Background: Systematic reviews of complex interventions can vary widely in purpose, data availability and heterogeneity, and stakeholder expectations.
Rationale: This article addresses the uncertainty that systematic reviewers face in selecting methods for reviews of complex interventions. Specifically, it lays out parameters for systematic reviewers to consider when selecting analytic approaches that best answer the questions at hand and suggests analytic techniques that may be appropriate in different circumstances.
Discussion: Systematic reviews of complex interventions comprising multiple questions may use multiple analytic approaches. Parameters to consider when choosing analytic methods for complex interventions include nature and timing of the decision (clinical practice guideline, policy, or other); purpose of the review; extent of existing evidence; logistic factors such as the timeline, process, and resources for deciding the scope of the review; and value of information to be obtained from choosing specific systematic review methods. Reviewers may elect to revise their analytic approach based on new or changing considerations during the course of the review but should guard against bias through transparency of reporting.
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http://dx.doi.org/10.1016/j.jclinepi.2017.06.014 | DOI Listing |
Microb Genom
January 2025
GMT Science 75 route de Lyons-La-Foret, Rouen F-76000, France.
Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFMol Neurobiol
January 2025
Translational Oncology Laboratory, Department of Zoology, Hansraj College, Delhi University, New Delhi, 110007, India.
This review explores the current understanding and recent advancements in neuroblastoma, one of the most common extracranial solid pediatric cancers, accounting for ~ 15% of childhood cancer-related mortality. The hallmarks of NBL, including angiogenesis, metastasis, apoptosis resistance, cell cycle dysregulation, drug resistance, and responses to hypoxia and ROS, underscore its complex biology. The tumor microenvironment's significance in disease progression is acknowledged in this study, along with the pivotal role of cancer stem cells in sustaining tumor growth and heterogeneity.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Graduate School of Qinghai University, Xining, 810000, Qinghai Province, People's Republic of China.
The occurrence and progression of breast cancer (BCa) are complex processes involving multiple factors and multiple steps. The tumor microenvironment (TME) plays an important role in this process, but the functions of immune components and stromal components in the TME require further elucidation. In this study, we obtained the RNA-seq data of 1086 patients from The Cancer Genome Atlas (TCGA) database.
View Article and Find Full Text PDFSleep Breath
January 2025
Department of Respiratory and Critical Care Medicine, Medical School of Nantong University, Nantong Key Laboratory of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China.
Background: The pathophysiology of obstructive sleep apnea (OSA) and diabetes mellitus (DM) is still unknown, despite clinical reports linking the two conditions. After investigating potential roles for DM-related genes in the pathophysiology of OSA, our goal is to investigate the molecular significance of the condition. Machine learning is a useful approach to understanding complex gene expression data to find biomarkers for the diagnosis of OSA.
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