Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage.

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations.

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations.

 The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as (simplified ellipsoid volume equation) and (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation.