The Hebrew Web Communicative Development Inventory (MB-CDI): Lexical Development Growth Curves.

This article presents data on lexical development of 881 Israeli Hebrew-speaking monolingual toddlers ages 1;0 to 2;0. A Web-based version of the Hebrew MacArthur-Bates Communicative Development Inventories (H-MB-CDI) was used for data collection. Growth curves for expressive vocabulary, receptive vocabulary, actions and gestures were characterized. Developmental trajectories of toddlers with various demographic characteristics, such as education, income, religiosity level, birth order of the child, and child-care arrangements were compared. Results show that the lexical growth curves for Hebrew are comparable to those reported for other languages. Sex, birth order, and child-care arrangements were found to influence the size of lexicons. It is recommended that the trajectories presented here be used as norms for lexical growth among typical Hebrew-speaking toddlers in the second year of life.

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements. METHODS: This retrospective study included 336 pregnant women with a chief complaint of pruritis without rash during the second/third trimesters. Data extracted included: demographics, obstetric, clinical and laboratory features. The primary outcome was an elevated bile acid measurement ≥ 10 µmol/L, regardless of liver enzyme levels. We used different machine-learning models and statistical regression to predict elevated bile acid levels. RESULTS: Among 336 women who complained about pruritis, 167 had bile acids ≥ 10 µmol/L and 169 had normal levels. Women with elevated bile acids were older than those with normal levels (p = 0.001), higher parity (p = 0.001), and higher glutamic oxaloacetic transaminase ( GOT) (p = 0.001) and glutamic-pyruvic transaminase (GPT) levels (p = 0.001). Using machine-learning models, the XGB Classifier model was the most accurate (area under the curve (AUC), 0.9) followed by the K-neighbors model (AUC, 0.86); and then the Support Vector Classification (SVC) model (AUC, 0.82). The model with the lowest predicative ability was the logistic regression (AUC, 0.72). The maximum sensitivity of the XGB model was 86% and specificity 75%. The best predictive parameters of the XGB model were elevated GOT (Importance 0.17), elevated GPT (Importance 0.16), family history of bile disease (0.16) and previous pregnancy with ICP (0.13). CONCLUSION: Machine-learning models using clinical data may predict ICP more accurately than logistic regression does. Using detection algorithms derived from these techniques may improve identification of ICP, especially when bile acid testing is not available.

Month of birth and risk of autism spectrum disorder: a retrospective cohort of male children born in Israel.

BACKGROUND: Increased incidence and prevalence of autism spectrum disorder (ASD) over the last two decades have prompted considerable efforts to investigate its aetiological factors. We examined an association between month of birth and ASD incidence. METHODS: In a retrospective cohort of male children born from January 1999 to December 2008 in a large health organisation in Israel (Maccabi Healthcare Services), ASD was followed from birth through December 2015. RESULTS: Of 108 548 boys, 975 cases of ASD were identified. The highest rates (10.3 and 10.2 per 1000 male live births) were recorded for children born in May and August, respectively, and the lowest rates for February (7.6 per 1000 male live births). Among lower socioeconomic status households, boys born in August were more likely (OR=1.71; 95% CI 1.06 to 2.74) of being diagnosed with ASD than children born in January. Significantly higher rates were not observed for other months. CONCLUSIONS: In line with several previous studies, we found a modestly higher likelihood of autism occurrence among male children of lower socioeconomic levels born in August.