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among children with pneumonia using a causal Bayesian network

Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data.

Core protocol for the adaptive Platform Trial In COVID-19 Vaccine priming and BOOsting (PICOBOO)

The need for coronavirus 2019 (COVID-19) vaccination in different age groups and populations is a subject of great uncertainty and an ongoing global debate. Critical knowledge gaps regarding COVID-19 vaccination include the duration of protection offered by different priming and booster vaccination regimens in different populations, including homologous or heterologous schedules.

Patient-reported outcome measures for paediatric acute lower respiratory infection studies

Patient-reported outcome measures (PROMs) are recommended for capturing meaningful outcomes in clinical trials. The use of PROMs for children with acute lower respiratory infections (ALRIs) has not been systematically reported. We aimed to identify and characterise patient-reported outcomes and PROMs used in paediatric ALRI studies and summarise their measurement properties.

BEAT CF pulmonary exacerbations core protocol for evaluating the management of pulmonary exacerbations in people with cystic fibrosis

Cystic fibrosis (CF) is a rare, inherited, life-limiting condition predominantly affecting the lungs, for which there is no cure. The disease is characterized by recurrent pulmonary exacerbations (PEx), which are thought to drive progressive lung damage. Management of these episodes is complex and generally involves multiple interventions targeting different aspects of disease. The emergence of innovative trials and use of Bayesian statistical methods has created renewed opportunities for studying heterogeneous populations in rare diseases.

Mapping national, regional and local prevalence of hypertension and diabetes in Ethiopia using geospatial analysis

This study aimed to map the national, regional and local prevalence of hypertension and diabetes in Ethiopia.

Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data

Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination.

Short-Term Active Safety Surveillance of the Spikevax and Nuvaxovid Priming Doses in Australia

Australia commenced administration of the Spikevax (Moderna mRNA-1273) COVID-19 vaccine in August 2021 and Nuvaxovid (Novavax NVX-CoV2373) in January 2022. This study describes the short-term safety profile of priming doses of the Spikevax and Nuvaxovid vaccines given between September 2021 and September 2023. 

Impact of Meningococcal ACWY Vaccination Program during 2017-18 Epidemic, Western Australia, Australia

The rising incidence of invasive meningococcal disease (IMD) caused by Neisseria meningitidis serogroup W in Western Australia, Australia, presents challenges for prevention. We assessed the effects of a quadrivalent meningococcal vaccination program using 2012-2020 IMD notification data.

The AuTOMATIC trial: a study protocol for a multi-arm Bayesian adaptive randomised controlled trial of text messaging to improve childhood immunisation coverage

While most Australian children are vaccinated, delays in vaccination can put them at risk from preventable infections. Widespread mobile phone ownership in Australia could allow automated short message service (SMS) reminders to be used as a low-cost strategy to effectively 'nudge' parents towards vaccinating their children on time.

Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling

In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes.