsibility that many medical professionals feel following avoidable error, and motivating medical professionals to report errors. (shrink)

Eyal Shahar’s essay review [1] of James Penston’s remarkable book [2] seems more inspired playful academic provocation than review or essay, expressing dramatic views of impossible validity. The account given of modern biostatistical causation reveals the slide from science into the intellectual confusion and non-science RCTs have created: “…. the purpose of medical research is to estimate the magnitude of the effect of a causal contrast, for example the probability ratio of a binary outcome …” But Shahar’s world is (...) simultaneously not probabilistic, but of absolute uncertainty: “We should have no confidence in any type of evidence ….. We should have no confidence at all”. Shahar’s "Causal contrast" is attractive. It seems to make sense, but bypasses in two words the means of establishing causation by the scientific method. This phrase assumes a numeric statistically significant “contrast” is causal rather than a potential correlation requiring further investigation. The concept of “causal contrast” is a slippery slope from sense into biostatistical non-science. This can be illustrated with an hypothetical RCT where 100% of interventions exhibit a posited treatment effect and 0% of placebo controls. Internal validity is seemingly quite reasonably assumed satisfied (common-sense dictating the likelihood of an awesome magnificent fraud, bias or plain error of the magnitude required is infinitesimal). Scientific method appears satisfied. The RCT demonstrates: (1) strict regularity of outcome in the presence of posited cause; (2) the absence of outcome in its absence and (3) an intervention (experiment) showing the direction of causation is from posited cause to posited effect. Now travel further down the slope from science. Assume 50% of interventions and 0% of controls are positive. We compromise scientific method, but justify this by assuming a large subgroup which we say surely must on these figures be exhibiting the posited treatment effect. But what of 10% of interventions and 9% of placebo controls exhibiting the posited treatment effect? Our biostatistician says the 1% “causal contrast” is statistically significant. But we have: (1) minimal evidence of regularity; (2) the posited outcome irrespective of presence of posited cause and (3) our intervention is at the highest equivocal in demonstrating any form of causation. This is not science. It is, however, where biostatistics has unthinkingly taken us, as Penston has shown comprehensively [2]. We, the audience of published medical research, are now for the 10% / 9% example well down the slope from science. An unattractive hypothesis results requiring numerous assumptions similar to these:- "There is a 'contrast' which is ‘causal’, albeit the method employed is not scientific. An effect of the intervention has been observed in a very small subgroup. This subgroup is susceptible to treatment. The similar number of placebo controls exhibiting the outcome sought is irrelevant, because the 1% difference between intervention and controls is statistically significant. The statistical analysis is valid and reliable. The RCT’s internal validity is sufficiently satisfied. No funding or bias or fraud has affected the results or their analysis.” As Penston notes: “Confirming and refuting the results of research is crucial to science …. But … there’s no way of testing the results of any particular large-scale RCT or epidemiological study. Each study … is left hanging in the air, unsupported.” It gets worse. To identify a rare serious adverse reaction of a frequency of 1:10,000 can require a trial of 200,000 or larger split between controls and interventions. This is not done. But for every 100 who prospectively benefit from the intervention, 9,900 also receive it. And for every 100 benefiting one person (who likely gains no benefit) will suffer a serious unidentified adverse reaction. This is also without taking account of more common adverse reactions whether serious or otherwise. References [1] Shahar, E. (2011) Research and medicine: human conjectures at every turn. International Journal of Person Centered Medicine 1 (2), 250-253. [2] Penston, J. (2010). stats.con: How we’ve been fooled by statistics-based research in medicine. London: The London Press, UK. (shrink)

R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness. Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015) towards the instantaneous amplitude and frequency of event-related changes correlated with a (...) contrast in access and in phenomenology. Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following. In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT). -/- In the second section, calculated the variance inflation factor (VIF). In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP). In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic. (shrink)

