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Quality of referrals and adherence to guidelines for adult patients with minimal to moderate head injuries in a selection of Norwegian hospitals.
Eur J Trauma Emerg Surg
January 2025
Department of Health Sciences, Norwegian University of Science and Technology (NTNU), Postbox 191, Gjøvik, 2802, Norway.
Purpose: This study aimed to assess adherence to the Scandinavian guidelines, the justification of referrals, and the quality of referrals of patients with mild, minimal, and moderate head injuries in a selection of Norwegian hospitals.
Methods: We collected 283 head CT referrals for head trauma patients at one hospital trust in Norway in 2022. The data included the patients' sex, age, and the referral text.
A Varying-Coefficient Additive Hazard Model for Recurrent Events Data.
Stat Med
February 2025
Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore.
The additive hazard model, which focuses on risk differences rather than risk ratios, has been widely applied in practice. In this paper, we consider an additive hazard model with varying coefficients to analyze recurrent events data. The model allows for both varying and constant coefficients.
View Article and Find Full Text PDFOperating Characteristics of the Simulated Healthy Participant Approach in Impaired Clearance Studies.
AAPS J
January 2025
Pharmacometrics and Systems Pharmacology, Pfizer, Groton, Connecticut, U.S.A..
Minimizing harm is a cornerstone of ethical research practices. A drug that has undergone extensive clinical pharmacological testing in healthy participants (HPs) and a diverse selection of patients can be described with a sufficiently predictive population pharmacokinetic (PopPK) model. In impaired clearance trials, recruitment is minimized and underpowered for all but major exposure differences.
View Article and Find Full Text PDFParticipant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.
J Med Internet Res
January 2025
Biomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.
View Article and Find Full Text PDFDeep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis.
JHEP Rep
February 2025
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
Background & Aims: Biliary abnormalities in autoimmune hepatitis (AIH) and interface hepatitis in primary biliary cholangitis (PBC) occur frequently, and misinterpretation may lead to therapeutic mistakes with a negative impact on patients. This study investigates the use of a deep learning (DL)-based pipeline for the diagnosis of AIH and PBC to aid differential diagnosis.
Methods: We conducted a multicenter study across six European referral centers, and built a library of digitized liver biopsy slides dating from 1997 to 2023.
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