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Research
Age at surgery and outcomes of an undescended testisWe investigated the prevalence of UDT and examined rates of surgery and age at surgery in an Australian population.
Research
Gross motor performance in children prenatally exposed to alcohol and living in remote AustraliaA higher than expected proportion of children with fetal alcohol spectrum disorders had gross motor scores that indicated impairment and need for therapy
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Seasonal variation in fetal growth: Accounting for sociodemographic, biological, and environmental exposuresWe sought to investigate seasonal variation in fetal growth, accounting for important sociodemographic, biological, and environmental exposures.
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A Novel Approach to Transforming Smoking Cessation Practice for Pregnant Aboriginal Women and Girls Living in the PilbaraTobacco smoking during pregnancy contributes to a range of adverse perinatal outcomes; but is a potentially modifiable behavior.
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Identification of health risk behaviours among adolescent refugees resettling in Western AustraliaWe aim to identify health risk behaviours among adolescent refugees resettling in WA and assess the feasibility of using a standardised questionnaire.
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Assessing motor skills to inform a fetal alcohol spectrum disorder diagnosis focusing on persons older than 12 years: a systematic review of the literatureA systematic review of current evidence using various electronic databases was conducted. Studies were appraised using a recognised clinical appraisal tool.

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The Kids Research Institute Australia researcher a nominee for WA’s Young Australian of the YearDr Hayley Passmore is among four nominees for Western Australia’s 2022 Young Australian of the Year.
Research
Physical and mental health in mothers of children with Down syndrome.Physical and mental health in mothers of children with Down syndrome.
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Perinatal risk factors for childhood Type 1 diabetes in Western Australia - a population-based study (1980-2002)To investigate perinatal risk factors for childhood Type 1 diabetes in Western Australia, using a complete population-based cohort.
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Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medicationThe majority of staff and machine learning models were not more accurate than suggested by chance