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

The global pessimistic meta-induction argues from the falsity of scientific theories accepted in the past to the likely falsity of currently accepted scientific theories. I contend that this argument commits a statistical error previously unmentioned in the literature and is self-undermining. I then compare the global pessimistic meta-induction to a local pessimistic meta-induction based on recent negative assessments of the reliability of medical research. If there is any future in drawing pessimistic conclusions from the history of science, it (...) lies in local meta-inductions, but these meta-inductions will not result in global distrust of the results of science. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

ABSTRACT: Abstract Most modern knowledge is not science. The physical sciences have successfully validated theories to infer they can be used universally to predict in previously unexperienced circumstances. According to the conventional conception of science such inferences are falsified by a single irregular outcome. And verification is by the scientific method which requires strict regularity of outcome and establishes cause and effect. -/- Medicine, medical research and many “soft” sciences are concerned with individual people in complex heterogeneous populations. These (...) populations cannot be tested to demonstrate strict regularity of outcome in every individual. Neither randomised controlled trials nor observational studies in medicine are science in the conventional conception. Establishing and using medical and other “soft science” theories cannot be scientific. It requires conceptually different means: requiring expert judgement applying all available evidence in the relevant available factual matrix. -/- The practice of medicine is observational. Prediction of outcomes for the individual requires professional expertise applying available medical knowledge and evidence. Expertise in any profession can only be acquired through experience. Prior cases are the fundament of knowledge and expertise in medicine. Case histories, studies and series can provide knowledge of extremely high reliability applicable to establishing reliable general theories and falsifying others. Their collation, study and analysis should be a priority in medicine. Their devaluation as evidence, the failure to apply their lessons, the devaluation of expert professional judgement and the attempt to emulate the scientific method are all historic errors in the theory and practice of modern medicine. (shrink)

The Foundational Model of Anatomy (FMA) symbolically represents the structural organization of the human body from the macromolecular to the macroscopic levels, with the goal of providing a robust and consistent scheme for classifying anatomical entities that is designed to serve as a reference ontology in biomedical informatics. Here we articulate the need for formally clarifying the is-a and part-of relations in the FMA and similar ontology and terminology systems. We diagnose certain characteristic errors in the treatment of these relations (...) and show how these errors can be avoided through adoption of the formalism we describe. We then illustrate how a consistently applied formal treatment of taxonomy and partonomy can support the alignment of ontologies. (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our (...) investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

Introduction: The purpose of this study was to assess dental care professionals' drug prescription knowledge, practices, and reporting of adverse drug reactions (ADRs). -/- Methods: A cross-sectional exploratory study was conducted by using a face-to-face survey administered to a sample of dentists from tertiary care hospitals in Adana, Türkiye. A questionnaire consisted of six sections with closed-ended items including sociodemographic characteristics, knowledge about drugs, patient history information, counseling practices during prescribing, source of information and ADR reporting. -/- Results: The study (...) included 180 dentists, with a 95.3% response rate. More than half of the dentists (50.6%) stated their level of knowledge about drug price/cost as low. Most of the dentists (50.6%;n=91) claimed that they never/rarely asked about health insurance during patient history information. Moreover, most of the participants reported that they never/rarely and sometimes counsel the patients regarding drug mechanism of action, side effects of the drug, interaction of prescribed drug with other drugs/nutrients. It was determined that the dentists learned about the drugs from Vademecum (Turkish Medication Guidebook: 70%;n=126) and the internet (55%;n=99). A higher proportion (85.5%) of the dentists indicated that they did not report ADR during their clinical practice. -/- Conclusion: This study showed a general improvement in dentists prescribing knowledge and practices, although they reported some lack of knowledge regarding drug cost, discussion about the possible side effect of a drug/interaction with other drugs/nutrients with patients and under-reporting of ADRs. Periodic education and training for dentists are critical to overcoming any problem related to prescribing errors and potential ADRs. (shrink)

