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Background: Recently, several novel scoring systems have been developed to evaluate the severity and outcomes of acute pancreatitis. This study aimed to compare the effectiveness of novel and conventional scoring systems in predicting the severity and outcomes of acute pancreatitis.
Methods: Patients treated between January 2003 and August 2020 were reviewed. The Ranson score (RS), Glasgow score (GS), bedside index of severity in acute pancreatitis (BISAP), pancreatic activity scoring system (PASS), and Chinese simple scoring system (CSSS) were determined within 48 h after admission. Multivariate logistic regression was used for severity, mortality, and organ failure prediction. Optimum cutoffs were identified using receiver operating characteristic curve analysis.
Results: A total of 1848 patients were included. The areas under the curve (AUCs) of RS, GS, BISAP, PASS, and CSSS for severity prediction were 0.861, 0.865, 0.829, 0.778, and 0.816, respectively. The corresponding AUCs for mortality prediction were 0.693, 0.736, 0.789, 0.858, and 0.759. The corresponding AUCs for acute respiratory distress syndrome prediction were 0.745, 0.784, 0.834, 0.936, and 0.820. Finally, the corresponding AUCs for acute renal failure prediction were 0.707, 0.734, 0.781, 0.868, and 0.816.
Conclusions: RS and GS predicted severity better than they predicted mortality and organ failure, while PASS predicted mortality and organ failure better. BISAP and CSSS performed equally well in severity and outcome predictions.
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http://dx.doi.org/10.1186/s12944-021-01470-4 | DOI Listing |
Genet Med
September 2025
Division of Medical Genetics, University of Washington School of Medicine.
Purpose: The fourth phase of the Electronic Medical Records and Genome Network (eMERGE4) is testing the return of 10 polygenic risk scores (PRS) across multiple clinics. Understanding the perspectives of health-system leaders and frontline clinicians can inform plans for implementation of PRS.
Methods: Fifteen health-system leaders and 20 primary care providers (PCPs) took part in semi-structured interviews.
J Magn Reson Imaging
September 2025
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.
View Article and Find Full Text PDFFoot Ankle Int
September 2025
Department of Orthopaedic Surgery, St. Luke's University Health Network, Bethlehem, PA, USA.
Background: In response to the opioid epidemic, many surgical specialties have adopted nonopioid pain management strategies. Ultrasound (US)-guided peripheral nerve blocks (PNBs) are effective in reducing pain and opioid consumption postsurgery. Liposomal bupivacaine (LB), shown effective in shoulder surgery, was approved in November 2023 for use in US-guided lower extremity blocks.
View Article and Find Full Text PDFDiabetes Obes Metab
September 2025
Eli Lilly and Company, Indianapolis, Indiana, USA.
Aims: To determine whether adults with type 2 diabetes (T2D) treated with retatrutide report greater changes in self-reported appetite, dietary restraint, and disinhibition compared to placebo or dulaglutide and to examine associations with weight change.
Materials And Methods: These pre-specified exploratory analyses examined changes from baseline in Appetite Visual Analogue Scale (VAS) and Eating Inventory (EI) scores after 24 and 36 weeks of once-weekly treatment with placebo, dulaglutide 1.5 mg, or retatrutide 0.
Electromagn Biol Med
September 2025
Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Coimbatore, India.
Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications.
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