The accreditation of sleep centres aims to ensure high-quality diagnosis and management of sleep centres. European accreditation standards were introduced in 2006, and were aimed at centres offering inpatient polysomnography and vigilance tests (Mean Sleep Latency Test and Maintenance of Wakefulness Test). Since then, the practice of sleep medicine has evolved, with greater use of ambulatory polysomnography and polygraphy. As a result, in many sleep centres, actual clinical practice, although of a high standard, is no longer in accordance with the published guidelines. The current criteria have been revised with the introduction of level-based criteria. Level 1 and 2 centres offer full diagnostic testing in a laboratory-based setting. Level 1 practices will usually be university affiliated, and have a full teaching and active research role. Level 3 and 4 practices may offer both inpatient and ambulatory testing. Level 3 practices perform polysomnography, while level 4 practices (usually monodisciplinary and focussed on sleep apnea) perform polygraphy only. The role of the medical and paramedical team, training, appropriate equipment, patient care pathways and patient management according to national/European recommendations is underlined for accreditation at each level. It is anticipated that the guidelines will be reviewed and if necessary revised after 4 years.
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http://dx.doi.org/10.1111/jsr.14200 | DOI Listing |
Sci Rep
December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
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December 2024
School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NZ, UK.
Worldwide museums hold collections of eggshells representing material for descriptive studies. However, an obstacle to this is the lack of information about the original contents and weight of the entire egg (W). This study aimed to fill this gap though development of a methodological mechanism for calculating the volume of the egg interior (V), its density (D) and W.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Pediatrics and Child Health Nursing, College of Medicine and Health Sciences, Bahir Dar, Ethiopia.
Introduction: Insecticide-treated bed nets are often used as a physical barrier to prevent infection of malaria. In Sub-Saharan Africa, one of the most important ways of reducing the malaria burden is the utilization of insecticide-treated bed nets. However, there is no sufficient information on the utilization of insecticide-treated bed nets and their associated factors in Ethiopia.
View Article and Find Full Text PDFHealth Expect
February 2025
School of Nursing, McMaster University, Hamilton, Ontario, Canada.
Introduction: The transition from paediatric to adult health care (i.e., 'health care transition') poses many challenges for youth with medical complexity (YMC) and their families.
View Article and Find Full Text PDFJ Dtsch Dermatol Ges
December 2024
Department of Dermatology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: Basal cell carcinoma (BCC) is a prevalent type of skin cancer in which the inherent subjectivity of dermoscopy poses diagnostic challenges. Existing AI systems, which provide mainly image-level insights, lack the interpretability that is crucial for effective clinical decisions and patient education.
Patients And Methods: Our study developed a refined BCC dataset from the Human‒Machine Adversarial Model (HAM10000), which was annotated by clinicians to identify key diagnostic features.
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