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Background: The statistical models developed for meta-analysis of diagnostic test accuracy studies require specialised knowledge to implement. This is especially true since recent guidelines, such as those in Version 2 of the Cochrane Handbook of Systematic Reviews of Diagnostic Test Accuracy, advocate more sophisticated methods than previously. This paper describes a web-based application - MetaBayesDTA - that makes many advanced analysis methods in this area more accessible.
Results: We created the app using R, the Shiny package and Stan. It allows for a broad array of analyses based on the bivariate model including extensions for subgroup analysis, meta-regression and comparative test accuracy evaluation. It also conducts analyses not assuming a perfect reference standard, including allowing for the use of different reference tests.
Conclusions: Due to its user-friendliness and broad array of features, MetaBayesDTA should appeal to researchers with varying levels of expertise. We anticipate that the application will encourage higher levels of uptake of more advanced methods, which ultimately should improve the quality of test accuracy reviews.
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http://dx.doi.org/10.1186/s12874-023-01910-y | DOI Listing |
J Appl Clin Med Phys
September 2025
Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA.
Purpose: The development of on-board cone-beam computed tomography (CBCT) has led to improved target localization and evaluation of patient anatomical change throughout the course of radiation therapy. HyperSight, a newly developed on-board CBCT platform by Varian, has been shown to improve image quality and HU fidelity relative to conventional CBCT. The purpose of this study is to benchmark the dose calculation accuracy of Varian's HyperSight cone-beam computed tomography (CBCT) on the Halcyon platform relative to fan-beam CT-based dose calculations and to perform end-to-end testing of HyperSight CBCT-only based treatment planning.
View Article and Find Full Text PDFBMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
BMC Ecol Evol
September 2025
Lehrstuhl für Zoologie, TUM School of Life Sciences, Technical University of Munich, Liesel-Beckmann Strasse 4, Freising, 85354, Germany.
Accurate three-dimensional localisation of ultrasonic bat calls is essential for advancing behavioural and ecological research. I present a comprehensive, open-source simulation framework-Array WAH-for designing, evaluating, and optimising microphone arrays tailored to bioacoustic tracking. The tool incorporates biologically realistic signal generation, frequency-dependent propagation, and advanced Time Difference of Arrival (TDoA) localisation algorithms, enabling precise quantification of both positional and angular accuracy.
View Article and Find Full Text PDFInt J Nurs Knowl
September 2025
Luciano Feijão College, Sobral, Ceará, Brazil.
Purpose: To clinically validate the nursing diagnosis "Inadequate Nutritional Intake" based on elements identified within a specific situation theory framework in the context of children with cancer.
Methods: This is a diagnostic accuracy study following the Standards for Reporting Diagnostic Accuracy Studies (STARD) protocol. Specifically, it refers to the clinical validation phase of the nursing diagnosis Inadequate nutritional intake, using a cross-sectional design.
Surg Endosc
September 2025
Department of Surgery and Cancer Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Introduction: This study aimed to evaluate surgeons' ergonomic satisfaction when using laparoscopic energy devices and to investigate how prolonged use affects muscle fatigue and surgical performance.
Methods: A two-part study, including a survey and a kinesiologic experiment, was conducted to compare 4 laparoscopic energy devices (D1-D4). Thirty surgeons completed a structured survey assessing ergonomic factors such as device weight, grip strength, handle design, comfort, and trigger location.