There is a great deal of evidence that method bias is really sure influences item validities, measurement error, correlation and covariance between latent constructs and thus leading the researchers to erroneous conclusion due to inflation or deflation during hypothesis testing. To remedy this, the study provides a guideline to minimize the method bias in the context of structural equation modeling employing the covariance method (CB-SEM) using medical tourism model. A practical approach is illustrated for the identification of method (...) bias based on the new construct namely common latent factor. Using this latent construct, we managed to identify which item has potential to permeate more variance from common latent factor. Nevertheless, we figure out that the method bias is do not exist in our developed model. Therefore, this measurement model is appropriate for structural model in order to achieve the research hypotheses. We hope that this discussion will help the researchers anticipate which items are likely exposed on method bias before proceed to advance modeling. (shrink)

Eastern philosophy and western science have convergent and divergent viewpoints for their explanation of consciousness. Convergence is found for the practice of meditation allowing besides a time dependent consciousness, the experience of a timeless consciousness and its beneficial effect on psychological wellbeing and medical improvements, which are confirmed by multiple scientific publications. Theories of quantum mechanics with non-locality and timelessness also show astonishing correlation to eastern philosophy, such as the theory of Penrose-Hameroff (ORC-OR), which explains consciousness by reduction of (...) quantum superposition in the brain. Divergence appears in the interpretation of the subjective experience of timeless consciousness. In eastern philosophy, meditation at a higher level of awareness allows the personal experience of timeless and non-dual consciousness, considered as an empirical proof for the existence of pure consciousness or spirituality existing before the material world and creating it by design. Western science acknowledges the subjective, non-dual experience, and its multiple beneficial effects, however, the interpretation of spirituality designing the material universe is in disagreement with the Darwinian Theory of mutation and selection. A design should create an ideal universe without the injustice of 3% congenital birth defects and later genetic health problems. The western viewpoint of selection is more adapted to explain congenital errors. The gap between subjectivity and objectivity, the mind-body problem, is in eastern philosophy reduced to the dominance of subjectivity over objectivity, whereas western science attributes equal values to both. Nevertheless, there remains an astonishing complementarity between eastern and western practices. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

Dr. Strange sees Dr. Stephen Strange abandon his once-promising medical career to become a superhero with the ability to warp time and space, and to travel through various dimensions. In order to make this transition, he is required to abandon many of his previous assumptions about the way the world works and learn to see things in a new way. Importantly, this is not merely a matter of learning a few facts, or of mastering new techniques. Instead, Dr. Strange (...) is required to alter his conception of the basic nature of the world and of how he relates to it. In time, this change extends to his values as well, as Strange comes to embrace his role in safeguarding the human world from interdimensional threats. -/- It is tempting to interpret Dr. Strange’s experience as one that shows the limits of rational, scientific inquiry, or even of human knowledge more generally. However, in this essay, I’d like to explore a different interpretation: that Strange’s experience can most fruitfully be thought of as a scientific revolution, in which Strange moves from one way of carrying out scientific inquiry to a different, incompatible way of doing it. In order to do this, I’ll be exploring the work of philosopher and historian of science Thomas Kuhn, who wrote a book—The Structure of Scientific Revolutions--about just this topic. Among other things, Kuhn’s work introduced the highly useful (though commonly misunderstood) notion of a paradigm shift. -/- We’ll begin by exploring what Kuhn describes as normal science, which consists of applying a set of well-understood concepts and methods to problems of interest. This “puzzle-solving” aspect of science is exemplified by Strange’s early successes in medicine. During this stage, scientists have good reasons to avoid questioning the basic validity of their paradigm, and to instead blame any problems that arise on other factors, such as human error. (As fans will know, Strange is quite good at assigning blame in just this way.) A crisis only occurs when significant, serious anomalies have accrued, such as those that Strange encounters in the aftermath of his accident. Even then, however, the old paradigm will only be abandoned only if a new paradigm can be found. Again, Strange’s experience bears this out, as he is able to move on only when the Sorcerer Supreme introduces him to the Mystic Arts. -/- Along the way, we’ll consider what it means for a practice to count as a scientific paradigm, and why it’s not just “anything goes.” Among other things, a paradigm requires that practitioners agree on which problems are most important, which techniques are appropriate to which problems, and what exemplary models of successful work look like. The Mystic Arts of Strange’s world, unlike the pseudoscientific theories of our own, plausibly do quite well on these sorts of criteria. (shrink)

