Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.

Osteoporotic hip fractures (HFs) in the elderly are a pertinent issue in healthcare, particularly in developed countries such as Australia. Estimating prognosis following admission remains a key challenge. Current predictive tools require numerous patient input features including those unavailable early in admission.

Response to COVID-19 vaccination in patients on cancer therapy: Analysis in a SARS-CoV-2-naïve population.

Background: Cancer patients have increased morbidity and mortality from COVID-19, but may respond poorly to vaccination. The Evaluation of COVID-19 Vaccination Efficacy and Rare Events in Solid Tumors (EVEREST) study, comparing seropositivity between cancer patients and healthy controls in a low SARS-CoV-2 community-transmission setting, allows determination of vaccine response with minimal interference from infection.

Methods: Solid tumor patients from The Canberra Hospital, Canberra, Australia, and healthy controls who received COVID-19 vaccination between March 2021 and January 2022 were included.