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Incidence and prevalence of traumatic and non-traumatic wounds and burns and access to wound care in Sierra Leone; data from a nationwide household survey (PRESSCO) 2020.
Heliyon
January 2025
Masanga Medical Research Unit, Masanga, Sierra Leone.
Objectives: This wound section of the PREvalence Study on Surgical COnditions (PRESSCO) determines the incidence and prevalence of wounds and burns in Sierra Leone. It further describes access to wound care and wound-related healthcare-seeking behaviour.
Methods: Between October 2019 and March 2020, a nationwide cross-sectional household survey was performed.
'It Cuts Both Ways': A Qualitative Analysis of Stakeholders' Views on the Emerging and Potential Unintended Consequences of Telehealth in Rural Australia.
J Eval Clin Pract
February 2025
Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, North Ryde, Australia.
Rationale: Telehealth has been consistently viewed as a viable solution for addressing healthcare inaccessibility and mitigating the impact of health workforce shortages in rural areas. However, despite high utilisation in rural areas, little is known about the unintended consequences of telehealth in terms of unexpected benefits and drawbacks.
Aims And Objectives: This study aimed to investigate the unintended consequences of telehealth in rural Australia.
Robotic Assistance Is Not Associated With Decreased Early Revisions in Cementless TKA: An Analysis of the American Joint Replacement Registry.
Clin Orthop Relat Res
November 2024
Department of Orthopaedic Surgery, Johns Hopkins Medicine, Columbia, MD, USA.
Background: Previously, we conducted a retrospective study of American Joint Replacement Registry (AJRR) data that examined the 2-year odds of revision between robotic-assisted and nonrobotic-assisted TKA, and we found no benefit to robotic assistance. However, proponents of robotic assistance have suggested that robot platforms confer more accurate bone cuts and precise implant sizing that might promote osteointegration of cementless implants by limiting micromotion at the bone-implant interface that could lead to aseptic loosening. Therefore, it seems important specifically to evaluate the odds of revision among patients with cementless implants only within our previous study population.
View Article and Find Full Text PDFUnbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.
Eur Radiol
November 2024
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
Objective: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).
Methods: Variable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting.
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