A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics.

Comparison of the risk-taking behaviours of children and the practices adopted by their caregivers for improving home safety.

Objectives: The objectives of this study were: (1) to describe the efforts of caregivers to provide a safe home environment for their children and the risk-taking behaviours of children; (2) to determine the efficacy of caregivers' practices for providing a safe environment on the risk-taking behaviours of children; (3) to identify factors influencing the home-safety practices adopted by caregivers for their children; and (4) to determine the information sources that caregivers use for preventing in-home injuries in their children.

Study Design: Cross-sectional study

Methods: The sample consisted of 563 pairs of elementary students and their caregivers, who were administered home-safety questionnaires at school and home, respectively. Five hundred and one matched pairs were included in the analysis.