The General Medical Council renewed its guidance on consent in 2020. In this essay, I argue that the 2020 guidance does not advance on the earlier, 2008 guidance in regard to treatments that doctors are obliged to offer to patients. In both, doctors are instructed to not provide treatments that are not in the overall benefit, or clinical interests, of the patient; although, patients are absolutely entitled to decline treatment. As such, consent has two aspects, and different standards apply to each aspect. To explore this paradigm, I propose the reconceptualisation of consent as a person's freedom to achieve treatment, using Amartya Sen's approach. Sen explains that freedom has two aspects: process and opportunity. Accordingly, a patient's freedom to achieve treatment would comprise a process for the identification of proper treatment, followed by an opportunity for the patient to accept or decline this treatment. As per Sen, the opportunity aspect is to be assessed by the standard of public reason, whereas the standard for the process aspect is variable and contingent on the task at hand. I then use this reconceptualised view of consent to analyse case law. I show that senior judges have conceived the patient's opportunity to be encompassed in information, which is to be decided by public reason. On the other hand, the process aspect relies on the private reason of medical professionals. Given the nature of professionalism, this reliance is inescapable, and it is maintained in the case law that is cited in both guidances.
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http://dx.doi.org/10.1136/medethics-2021-107347 | DOI Listing |
Ann Surg Oncol
January 2025
Department of Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan.
Background: Tumor size (TS) in pancreatic ductal adenocarcinoma (PDAC) is one of the most important prognostic factors. However, discrepancies between TS on preoperative images (TSi) and pathological specimens (TSp) have been reported. This study aims to evaluate the factors associated with the differences between TSi and TSp.
View Article and Find Full Text PDFGeroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Brain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
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