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Explaining cognitive function in multiple sclerosis through networks of grey and white matter features: a joint independent component analysis.
J Neurol
January 2025
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.
View Article and Find Full Text PDFImpact of worklist selection on point-of-care ultrasound workflow - a quality improvement project.
BMC Health Serv Res
January 2025
Department of Emergency Medicine, University of California, Irvine, Orange, CA, 92868, USA.
Background: Research demonstrates that Point-of-care ultrasound (POCUS) improves clinical outcomes for patients. Improving clinician satisfaction with POCUS should promote utilization into everyday practice, leading to improved clinical outcomes. Despite this benefit, there are still barriers to use including POCUS workflow.
View Article and Find Full Text PDFPredictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFChallenges of sharing individual participant data for secondary research on neglected tropical diseases: the experience of Drugs for Neglected Diseases initiative and a call for action.
BMJ Open
December 2024
Drugs for Neglected Diseases initiative, Geneva, Switzerland
Introduction: The Drugs for Neglected Diseases initiative (DNDi) is committed to maximising the scientific value of the individual participant data (IPD) it has collected during its 20 years of activity and the IPD it will collect in the future, while safeguarding research participants' privacy and their right to know how their data will be processed.
Objective: The objective of this article is to share what DNDi has learnt while working on its commitment to data sharing. It also aims to advance the debate about best practice in the research community to avoid 'IPD sharing paralysis', with a focus on multistakeholder projects involving patients and researchers based in countries with various levels of data privacy regulations and measures.
Background: In 2018, a nationwide survey carried out in 387 acute care hospitals from 16 out of 21 Italian regions, allowed defining an extended checklist for the participatory evaluation of person-centredness in hospital care. We aimed to validate a reduced set of core items for continuous use across the country.
Methods: Factor analysis was used to validate the construct of the checklist.
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