Building and Verifying a Prediction Model for Deep Vein Thrombosis Among Spinal Cord Injury Patients Undergoing Inpatient Rehabilitation.

Objective: To explore the relevant variables that contribute to deep vein thrombosis (DVT) among spinal cord injury (SCI) patients undergoing inpatient rehabilitation and to build and validate a nomogram model that predicts DVT risk.

Methods: By convenience sampling, 558 SCI patients who were hospitalized at a tertiary-level Grade A general hospital in Anhui Province, China between January 2017 and March 2022 were chosen as the study subjects. They were split into 2 groups at random, one for training (n = 446) and the other for validation (n = 112).

Construction and Verification of Urinary Tract Infection Prediction Model for Hospitalized Rehabilitation Patients with Spinal Cord Injury.

Objective: To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model.

Methods: This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected.

BioGPT: generative pre-trained transformer for biomedical text generation and mining.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e.