Background And Objectives: To determine whether intra-individual differences in pre-donation blood test results were associated with vasovagal reactions (VVRs).
Materials And Methods: The study included donors who voluntarily donated 400 mL of whole blood at least twice during a 5-year blood collection period of the Japanese Red Cross, including both donations with and without a VVR. A conditional logistic regression analysis by age group and sex was conducted, using each donor as a stratum, together with an analysis of deviance to test the significance of including an interaction term between age group and blood data in the regression model.
Background: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings.
Methods And Results: ML-based models were developed to predict in-hospital mortality in 527 patients with ICH using raw brain imaging data from brain computed tomography and clinical data.