A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures.

Aims: This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods: 504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses.

Using Radiomics and Convolutional Neural Networks for the Prediction of Hematoma Expansion After Intracerebral Hemorrhage.

Background: Hematoma enlargement (HE) is a common complication following acute intracerebral hemorrhage (ICH) and is associated with early deterioration and unfavorable clinical outcomes. This study aimed to evaluate the predictive performance of a computed tomography (CT) based model that utilizes deep learning features in identifying HE.

Methods: A total of 408 patients were retrospectively enrolled between January 2015 and December 2020 from our institution.

[Case-control studies of the relevant factors among Ningxia Hui and Han prostate cancer groups].

Objective: To explore the relationship between the relevant factors and prostate cancer among Hui and Han populations.

Methods: The study involved 267 prostate cancer patients as cases (214 cases from Han population and 53 cases from Hui population) and 534 prostatic hyperplasia patients as controls (428 cases from Han population and 106 cases from Hui population). All the patients were collected from the General Hospital of Ningxia Medical University during January of 2007 to September of 2013.