Although the impact of so-called “sponsorship bias” has been the subject of increased attention in the philosophy of science, what exactly constitutes its epistemic wrongness is still debated. In this paper, I will argue that neither evidential accounts nor social–epistemological accounts can fully account for the epistemic wrongness of sponsorship bias, but there are good reasons to prefer social–epistemological to evidential accounts. I will defend this claim by examining how both accounts deal with a paradigm case from medical epistemology, (...) recently discussed in a paper by Bennett Holman. I will argue that evidential accounts cannot adequately capture cases of sponsorship bias that involve the manufacturing of certainty because of their neutrality with respect to the role of non-epistemic values in scientific practice. If my argument holds, it further highlights the importance of integrating social and ethical concerns into epistemological analysis, especially in applied contexts. One can only properly grasp sponsorship bias as an epistemological problem if one resists the methodological tendency to analyze social, ethical, and epistemological issues in isolation from each other. (shrink)

Importance: Patients with remitted major depressive disorder (MDD) were previously found to display abnormal functional magnetic resonance imaging connectivity (fMRI) between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions relative to emotions related to blaming others (eg, "indignation or anger toward others"). This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, "feeling guilty for everything"), known to have a key role in cognitive (...) vulnerability to MDD. It is unknown whether this neural signature predicts risk of recurrence, a crucial step in establishing its potential as a prognostic biomarker, which is urgently needed for stratification into pathophysiologically more homogeneous subgroups and for novel treatments. -/- Objective: To use fMRI in remitted MDD at baseline to test the hypothesis that RSATL-SCSR connectivity for self-blaming relative to other-blaming emotions predicts subsequent recurrence of depressive episodes. -/- Design, setting, and participants: A prospective cohort study from June 16, 2011, to October 10, 2014, in a clinical research facility completed by 75 psychotropic medication-free patients with remitted MDD and no relevant comorbidity. In total, 31 remained in stable remission, and 25 developed a recurring episode over the 14 months of clinical follow-up and were included in the primary analysis. Thirty-nine control participants with no personal or family history of MDD were recruited for further comparison. -/- Main outcomes and measures: Between-group difference (recurring vs stable MDD) in RSATL connectivity, with an a priori SCSR region of interest for self-blaming vs other-blaming emotions. -/- Results: We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence. The recurring MDD group showed higher connectivity than the stable MDD group (familywise error-corrected P < .05 over the a priori SCSR region of interest) and the control group. In addition, the recurring MDD group also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy. -/- Conclusions and relevance: To our knowledge, this study is the first to provide a robust demonstration of an fMRI signature of recurrence risk in remitted MDD. Additional studies are needed for its further optimization and validation as a prognostic biomarker. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are improving (...) imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

Recently, I argued that human subjects in artificial wombs ‘share the same moral status as newborns’ and so, deserve the same treatment and protections as newborns. This thesis rests on two claims: subjects of partial ectogenesis—those that develop in utero for at time before being transferred to AWs—are newborns and subjects of complete ectogenesis—those who develop in AWs entirely—share the same moral status as newborns. In response, Elizabeth Chloe Romanis argued that the subject in an AW is ‘a unique human (...) entity…rather than a fetus or a newborn’. She provides four lines of response to my essay. First, she argues that I have ‘misconstrued’ what birth is. Once we correct that error, it becomes clear that subjects of partial ectogenesis have not been born. Second, she argues that my claims imply that non-implanted embryos ‘would also be “born”’. But that is absurd. Third, she claims I fail to ‘meaningfully respond’ to distinctions she draws between subjects of ectogenesis and neonates. Finally, she criticises my essay for focusing on subjects of AWs rather than focusing on pregnant persons. I respond to each of these charges. In doing so, I reaffirm that some subjects of ectogenesis are newborns and all subjects of ectogenesis—even those that have not been born—share the same moral status as newborns. (shrink)

