To investigate the construct-concurrent validity and reliability of the Turkish version of the European Child Environment Questionnaire (ECEQ-T) in children with cerebral palsy (CP). Mean age 9.53 ...

Background: It is important to monitor the development of infants and children during their growth period. Various anthropometric parameters of children are measured at regular intervals after birth, and their general health and nutrition and physiological needs are assessed based on these measurements.Aims: To construct the current body mass index (BMI) percentiles and compare them with the literature reports and World Health Organisation (WHO) data.Study Design: Cross-sectional study. Methods: This study is a cross-sectional research on 1,345 boys and 1,364 girls of age ranging from 0 to 24 months; their BMIs were measured at the Bakent University Hospital from January 2018-December 2021. The BMI growth curves for either gender were constructed according to the LMS method by using RefCurv 0.4.2. software. The "gamlss" package was employed for the selection of model parameters in fitting the BMI growth curves, and the model performance was evaluated with reference to the generalized Akaike information criterion (GAIC).Results: According to gender, smoothed BMI growth curves were constructed in the 3rd-97th percentiles. The model adequacy of the fitted growth curves was evaluated with the worm plot. The fit of the BMI model to the data was found to be sufficient, with 95% of the BMI values occurring between two elliptic curves.Conclusion: The study shows a slight increase in BMI percentile values obtained by gender compared to WHO standards.

Identifying an optimal linear combination of continuous variables is a key objective in various fields of research, such as medicine. This manuscript explores the use of information-theoretical approaches used to establish these linear combinations. Coefficients obtained from logistic regression can be used to construct such a linear combination, and this approach has been commonly adopted in the literature for comparison purposes. The main contribution of this work is to propose novel ways of determining these linear combination coefficients by optimizing information-theoretical objective functions. Biomarkers are usually continuous measurements utilized to diagnose if a patient has the underlying disease. Certain disease contexts may lack high diagnostic power biomarkers, making their optimal combination a critical area of interest. We apply the above-mentioned novel methods to the problem of a combination of biomarkers. We assess the performance of our proposed methods against combinations derived from logistic regression coefficients, by comparing area under the ROC curve (AUC) values and other metrics in a broad simulation and a real life data application.