After virtue, a study in moral theory

After Virtue: A Study in Moral Theory

Good medical practice

Americans have always been the world's most anxiously enthusiastic consumers of "enhancement technologies." Prozac, Viagra, and Botox injections are only the latest manifestations of a familiar pattern: enthusiastic adoption, public hand-wringing, an occasional congressional hearing, and calls for self-reliance. In a brilliant diagnosis of our reactions to self-improvement technologies, Carl Elliott asks questions that illuminate deep currents in the American character: Why do we feel uneasy about these drugs, procedures, and therapies even while we embrace them? Where do we draw the line between self and society? Why do we seek self-realization in ways so heavily influenced by cultural conformity?

Enhancement as an American DilemmaBetter Than Well: American Medicine Meets the American Dream

Authors/Task Force Members: Michele Brignole* (Chairperson) (Italy), Angel Moya* (Co-chairperson) (Spain), Frederik J. de Lange (The Netherlands), Jean-Claude Deharo (France), Perry M. Elliott (UK), Alessandra Fanciulli (Austria), Artur Fedorowski (Sweden), Raffaello Furlan (Italy), Rose Anne Kenny (Ireland), Alfonso Mart ın (Spain), Vincent Probst (France), Matthew J. Reed (UK), Ciara P. Rice (Ireland), Richard Sutton (Monaco), Andrea Ungar (Italy), and J. Gert van Dijk (The Netherlands)

2018 ESC Guidelines for the diagnosis and management of syncope

Many critics of medicalization (the process by which phenomena become candidates for medical definition, explanation and treatment) express concern that the process privileges individualised, biologically grounded interpretations of medicalized phenomena, inhibiting understanding and communication of aspects of those phenomena that are less relevant to their biomedical modelling. I suggest that this line of critique views medicalization as a hermeneutical injustice—a form of epistemic injustice that prevents people having the hermeneutical resources available to interpret and communicate significant areas of their experience. Interpreting the critiques in this fashion shows they frequently fail because they: neglect the ways in which medicalization may not obscure, but rather illuminate, individuals’ experiences; and neglect the testimony of those experiencing first-hand medicalized problems, thus may be guilty of perpetrating testimonial injustice. However, I suggest that such arguments are valuable insofar as they highlight the unwarranted epistemic privilege frequently afforded to medical institutions and medicalized models of phenomena, and a consequent need for greater epistemic humility on the part of health workers and researchers.

Medicalization and epistemic injustice

Background Transient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses). Methods Multi-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports). Results Three hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%–92.6%]) of patients correctly in validation (100 [86.7%–100%] syncope, 85.7 [67.3%–96.0%] epilepsy, 75.0 [56.6%–88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%–86.2%)] in validation, 83.8 [68.0%–93.8%] syncope, 81.5 [61.9%–93.7%] epilepsy, 67.9 [47.7%–84.1%] PNES). Conclusions A tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates. Classification of evidence This study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.

Machine learning as a diagnostic decision aid for patients with transient loss of consciousness.

Felicitas Kraemer draws on the experiences of patients undergoing deep-brain stimulation (DBS) to propose two distinct and potentially conflicting principles of respect: for an individual's autonomy (interpreted as mental competence), and for their authenticity. I argue instead that, according to commonly-invoked justifications of respect for autonomy, authenticity is itself in part constitutive of an analysis of autonomy worthy of respect; Kraemer's argument thus highlights the shortcomings of practical applications of respect for autonomy that emphasise competence while neglecting other important dimensions of autonomy such as authenticity, since it shows that competence alone cannot be interpreted as a reliable indicator of an individual's capacity for exercising autonomy. I draw from relational accounts to suggest how respect for a more expansive conception of autonomy might be interpreted for individuals undergoing DBS and in general.

https://jme.bmj.com/content/medethics/40/8/563.full.pdf

Authenticity and autonomy in deep-brain stimulation

Relational Autonomy and the Ethics of Health Promotion

Background Transient loss of consciousness (TLOC) is a common presentation in primary care. Over 90% of these are due to epileptic seizures (ES), syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnosis rates are as high as 30%. Methods Systematic review of inter-ictal clinical criteria to aid differential diagnosis of TLOC. We searched Medline, EMBASE, CINAHL and PsycInfo databases, as well as relevant grey literature depositories and citations of relevant reviews and guidelines for studies giving sensitivity and specificity of inter-ictal clinical characteristics used to differentiate between causes of TLOC. Two independent reviewers selected studies for inclusion and performed critical appraisal of included articles. We performed a narrative synthesis of included studies. Results Of 1023 results, 16 papers were included. Two compared syncope, ES, and PNES; all others compared ES and PNES. All were at significant risk of bias in at least one domain. 6 studied patient symptoms, 6 medical and social history, 3 witness reports and 1 examination findings. No individual criterion differentiated between diagnoses with high sensitivity and specificity. Conclusions There is a lack of validated diagnostic criteria to help clinicians assessing patients in primary or emergency care settings to discriminate between common causes of TLOC. Performance may be improved by combining sets of criteria in a clinical decision rule, but no such rule has been validated prospectively against gold-standard diagnostic criteria.

Alistair Wardrope

Papers

Medicalization and epistemic injustice

Machine learning as a diagnostic decision aid for patients with transient loss of consciousness.

Authenticity and autonomy in deep-brain stimulation

Relational Autonomy and the Ethics of Health Promotion

Diagnostic criteria to aid the differential diagnosis of patients presenting with transient loss of consciousness: A systematic review