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ObjectiveThe objective of this study was to explore the potential of the Infant/Toddler Sensory Profile (ITSP) as a screening tool for autism spectrum disorders (ASD) in prematurely born children.MethodsParents of 157 children with birth weights <1,500 g (aged 2 years, corrected for prematurity; 88 boys, 69 girls) completed a screening battery that included the ITSP, Modified Checklist for Autism in Toddlers (M-CHAT), and the Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist (CSBS-DP-ITC). Children with known disabilities were excluded. All the children who were screened positive on any of the screening tools subsequently underwent clinical examination including the Autism Diagnostic Observation Schedule.ResultsWe used classification trees to answer the question whether ITSP (or some of its subscales) could be combined with the M-CHAT and/or the CSBS-DP-ITC or its subscales into an effective ASD screening tool. Using the CSBS-DP-ITC, overall score, and the Sensation Seeking subscale of the ITSP, we obtained a screening tool that was able to identify all of the ASD children in our sample (confirmed by cross-validation). The proposed screening tool is scored as follows: 1) if the overall CSBS-DP-ITC value is <45.5, then the screening is positive; 2) if the overall CSBS-DP-ITC value is ≥45.5 and the z-score of the Sensation Seeking subscale of ITSP is ≥1.54, then the screening is positive; 3) otherwise, the screening is negative.ConclusionThe use of CSBS-DP-ITC in combination with the Sensation Seeking subscale of the ITSP improved the accuracy of autism screening in preterm children.

Modelling to generate alternatives (MGA)an approach to optimization aiming on providing not one, but more feasible solutions as different from each other as possible, all with the values of the objective function close to the optimal value (see e.g. Brill, Chang and Hopkins 1982 or Yeomans 2011).Kansei engineeringit is a consumer-oriented approach to product design based on the reflection of less tangible aspects such as feelings concerning the product in the design process. The aim is to inspire specific feelings by the features of the design alternative (see Nagamachi 1995;Jindo, Hirasago and Nagamachi 1995;or Kobayashi and Kinumura 2017).Kansei adjectives -Kansei words in the form of adjectives, i.e. words describing customers' or consumers' needs, feelings and perceptions concerning the product (see e.g. Jiao, Zhang and Helander (2006) for a Kansei mining system).Kansei taggroup or cluster of Kansei adjective corresponding to the same concept or basic emotion (Xu and Wunsch (2009) provide an example of a clustering algorithm suitable for the creation of Kanseiadjectives clusters, i.e. Kansei tags).Likert scalea psychometric measurement instrument popularized by Likert (see e.g. Likert ( 1932)) frequently used in questionnaires. Likert scales are discrete scales with linguistic labels on the agreedisagree or similar continuums, which are supposed to be symmetrical with respect to the middle point (either present in the scale itself, or theoretical; e.g. strongly agree, agree, undecided, disagree, strongly disagree) of the scale. Usually the equidistance of the scale-values is assumed.Semantic differentiala method proposed by Osgood, Suci and Tannenbaum (1957) for the measurement of attitudes. The method utilizes discrete bipolar-adjective scales to get input information and uses factor analysis to define the semantic space and represents the attitude towards a concept (or its connotative meaning) as a point in this n-dimensional semantic space.