Recreational Cannabis Legalization in Canada: A Pediatrics Perspective.

Background: Despite its controversial nature, the use of recreational marijuana and cannabis-derived products continue to increase in popularity. Cannabis consumption is currently legal in certain American states as well as Canada, and it is also frequently used by Canadian youth. However, cannabis use during childhood and adolescence can contribute to significant harm. Materials andMethods: A review of current literature regarding the impacts of cannabis consumption among Canadian youth was conducted. Medline, Cochrane, Embase and PubMed databases were utilized to identify journal articles published within 10 years that highlighted the impacts of cannabis consumption in its different forms among the North American pediatric population. Results: Our review identified harms include structural and functional impairments in the developing brain, the development of mental health conditions such as depression and anxiety, impaired road safety while driving under the influence of cannabis, and the significant consequences of accidental ingestion of cannabis products by children. We also ascertained that cannabis cessation strategies that employed an affective model, which explores the root causes of adolescents turning to cannabis, are most effective in reducing substance use among adolescents. Conclusions: In light of the recent legalization of cannabis in Canada, the purpose of this article was to provide background on cannabis consumption and its legalization in Canada, the impacts of cannabis on Canadian youth, and evidence-based strategies to help mitigate them.

Food for Thought: Machine Learning in Autism Spectrum Disorder Screening of Infants.

Diagnoses of autism spectrum disorders (ASD) are typically made after toddlerhood by examining behavioural patterns. Earlier identification of ASD enables earlier intervention and better outcomes. Machine learning provides a data-driven approach of diagnosing autism at an earlier age. This review aims to summarize recent studies and technologies utilizing machine learning based strategies to screen infants and children under the age of 18 months for ASD, and identify gaps that can be addressed in the future. We reviewed nine studies based on our search criteria, which includes primary studies and technologies conducted within the last 10 years that examine children with ASD or at high risk of ASD with a mean age of less than 18 months old. The studies must use machine learning analysis of behavioural features of ASD as major methodology. A total of nine studies were reviewed, of which the sensitivity ranges from 60.7% to 95.6%, the specificity ranges from 50% to 100%, and the accuracy ranges from 60.9% to 97.7%. Factors that contribute to the inconsistent findings include the varied presentation of ASD among patients and study design differences. Previous studies have shown moderate accuracy, sensitivity and specificity in the differentiation of ASD and non-ASD individuals under the age of 18 months. The application of machine learning and artificial intelligence in the screening of ASD in infants is still in its infancy, as observed by the granularity of data available for review. As such, much work needs to be done before the aforementioned technologies can be applied into clinical practice to facilitate early screening of ASD.