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Characteristics of eligible patients with knee osteoarthritis accepting versus declining participation in a randomised trial investigating the effect of weight loss versus knee arthroplasty to explore generalisability: A cross-sectional study.
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Department of Orthopedic Surgery and Traumatology, Copenhagen University Hospital-Bispebjerg & Frederiksberg, Copenhagen, Denmark.
Background: The INtensive diet versus Knee Arthroplasty (INKA) trial is a randomised trial assessing weight loss as an alternative to knee arthroplasty (KA) in obese patients with severe knee osteoarthritis (OA) awaiting KA (NCT05172843). The external validity of the INKA trial may be hampered if the patients who participate differ from those who decline participation.
Objective: To compare baseline characteristics between patients who enrol in the INKA trial and those who decline participation (i.
The sarcoma ring trial: a case-based analysis of inter-center agreement across 21 German-speaking sarcoma centers.
J Cancer Res Clin Oncol
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Sarcoma Unit, Department of Surgery, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Purpose: The management of soft tissue sarcoma (STS) at reference centers with specialized multidisciplinary tumor boards (MTB) improves patient survival. The German Cancer Society (DKG) certifies sarcoma centers in German-speaking countries, promoting high standards of care. This study investigated the variability in treatment recommendations for localized STS across different German-speaking tertiary sarcoma centers.
View Article and Find Full Text PDFMachine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.
Sci Rep
January 2025
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.
View Article and Find Full Text PDFMachine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.
BMC Musculoskelet Disord
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Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
Health commodity management information system (Dagu-2 Software) implementation status in public health facilities of South-western Oromia, Ethiopia: a mixed method study.
BMC Health Serv Res
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Logistics Education (LEED) at Kühne Foundation, Hamburg, Germany.
Background: To ensure the complete traceability of healthcare commodities, robust end-to-end data management protocols are needed for the supply chain. In Ethiopia, digital tools like Dagu-2 are used in the lower levels of the healthcare supply chain. However, there is a lack of information regarding the implementation status, factors, and challenges of Dagu-2, as it is a recent upgrade from the offline Dagu-1 application.
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