Recently, I argued that human subjects in artificial wombs (AWs) “share the same moral status as newborns” and so, deserve the same treatment and protections as newborns. This thesis rests on two claims: (A) “Subjects of partial ectogenesis—those that develop in utero for at time before being transferred to AWs—are newborns,” and (B) “Subjects of complete ectogenesis—those who develop in AWs entirely—share the same moral status as newborns.” In response, Elizabeth Chloe Romanis argued that the subject in an AW is (...) “a unique human entity…rather than a fetus or a newborn.” She provides four lines of response to my essay. First, she argues that I have “misconstrued” what birth is. Once we correct that error, it becomes clear that subjects of partial ectogenesis have not been born. Second, she argues that my claims imply that non-implanted embryos (existing in vivo) “would also be ‘born.’” But that is absurd. Third, she claims I fail to “meaningfully respond” to distinctions she draws between subjects of ectogenesis and neonates. Finally, she criticizes my essay for focusing on subjects of AWs rather than focusing on pregnant persons (who should be at the “centre” of debates over AWs). I respond to each of these charges. In doing so, I reaffirm that (contra Romanis) some subjects of ectogenesis are newborns and all subjects of ectogenesis—even those that have not been born—share the same moral status as newborns. (shrink)

Recent years have seen the rise of ‘Just Culture’ as an ideal in the patient safety movement, with numerous hospitals and professional organisations adopting a Just Culture response to incidents ranging from non-culpable human error to intentional misconduct. This paper argues that there is a deep problem with the Just Culture model, resulting from its impoverished understanding of the value of punitive, fundamentally backward-looking, practices of holding people accountable. I show that the kind of ‘accountability’ and ‘punishment’ contemporary Just (...) Culture advocates endorse disrespects both patients and providers. I claim, first, that punishment is good because it respects participants in the healthcare system by restoring an equilibrium of social and moral status that wrongdoing disturbs, and, second, that it only does so when it communicates a backward-looking message of resentful blame. (shrink)

Anna Smajdor and Joona Räsänen argue that we have good reason to classify pregnancy as a disease. They discuss five accounts of disease and argue that each account either implies that pregnancy is a disease or, if it does not, it faces problems. This strategy allows Smajdor and Räsänen to avoid articulating their own account of disease. Consequently, they cannot establish that pregnancy is a disease, only that plausible accounts of disease suggest this. Some readers will dismiss Smajdor and Räsänen’s (...) claims as counterintuitive. By analogy, if a mathematical proof concludes ‘2+2=5’, readers will know—without investigation—that an error occurred. Rather than dismiss Smajdor and Räsänen’s work, however, the easiest way to undermine their argument is to describe at least one plausible account of disease that (i) excludes pregnancy and (ii) avoids the problems they raise for it. This is our strategy. We focus on dysfunction accounts of disease. After outlining Smajdor and Räsänen’s main arguments against dysfunction accounts, we explain why pregnancy is not a disease on these accounts. Next, we defend dysfunction accounts against the three problems that Smajdor and Räsänen raise. If successful, then contra Smajdor and Räsänen, at least one plausible account of disease does not imply that pregnancy is a disease. We suspect that defenders of other accounts can respond similarly. Yet, we note that insofar as dysfunction accounts align with the commonsense intuition that pregnancy is not a disease, this, all else being equal, seems like a point in their favour. (shrink)

It is generally unquestioned that human subjects research review boards should assess the ethical acceptability of protocols. It says so right on the tin, after all: they are explicitly called research ethics committees in the UK. But it is precisely those sorts of unchallenged assumptions that should, from time to time, be assessed and critiqued, in case they are in fact unfounded. John Stuart Mill's objection to suppressers of dissent is instructive here: “If the opinion is right, they are deprived (...) of the opportunity of exchanging error for truth: if wrong, they lose, what is almost as great a benefit, the clearer perception and livelier impression of truth, produced by its collision with error”.1 Andrew Moore and Andrew Donnelly's compelling article, “The job of ‘ethics committees’”, is just this sort of challenge to conventional wisdom, a rejection of the notion that review boards should be considering ethical matters at all.2 If true, we should be substantially reforming our research ethics policies. And if false, rebuttals would help strengthen and ground the otherwise unquestioned assumption of the role of ethics committees. As it happens, I will argue that their critiques fall into the second category. While mistaken, they present a valuable opportunity to clarify the role of ethics committees and their relationship to relevant codes. In particular, I will defend a hybrid account where codes have strict primacy, but leave significant room for review boards …. (shrink)

Moral error theory of the kind defended by J. L. Mackie and Richard Joyce is premised on two claims: (1) that moral judgements essentially presuppose that moral value has absolute authority, and (2) that this presupposition is false, because nothing has absolute authority. This paper accepts (2) but rejects (1). It is argued first that (1) is not the best explanation of the evidence from moral practice, and second that even if it were, the error theory would still (...) be mistaken, because the assumption does not contaminate the meaning or truth-conditions of moral claims. These are determined by the essential application conditions for moral concepts, which are relational rather than absolute. An analogy is drawn between moral judgements and motion judgements. (shrink)

Normative error theorists aim to defend an error theory which says that normative judgments ascribe normative properties, and such properties, including reasons for belief, are never instantiated. Many philosophers have raised objections to defending a theory which entails that we cannot have reason to believe it. Spencer Case objects that error theorists simply cannot avoid self-defeat. Alternatively, Bart Streumer argues that we cannot believe normative error theory but that, surprisingly, this helps its advocates defend it against (...) these objections. I think that if Streumer’s argument is successful, it provides error theorists an escape from Case’s self-defeat objection. However, I build upon and improve Case’s argument to show that we could never even successfully defend normative error theory whether we can believe it or not. So, self-defeat remains. I close by offering some reasons for thinking our inability to defend normative error theory means that we should reject it, which, in turn, would mean that it’s false. (shrink)

In this paper I defend what I call the argument from epistemic reasons against the moral error theory. I argue that the moral error theory entails that there are no epistemic reasons for belief and that this is bad news for the moral error theory since, if there are no epistemic reasons for belief, no one knows anything. If no one knows anything, then no one knows that there is thought when they are thinking, and no one (...) knows that they do not know everything. And it could not be the case that we do not know that there is thought when we believe that there is thought and that we do not know that we do not know everything. I address several objections to the claim that the moral error theory entails that there are no epistemic reasons for belief. It might seem that arguing against the error theory on the grounds that it entails that no one knows anything is just providing a Moorean argument against the moral error theory. I show that even if my argument against the error theory is indeed a Moorean one, it avoids Streumer's, McPherson's and Olson's objections to previous Moorean arguments against the error theory and is a more powerful argument against the error theory than Moore's argument against external world skepticism is against external world skepticism. (shrink)

Error Management Theory (EMT) suggests that cognitive adaptations evolve to minimize the cost of false negative and false positive errors in detections of consequential environmental conditions. These adaptations manifest as biases tailored to specific environmental conditions. This paper proposes that the same selection pressure fostered the evolution of a self-biasing ability, allowing us to minimize such costs based on experience and culturally transmitted information. The research indicates that this ability specifically applies to productions of belief or doubt about the (...) existence of an environmental condition that is not adequately perceptible to the senses, e.g., doubt that the man is being honest. A model of this self-biasing process, rooted in signal detection theory, is developed and its explanatory reach is demonstrated through many diverse examples from epistemological and social psychological literatures. These examples feature an evidentiary standard for belief that appears too high or low, or seems to have modulated. This study aims to correct and to significantly enhance EMT by introducing this evolved self-biasing ability. (shrink)

Moral error theories claim that (i) moral utterances express moral beliefs, that (ii) moral beliefs ascribe moral properties, and that (iii) moral properties are not instantiated. Thus, according to these views, there seems to be conclusive evidence against the truth of our ordinary moral beliefs. Furthermore, many error theorists claim that, even if we accepted moral error theory, we could still in principle keep our first-order moral beliefs. This chapter argues that this last claim makes many popular (...) versions of the moral error theory incompatible with the standard philosophical accounts of beliefs. Functionalism, normative theories of beliefs, representationalism, and interpretationalism all entail that being sensitive to thoughts about evidence is a constitutive feature of beliefs. Given that many moral error theorists deny that moral beliefs have this quality, their views are in a direct conflict with the most popular views about the nature of beliefs. (shrink)

The paper explores the consequences of adopting a moral error theory targeted at the notion of reasonable convergence. I examine the prospects of two ways of combining acceptance of such a theory with continued acceptance of moral judgements in some form. On the first model, moral judgements are accepted as a pragmatically intelligible fiction. On the second model, moral judgements are made relative to a framework of assumptions with no claim to reasonable convergence on their behalf. I argue that (...) the latter model shows greater promise for an error theorist whose commitment to moral thought is initially serious. (shrink)

Error theories about morality often take as their starting point the supposed queerness of morality, and those resisting these arguments often try to argue by analogy that morality is no more queer than other unproblematic subject matters. Here, error theory (as exemplified primarily by the work of Richard Joyce) is resisted first by arguing that it assumes a common, modern, and peculiarly social conception of morality. Then error theorists point out that the social nature of morality requires (...) one to act against one's self-interest while insisting on the categorical, inescapable, or overriding status of moral considerations: they argue that morality requires magic, then (rightly) claim that there is no such thing as magic. An alternate eudaimonist conception of morality is introduced which itself has an older provenance than the social point of view, dating to the ancient Greeks. Eudaimonism answers to the normative requirements of morality, yet does not require magic. Thus, the initial motivation for error theory is removed. (shrink)

Moral error theory is the doctrine that our first-order moral commitments are pervaded by systematic error. It has been objected that this makes the error theory itself a position in first-order moral theory that should be judged by the standards of competing first-order moral theories :87–139, 1996) and Kramer. Kramer: “the objectivity of ethics is itself an ethical matter that rests primarily on ethical considerations. It is not something that can adequately be contested or confirmed through non-ethical (...) reasoning” [2009, 1]). This paper shows that error theorists can resist this charge if they adopt a particular understanding of the presuppositions of moral discourse. (shrink)

Pharmaceuticals or other emerging technologies could be used to enhance (or diminish) feelings of lust, attraction, and attachment in adult romantic partnerships. While such interventions could conceivably be used to promote individual (and couple) well-being, their widespread development and/or adoption might lead to “medicalization” of human love and heartache—for some, a source of serious concern. In this essay, we argue that the “medicalization of love” need not necessarily be problematic, on balance, but could plausibly be expected to have either good (...) or bad consequences depending upon how it unfolds. By anticipating some of the specific ways in which these technologies could yield unwanted outcomes, bioethicists and others can help direct the course of love’s “medicalization”—should it happen to occur—more toward the “good” side than the “bad.”. (shrink)

Proponents of the epistemic companions in guilt argument argue that we should reject the moral error theory because it entails that there are no epistemic reasons. In this paper, I investigate whether a plausible version of the moral error theory can be constructed that does not entail an error theory about epistemic reasons. I argue that there are no irreducibly normative second-personal reasons even if there are irreducibly normative reasons. And epistemic reasons are not second-personal reasons. In (...) this case, a plausible version of the moral error theory can be constructed that does not entail an error theory about epistemic reasons if facts and claims about morality entail facts and claims about irreducibly normative second-personal reasons. And, as I explain, there is a good case that facts and claims about morality do entail facts and claims about irreducibly normative second-personal reasons. (shrink)

Technology is a specific form of human agency that yields to (an imperfect) realization of human control over a technological situation-that is, a situation not governed to an end by natural constraints but by specific human aims. In this view, technology can be considered the only way of producing artificial beings. However, all technology is finite by nature, which means that sooner or later, all technology will fail, break down, and go wrong. The fate of all technologies and artificial beings (...) produced by technologies is finitude. Human beings are artificial beings, in this way human existence is also finite. (shrink)

