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High fatigue levels among psychiatric outpatients - the validity of the Danish Patient Reported Outcomes Measurement Information System Fatigue Short-Form (PROMISF-SF).
J Patient Rep Outcomes
January 2025
Department of Clinical Medicine, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
Background: Patient Reported Outcomes Measurement Information System Fatigue Short-Form (PROMIS-F-SF) is a self-administered, patient reported outcome (PRO) designed to assess fatigue in healthy and clinical populations and for tracking progress during treatment for disorders complicated with fatigue.
Methods: Patients in the Mental Health Service Outpatient Clinics and healthy volunteers were invited to complete a survey, which included the Danish translation of the PROMIS-F-SF, the Chalder Fatigue Scale (CFS-11), and measures of depression and anxiety. We conducted a confirmatory factor analysis of the previously suggested single-factor structure of the instrument.
Objectives: To determine the value of preoperative magnetic resonance imaging (MRI) in predicting macrotrabecular-massive hepatocellular carcinoma (MTM-HCC).
Materials And Methods: A search was conducted on PubMed, Web of Science, Cochrane Library databases, and Embase for studies evaluating the performance of MRI in assessing MTM-HCC. The quality assessment of diagnostic studies (QUADAS-2) tool was used to assess the risk of bias.
The tissue-plasminogen activator-challenged thromboelastography provides a comprehensive assessment of fibrinolysis in the severely injured.
J Trauma Acute Care Surg
January 2025
From the Division of Acute Care Surgery, Department of Surgery (E.R.M., T.B.M., C.M.W., H.S., R.H., C.D.B.), University of Nebraska Medical Center, Omaha, Nebraska; Department of Surgery (H.B.M.), AdventHealth Porter; Department of Surgery (E.E.M., J.G.C.), Ernest E Moore Shock Trauma Center at Denver Health, Denver; Department of Surgery (E.E.M.), University of Colorado Anschutz Medical Campus, Aurora, Colorado; Hunter College (I.M.B.), New York, New York; Sauaia Statistical Solutions, LLC (A.S.), Denver, Colorado; and Department of Cellular and Integrative Physiology (F.I.G., C.D.B.), University of Nebraska Medical Center, Omaha, Nebraska.
Background: Tissue-plasminogen activator-challenged thromboelastography (tPA-TEG) predicts massive transfusion and mortality better than conventional rapid thromboelastography (rTEG), with little concordance between their lysis values (LY30). We hypothesized that the main fibrinolytic inhibitors plasminogen activator inhibitor-1 (PAI-1) and α-2 antiplasmin (A2AP), as well as markers of fibrinolytic activation (plasmin-antiplasmin [PAP], tPA-PAI-1 complex, tPA activity), would correlate more strongly with tPA-TEG versus rTEG LY30 and may explain the recent findings of four distinct fibrinolytic phenotypes in trauma based on these two TEG methodologies.
Methods: Adult trauma patients (n = 56) had tPA-TEG, rTEG, and plasma obtained on arrival to the emergency department with institutional review board approval.
Graphical Model Selection to Infer the Partial Correlation Network of Allelic Effects in Genomic Prediction With an Application in Dairy Cattle.
J Anim Breed Genet
January 2025
Departamento de Ciencias Agrícolas y Pecuarias, Universidad Francisco de Paula Santander, Cúcuta, Colombia.
We addressed genomic prediction accounting for partial correlation of marker effects, which entails the estimation of the partial correlation network/graph (PCN) and the precision matrix of an unobservable m-dimensional random variable. To this end, we developed a set of statistical models and methods by extending the canonical model selection problem in Gaussian concentration, and directed acyclic graph models. Our frequentist formulations combined existing methods with the EM algorithm and were termed Glasso-EM, Concord-EM and CSCS-EM, whereas our Bayesian formulations corresponded to hierarchical models termed Bayes G-Sel and Bayes DAG-Sel.
View Article and Find Full Text PDFAutomated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
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