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.
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.
BackgroundTransient 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).MethodsMulti-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).ResultsThree 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).ConclusionsA 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 evidenceThis study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.
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