Objectives: Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed together, permitting intervention hierarchies to be created. Our aim was to develop a novel graphical display to aid interpretation of NMA to clinicians and decision-makers that incorporates ranking of interventions.
Study Design And Setting: Current literature was searched, scrutinized, and provided direction for developing the novel graphical display. Ranking results were often found to be misinterpreted when presented alone and, to aid interpretation and effective communication to inform optimal decision-making, need to be displayed alongside other important aspects of the analysis including the evidence networks and relative intervention effect estimates.
Results: Two new ranking visualizations were developed-the 'Litmus Rank-O-Gram' and the 'Radial SUCRA' plot-and embedded within a novel multipanel graphical display programmed within the MetaInsight application, with user feedback gained.
Conclusion: This display was designed to improve the reporting, and facilitate a holistic understanding, of NMA results. We believe uptake of the display would lead to better understanding of complex results and improve future decision-making.
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http://dx.doi.org/10.1016/j.jclinepi.2023.02.016 | DOI Listing |
Sci Rep
January 2025
Instituto Politécnico Nacional, Centro de Desarrollo Aeroespacial, 06610, Mexico City, Mexico.
This work presents the design and validation of a thermal subsystem for a 1U CubeSat-type nanosatellite. The design encompasses two stages: regulating the satellite's temperature range through implementing passive control based on multilayer coatings and an electronic board capable of measuring the internal surface temperature of each of the satellite's six faces. Validation is conducted through tests performed in a theoretical thermo vacuum chamber that provides a controlled environment, simulating the thermal conditions to which the satellite will be exposed once in orbit.
View Article and Find Full Text PDFInt J Legal Med
January 2025
Department of Stomatology, Public Health and Forensic Odontology, Ribeirão Preto School of Dentistry, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
The age estimation by tooth cementum thickness is a method that has been discussed regarding its applicability. The aim of this study was to conduct a systematic review on the use of tooth cementum thickness as a biomarker for age estimation in adults, as well as a meta-analysis to assess the method's reliability. The search was conducted on Embase, LILACS, PubMed/MEDLINE, SciELO, Scopus, and Web of Science databases.
View Article and Find Full Text PDFBio Protoc
January 2025
Department of Stomatology, Peking Union Medical College Hospital, Beijing, China.
Pulpitis is an important and prevalent disease within the oral cavity. Thus, animal models are necessary tools for basic research focused on pulpitis. Researchers worldwide often use dogs and miniature pigs to construct animal models of pulpitis.
View Article and Find Full Text PDFiScience
January 2025
Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
Recent studies showed that humans, regardless of age, education, and culture, can extract the linear trend of a noisy scatterplot. Although this capacity looks sophisticated, it may simply reflect the extraction of the principal trend of the graph, as if the cloud of dots was processed as an oriented object. To test this idea, we trained Guinea baboons to associate arbitrary shapes with the increasing or decreasing trends of noiseless and noisy scatterplots, while varying the number of points, the noise level, and the regression slope.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
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