Introduction: Nurses and other nonspecialists in dysphagia are often trained to screen swallowing poststroke. There are many basic tools that test water only, they are usually conservative, and patients that fail the test remain nil by mouth until a speech and language therapy assessment. More comprehensive tests also allow nonspecialists to recommend modified oral intake. Little is known about the accuracy, clinical utility and cost-effectiveness of these tests.
Methods: Following PRISMA guidelines, a systematic review was conducted to describe comprehensive swallowing tests that are available for use in acute stroke by nurses or other nonspecialists in dysphagia. A meta-analysis was performed to evaluate accuracy and the clinical utility of the tests was considered. Searches and analyses, conducted by two reviewers, included MEDLINE, Embase, trial registries and grey literature up to December 2018. Validated studies were assessed for quality and risk of bias using QUADAS-2.
Results: Twenty studies were included, describing five different tests, three of which had undergone validation. The tests varied in content, recommendations and use. There was no test superior in accuracy and clinical utility. Three studies validating the Gugging Swallow Screen provided sufficient data for meta-analysis, demonstrating high sensitivity; 96% (95% CI 0.90-0.99), but low specificity, 65% (95% CI 0.47-0.79), in line with many water swallow tests. Results should be interpreted with caution as study quality and applicability to the acute stroke population was poor.
Conclusions: There is no comprehensive nurse dysphagia assessment tool that has robustly demonstrated good accuracy, clinical utility and cost-effectiveness in acute stroke.
Relevance To Clinical Practice: Nurses and other clinicians can develop competencies in screening swallowing and assessing for safe oral intake in those with poststroke dysphagia. It is important to use a validated assessment tool that demonstrates good accuracy, clinical utility and cost-effectiveness.
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http://dx.doi.org/10.1111/jocn.15192 | DOI Listing |
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
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View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
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J Vis Exp
January 2025
Department of Biomedical Engineering, Washington University in St. Louis; Department of Obstetrics & Gynecology, Washington University in St. Louis;
For noninvasive light-based physiological monitoring, optimal wavelengths of individual tissue components can be identified using absorption spectroscopy. However, because of the lack of sensitivity of hardware at longer wavelengths, absorption spectroscopy has typically been applied for wavelengths in the visible (VIS) and near-infrared (NIR) range from 400 to 1,000 nm. Hardware advancements in the short-wave infrared (SWIR) range have enabled investigators to explore wavelengths in the ~1,000 nm to 3,000 nm range in which fall characteristic absorption peaks for lipid, protein, and water.
View Article and Find Full Text PDFEur Radiol
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Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
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