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Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey.
J Med Internet Res
January 2025
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Current literature is unclear on the safety and optimal timing of delivery for pregnant individuals with gestational diabetes mellitus, which inspired our study team to conduct a web-based survey study exploring patient and provider opinions on delivery options. However, an incident of fraudulent activity with survey responses prompted a shift in the focus of the research project. Unfortunately, despite the significant rise of web-based surveys used in medical research, there remains very limited evidence on the implications of and optimal methods to handle fraudulent web-based survey responses.
View Article and Find Full Text PDFManagement of patients with urinary incontinence in senior age.
Cent Eur J Public Health
December 2024
Department of Public Health and Hygiene, Faculty of Medicine, Pavol Jozef Safarik University in Kosice, Kosice, Slovak Republic.
Objectives: Urinary incontinence is an extremely stressful and often debilitating disease, increasing morbidity in society. The aim of the work is to point out the problems of the management of incontinent patients - seniors in the context of their quality of life as well as treatment costs to find ways to make the widest possible public awareness of the fact that in most cases incontinence is solvable in terms of improving the quality of life.
Methods: The group consisted of 100 patients with urinary incontinence who were treated with conservative medical procedures at the urological outpatient clinic of the Railway Hospital in Košice.
First-time use of electronic patient-reported outcome measures in a cluster randomized trial: a qualitative study.
J Patient Rep Outcomes
January 2025
Department of Health Sciences Ålesund, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Ålesund, Norway.
Background: Although there is clear evidence supporting the beneficial effects of regularly assessing patient-reported outcomes (PROs), the comprehensive integration of patient-reported outcome measures (PROMs) into routine cancer care remains limited. This study aimed to explore the facilitators and barriers encountered by principal investigators (PIs) (oncologists) and study nurses during the implementation of the Eir ePROM within a cluster randomized trial (c-RCT) in cancer outpatient clinics. Additionally, we sought to examine the influence of Eir on the working routines of the participants.
View Article and Find Full Text PDFClinical and radiographic outcomes of extra-short implants (≤ 6 mm) in the posterior atrophic jaws: a retrospective cohort study.
Int J Implant Dent
January 2025
Department of Neurosciences, School of Dentistry, University of Padua, Padua, Italy.
Objective: This study aimed at investigating implant survival rate and marginal bone loss (MBL) around extra-short implants. The impact of the loading protocol and of the use of an intermediate abutment was also evaluated, to explore possible differences in terms of the outcome measures.
Materials And Methods: Patients with single or multiple mandibular or maxillary posterior edentulism rehabilitated using extra-short 5-6 mm long implants were included.
Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture.
Int J Comput Assist Radiol Surg
January 2025
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Purpose: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).
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