This paper surveys contemporary accounts of error theory and fictionalism. It introduces these categories to those new to metaethics by beginning with moral nihilism, the view that nothing really is right or wrong. One main motivation is that the scientific worldview seems to have no place for rightness or wrongness. Within contemporary metaethics there is a family of theories that makes similar claims. These are the theories that are usually classified as forms of error theory or fictionalism though (...) there are different ways of accepting some form of the view that nothing is really write or wrong. A range of different ways of going in the light of such a realization is also proposed. The resulting taxonomy of positions is quite complicated and sometimes surprising. One surprise will be that some positions plausibly classified as error theories or forms of fictionalism do not quite seem to be forms of nihilism. (shrink)

Moral error theorists hold that morality is deeply mistaken, thus raising the question of whether and how moral judgments and utterances should continue to be employed. Proposals include simply abolishing morality, adopting some revisionary fictionalist stance toward morality, and conserving moral judgments and utterances unchanged. I defend a fourth proposal, namely revisionary moral expressivism, which recommends replacing cognitivist moral judgments and utterances with non-cognitivist ones. Given that non-cognitivist attitudes are not truth apt, revisionary expressivism does not involve moral (...) class='Hi'>error. Moreover, revisionary expressivism has the theoretical resources to retain many of the useful features of morality, such as moral motivation, moral disagreement, and moral reasoning. Revisionary expressivism fares better than the three major alternatives in both avoiding moral error and preserving these useful features of morality. I also show how this position differs from the “revolutionary expressivism” of Sebastian Köhler and Michael Ridge. (shrink)

This paper critically evaluates Foucault’s relation to Bachelard and Canguilhem. It reconsiders the relevance of the concept of “influence” for treating this relation in order to register the more sceptical position Foucault adopts towards knowledge practices than either of these figures from twentieth-century French epistemology.

Estimating word complexity is a well-established task in computer-assisted language learning. So far, however, complexity estimation has been largely limited to comprehension. This neglects words that are easy to comprehend, but hard to produce. We introduce semantic error prediction (SEP) as a novel task that assesses the production complexity of content words. Given the corrected version of a learner-produced text, a system has to predict which content words replace tokens from the original text. We present and analyse one example (...) of such a semantic error prediction dataset, which we generate from an error correction dataset. As neural baselines, we use BERT, RoBERTa, and LLAMA2 embeddings for SEP. We show that our models can already improve downstream applications, such as predicting essay vocabulary scores. (shrink)

Recently, companions in guilt strategies have garnered significant philosophical attention as a response to arguments for moral error theory, the view that there are no moral facts and that our moral beliefs are thus systematically mistaken. According to Cuneo (The normative web: an argument for moral realism, Oxford University Press, Oxford, 2007), Das (Philos Q 66:152–160, 2016; Australas J Philos 95(1):58–69, 2017), Rowland (J Ethics Soc Philos 7(1):1–24, 2012; Philos Q 66:161–171, 2016) and others, epistemic facts would be just (...) as metaphysically problematic (or ‘guilty’) as moral facts. But since epistemic error theory is implausible, arguments for moral error theory prove too much and should be rejected. My aim is to argue that the success of this strategy is limited. In particular, the companions in guilt response fails against error-theoretic arguments motivated by concerns about explanatory dispensability, as recently developed by Joyce (The evolution of morality, MIT press, Bradford, 2005) and Olson (Moral error theory: history, critique, defence, Oxford University Press, Oxford, 2014: Ch. 7). To succeed, the response would require a prima facie plausible argument to the effect that epistemic facts are metaphysically dubious because they, too, are explanatorily dispensable. But, as I show, any such argument proves self-effacing: its premise commits us to believing in epistemic facts, while its conclusion forces us to deny their existence. Consequently, companions in guilt strategies don’t offer a panacea against arguments for moral error theory. (shrink)

Many contemporary philosophers rate error theories poorly. We identify the arguments these philosophers invoke, and expose their deficiencies. We thereby show that the prospects for error theory have been systematically underestimated. By undermining general arguments against all error theories, we leave it open whether any more particular arguments against particular error theories are more successful. The merits of error theories need to be settled on a case-by-case basis: there is no good general argument against (...) class='Hi'>error theories. (shrink)