The ability to identify children who require specialist assessment for the possibility of autism at as early an age as possible has become a growing area of research. A number of measures have been developed as potential screening tools for autism. The reliability and validity of one of these measures for screening for autism in young children with developmental problems was evaluated. The parents of 207 children aged 20-51 months completed the Developmental Checklist-Early Screen (DBC-ES), prior to their child undergoing assessment. Good interrater agreement and internal consistency was found, along with significant correlations with a clinician completed measure of autism symptomatology. High sensitivity was found, with lower specificity for the originally proposed 17-item screening tool and a five-item version.
"Evidence of accelerated head circumference (HC) or macrocephaly and body growth during infancy in children with ASDs is well supported in the literature, although variation in the timing of acceleration across studies exists [106, 109, 119]. Such accelerated growth has even been suggested as an early biological indicator of ASD within the first 12 months of life [120, 121]. Research investigating whether abnormally large HC during the early years can be a reliable indicator of ASD is supported by findings that HC during the early years more accurately reflects brain volume than that during adolescence and a crucial factor for the analysis of ASD onset is the timing of the increase in HC in infancy and toddlerhood [120, 122, 123]. "
[Show abstract][Hide abstract] ABSTRACT: Despite the widely-held understanding that the biological changes that lead to autism usually occur during prenatal life, there has been relatively little research into the functional development of the brain during early infancy in individuals later diagnosed with autism spectrum disorder (ASD).
This review explores the studies over the last three years which have investigated differences in various brain regions in individuals with ASD or who later go on to receive a diagnosis of ASD.
We used PRISMA guidelines and selected published articles reporting any neurological abnormalities in very early childhood in individuals with or later diagnosed with ASD.
Various brain regions are discussed including; the amygdala; cerebellum; frontal cortex and lateralised abnormalities of the temporal cortex during language processing. This review discusses studies investigating head circumference, electrophysiological markers and inter-hemispheric synchronisation. All the recent findings from the beginning of 2009 across these different aspects of defining neurological abnormalities are discussed in light of earlier findings.
The studies across these different areas reveal the existence of atypicalities in the first year of life, well before ASD is reliably diagnosed. Cross-disciplinary approaches are essential to elucidate the pathophysiological sequence of events that lead to ASD.
[Show abstract][Hide abstract] ABSTRACT: The predictive utility of mathematics curriculum-based measurement (MCBM) to identify students who are at risk for failure on important educational measures is an emerging area of study in need of further investigation. The present study sought to identify which of four MCBM probes could be accurately used to determine students' risk status on selected subtests of three important educational measures commonly used to make educational placement decisions (WIAT-II, WJ-ACH-III, and KM 3) in grades 2 (n = 49), 4 (n = 48), and 6 (n = 47). The study also sought to determine which type of student performance measurement strategy (i.e., level, slope, or dual discrepancy) on each of the four types of MCBM probes proved to be the best method to determine student risk status. The results of the study indicated that the ability of the MCBM probes to identify students' risk status was generally poor. However, evidence indicated that MCBM probes could be used more reliably and accurately to determine students in the low risk category than those in the high risk category across all probe types and administration times. Finally, the level method generated the greatest support and the slope method generated the least support for identification of high and low risk student status on each probe or combination of probes.
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