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http://dx.doi.org/10.1177/00033197221092812 | DOI Listing |
J Periodontal Res
January 2025
Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Aim: This study aimed to evaluate and compare the results of combination therapy involving bone grafting and two different resorbable collagen membranes in 1-, 2- and 3-wall infrabony defects.
Methods: A total of 174 patients with infrabony defects (≥ 7 mm periodontal probing depth) were randomized to receive deproteinized bovine bone mineral (DBBM) with either a native porcine non-crosslinked collagen membrane (N-CM, control, n = 87) or a novel porcine crosslinked collagen membrane (C-CM, test, n = 87). Clinical parameters, including periodontal probing depth (PPD), clinical attachment level (CAL), and gingival recession (GR), were recorded at baseline, 12 weeks, and 24 weeks.
BMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFJ Hum Hypertens
January 2025
Department of Pediatrics and Child Health, University of Ilorin, Ilorin, Nigeria.
Sci Rep
January 2025
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G2P5, Canada.
NPJ Digit Med
January 2025
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.
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