Objective: We conducted a systematic review and meta-analysis of diagnostic test accuracy studies to summarise the properties of sweat conductivity (SC) to rule in/out cystic fibrosis (CF).
Data Source: We searched PubMed, Embase, Web of Science, Google Scholar, SciELO and LILACS up to 13 March 2023.
Study Selection: We selected prospective and retrospective diagnostic test accuracy studies which compared SC, measured through two well-established and commercially available devices, that is, Nanoduct or Sweat-Chek Analyser, to quantitative measurement of sweat chloride.
Main Outcome Measures: Pooled sensitivity, specificity, positive likelihood ratio (+LR) and negative likelihood ratio (-LR), and their corresponding 95% CIs.
Data Extraction And Synthesis: The Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines were followed. Data were extracted by one reviewer and checked by another. The hierarchical summary receiver operating characteristics model was used to estimate diagnostic test accuracy.
Results: Ten studies involving 8286 participants were included. The pooled estimates of sensitivity, specificity, +LR and -LR were 0.97 (95% CI 0.94 to 0.98), 0.99 (95% CI 0.98 to 0.99), 171 (95% CI 58 to 500) and 0.02 (95% CI 0.01 to 0.05), respectively. Sensitivity analyses did not reveal a substantial impact of study-level factors on the results, such as study quality, cut-off values for a positive test, study sample size and participant age group. The quality of evidence was considered moderate.
Conclusion: SC demonstrated excellent diagnostic performance. In addition, its accuracy parameters suggest its role as an alternative to the sweat test for CF diagnosis.
Prospero Registration Number: CRD42022284504.
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http://dx.doi.org/10.1136/archdischild-2023-325629 | DOI Listing |
Int J Geriatr Psychiatry
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Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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January 2025
School of Medical Technology, Xuzhou Medical University, Xuzhou, 221004, China.
Background: Tuberculosis (TB) is a global problem that seriously jeopardizes human health. Among them, the diagnosis and treatment of smear- or culture-negative TB patients is a challenge. The Xpert MTB/RIF (Xpert) assay has been reported to be a novel molecular diagnostic tool for rapidly detecting TB.
View Article and Find Full Text PDFJ Craniomaxillofac Surg
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknesses of these models in terms of data transparency and scientific accuracy by incorporating current scientific knowledge into responses. In this study, RAG and GPT-4 by OpenAI were applied to develop GuideGPT, a context aware chatbot integrated with a knowledge database from 449 scientific publications designed to provide answers on the prevention, diagnosis, and treatment of medication-related osteonecrosis of the jaw (MRONJ). A comparison was made with a generic LLM ("PureGPT") across 30 MRONJ-related questions.
View Article and Find Full Text PDFMed Clin (Barc)
January 2025
Servicio de Hematología, Hospital Universitario Vall d'Hebron, Barcelona, España.
Introduction: Chronic lymphocytic leukemia (CLL) is the most common form of leukemia in adults in Western countries, with a median age at diagnosis of 72 years. This guide, developed by the Spanish Group for Chronic Lymphocytic Leukemia (GELLC), addresses the most relevant aspects of CLL, with the objectives of facilitating and aiding the diagnostic process, establishing therapeutic recommendations for choosing the best treatment for each type of patient, as well as standardizing the management of CLL and ensuring equity across different hospitals in terms of the use of the various available treatment regimens.
Methodology: The references obtained were classified according to the level of evidence and following the criteria established by the Agency for Health Research and Quality, and the recommendations were classified according to the criteria of the National Comprehensive Cancer Network (NCCN).
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