METHODS: Retrospective data of 347 patients, consisting of maternal demographics and ultrasound parameters collected between the 20th and 25th gestational weeks, were studied. ML models were applied to different combinations of the parameters to predict SGA and severe SGA at birth (defined as 10th and third centile birth weight).

RESULTS: Using second-trimester measurements, ML models achieved an accuracy of 70% and 73% in predicting SGA and severe SGA whereas clinical guidelines had accuracies of 64% and 48%. Uterine PI (Ut PI) was found to be an important predictor, corroborating with existing literature, but surprisingly, so was nuchal fold thickness (NF). Logistic regression showed that Ut PI and NF were significant predictors and statistical comparisons showed that these parameters were significantly different in disease. Further, including NF was found to improve ML model performance, and vice versa.

METHODS: The data of 955 older adults with any type of psychiatric disorders were extracted from the database of the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD) project. Demographic and clinical characteristics were recorded using a standardized protocol and data collection procedure. Both univariate and multiple logistic regression analyses were performed.

RESULTS: The proportion of benzodiazepine and antidepressant combination in this cohort was 44.3%. Multiple logistic regression analysis revealed that higher doses of antidepressants, younger age (<65 years), inpatients, public hospital, major comorbid medical conditions, antidepressant types, and country/territory were significantly associated with more frequent co-prescription of benzodiazepines and antidepressants.