Articles: cations.
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Sleep disorders are common in people with Parkinson's disease. These disorders, which increase in frequency throughout the course of the neurodegenerative disease and impair quality of life, include insomnia, excessive daytime sleepiness, circadian disorders, obstructive sleep apnoea, restless legs syndrome, and rapid eye movement (REM) sleep behaviour disorder. The causes of these sleep disorders are complex and multifactorial, including the degeneration of the neural structures that modulate sleep, the detrimental effect of some medications on sleep, the parkinsonian symptoms that interfere with mobility and comfort in bed, and comorbidities that disrupt sleep quality and quantity. ⋯ The management of patients with Parkinson's disease and a sleep disorder is challenging and should be individualised. Treatment can include education aiming at changes in behaviour (ie, sleep hygiene), cognitive behavioural therapy, continuous dopaminergic stimulation at night, and specific medications. REM sleep behaviour disorder can occur several years before the onset of parkinsonism, suggesting that the implementation of trials of neuroprotective therapies should focus on people with this sleep disorder.
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Intraoperative MRI (iMRI) made its debut to great fanfare in the mid-1990s. However, the enthusiasm for this technology with seemingly obvious benefits for neurosurgeons has waned. We review the benefits and utility of iMRI across the field of neurosurgery and present an overview of the evidence for iMRI for multiple neurosurgical disciplines: tumor, skull base, vascular, pediatric, functional, and spine. ⋯ Evidence for iMRI use varies greatly by specialty, with the most evidence for tumor, vascular, and pediatric neurosurgery. The benefits of real-time anatomic imaging, a lack of radiation, and evaluation of surgical outcomes are limited by the cost and difficulty of iMRI integration. Nonetheless, the ability to ensure patients are provided by a maximal yet safe treatment that specifically accounts for their own anatomy and highlights why iMRI is a valuable and underutilized tool across multiple neurosurgical subspecialties.
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OSA is a widespread condition that significantly affects both health and health-related quality of life (HRQoL). If left untreated, OSA can lead to accidents, decreased productivity, and medical complications, resulting in significant economic burdens including the direct costs of managing the disorder. Given the constraints on health care resources, understanding the cost-effectiveness of OSA management is crucial. A key factor in cost-effectiveness is whether OSA therapies reduce medical costs associated with OSA-related complications. ⋯ OSA management is cost-effective, although uncertainties persist regarding the therapy's impact on medical costs. Future studies should focus on reducing bias, particularly the healthy adherer effect, and addressing other confounding factors to clarify potential medical cost savings. Promising avenues to further understanding include using quasiexperimental designs, incorporating more sophisticated characterization of OSA severity and symptoms, and leveraging newer technologies (eg, big data, wearables, and artificial intelligence).
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Artificial intelligence (AI) is increasingly being used in health care. Without an ethically supportable, standard approach to knowing when patients should be informed about AI, hospital systems and clinicians run the risk of fostering mistrust among their patients and the public. Therefore, hospital leaders need guidance on when to tell patients about the use of AI in their care. ⋯ To determine which AI technologies fall into each of the identified categories (no notification or no informed consent [IC], notification only, and formal IC), we propose that AI use-cases should be evaluated using the following criteria: (1) AI model autonomy, (2) departure from standards of practice, (3) whether the AI model is patient facing, (4) clinical risk introduced by the model, and (5) administrative burdens. We take each of these in turn, using a case example of AI in health care to illustrate our proposed framework. As AI becomes more commonplace in health care, our proposal may serve as a starting point for creating consensus on standards for notification and IC for the use of AI in patient care.
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In this study, we describe the development and validation of a revised Pediatric Chronic Pain Grading (P-CPG) for children aged 8 to 17 years that adds emotional impairment to previously used measures of pain intensity and functional impairment. Such a measure enables the assessment of chronic pain severity in different epidemiological and clinical populations, the stratification of treatment according to pain severity, and the monitoring of treatment outcome. The P-CPG was developed using a representative sample of school children with chronic pain (n = 454; M age = 12.95, SD = 2.22). ⋯ Convergent validity was demonstrated by significant positive correlations between the P-CPG and global ratings of pain severity as well as objective claims data; the latter reflects greater health care costs with increasing P-CPG scores. Sensitivity to change was supported by a significant reduction in baseline P-CPG grades 3 and 6 months after intensive interdisciplinary pain treatment in tertiary care sample. In conclusion, the P-CPG is an appropriate measure of pain severity in children and adolescents with chronic pain in clinical as well as epidemiological settings.