Transtorno do Espectro Autista e Transtorno Desafiante de Oposição: Dificuldades no diagnostico / Transtorno del Espectro Autista y Trastorno Negativista Desafiante: Dificultades en el diagnóstico / Autism Spectrum Disorder and Oppositional Defiant Disorder: Difficulties in diagnosis

Introdução: O Transtorno do Espectro Autista e Transtorno Desafiante de Oposição, são desordens comumente diagnosticadas em indivíduos ainda na infância. Objetivo: Identificar possíveis fatores dificultadores no diagnóstico diferencial dos referidos transtornos. Metodologia: Foi realizada uma revisão integrativa da literatura, a qual selecionou artigos nas bases de dados Biblioteca Virtual de Saúde, periódico Coordenação de Aperfeiçoamento de Pessoal de Nível Superior e Periódicos Eletrônicos de Psicologia entre os meses de setembro e outubro de 2021. Para tanto, foram utilizadas as palavras chaves Transtorno do Espectro Autista, autismo, Transtorno Desafiante de Oposição, Transtorno Opositor Desafiador, diagnóstico, comorbidades, comportamentos disruptivos e dificuldades diagnósticas. Resultados: Oito artigos foram selecionados para extração de dados. O diagnóstico correto desses transtornos pode ser desafiador devido à sobreposição de sinais com outros transtornos e comorbidades, bem como à diversidade presente no espectro autista e à variedade de manifestações dos transtornos disruptivos. Além disso, a maioria dos estudos destacam os prejuízos na área da comunicação, o comprometimento na área social e os graus de severidade, como sendo características semelhantes entre os dois transtornos, podendo serem possíveis fatores que podem dificultar no diagnóstico do Transtorno do Espectro Autista e Transtorno Desafiante de Oposição, de maneira diferencial ou concomitante. Conclusões: O número de pesquisas relacionadas aos transtornos citados acima é inferior ao que se faz necessário para melhor conhecimento sobre o tema. No que diz respeito as pesquisas de materiais científicos, foram encontradas dificuldades para obtenção de estudos que estivessem de acordo com a nossa pesquisa. Com isso, faz-se necessário mais pesquisas que tentem investigar e compreender o porquê da escassez de material que estudem tais diagnósticos de maneira concomitante (AU).

Introduction: Autism Spectrum Disorder and Oppositional Defiant Disorderare disorders commonly diagnosed in individuals in childhood. Objective:Identify possible factors that hinder the differential diagnosis of these disorders. Methodology:An integrative review of the literature was carried out, which selected articles from the Virtual Health Library databases, Coordination for the Improvement of Higher Education Personnel journal and Electronic Psychology Journalsdatabases between the months of September and October 2021. To this end, the keywords Autistic Spectrum Disorder, autism, Disorder Defiant Disorder, Opposition, Oppositional Defiant Disorder, diagnosis, comorbidities, disruptive behaviors and diagnostic difficulties.Results:Eight articles were selected for data extraction. Correctly diagnosing these disorders can be challenging due to overlapping signs with other disorders and comorbidities, as well as the diversity present in the autism spectrum and the variety of manifestations of disruptive disorders. Furthermore, most studies highlight losses in the area of communication, impairment in the social area and degrees of severity, as being similar characteristics between the two disorders, and may be possible factors that can make it difficult to diagnose Autism Spectrum Disorder and Oppositional Defiant Disorder, differentially or concomitantly. Conclusions:The number of studies related to the disorders mentioned above is lower than what is needed for a better understanding of the subject. With regard to research on scientific materials, difficulties were encountered in obtaining studies that were in accordance with our research. With this, more research is needed to try to investigate and understand the reason for the scarcity of material that studies such diagnoses concomitantly (AU).

Introducción: El Trastorno del Espectro Autista y el Trastorno Negativista Desafiante son trastornos comúnmente diagnosticados en individuos en la infancia. Objetivo: Identificar posibles factores que puedan dificultar el diagnóstico diferencial de los trastornos antes mencionados.Metodología:Se realizó una revisión integrativa de la literatura, que seleccionó artículos en las bases de datos Biblioteca Virtual en Salud, revista Coordinación para el Perfeccionamiento del Personal de Educación Superior y Revistas Electrónicas de Psicología entre septiembre y octubre de 2021. Para ello, se utilizaron las palabras clave Trastorno del espectro autista, autismo, Trastorno negativista desafiante, Trastorno negativista desafiante, diagnóstico, comorbilidades, conductas disruptivas y dificultades diagnósticas. Resultados: Se seleccionaron ocho artículos para la extracción de datos. El diagnóstico correcto de estos trastornos puede ser un desafío debido a la superposición de síntomas con otros trastornos y comorbilidades, así como a la diversidad presente en el espectro del autismo y la variedad de manifestaciones de los trastornos disruptivos. Además, la mayoría de los estudios destacan las deficiencias en el área de la comunicación, la deficiencia en el área social y los grados de gravedad, como características similares entre ambos trastornos, que pueden ser posibles factores que dificulten el diagnóstico del Trastorno del Espectro Autista y Trastorno de Oposición Desafiante, ya sea de forma diferencial o concomitante. Conclusiones: El número de estudios relacionados con los trastornos antes mencionados es inferior al necesario para una mejor comprensión del tema. En cuanto a la investigación sobre materiales científicos, se encontraron dificultades para obtener estudios que estuvieran de acuerdo con nuestra investigación. Con esto, se necesita más investigación para tratar de investigar y comprender la razón de la escasez de material que estudie dichos diagnósticos de forma concomitante (AU).

Seeing through a robot's eyes: A cross-sectional exploratory study in developing a robotic screening technology for autism.

The present exploratory cross-sectional case-control study sought to develop a reliable and scalable screening tool for autism using a social robot. The robot HUMANE, installed with computer vision and linked with recognition technology, detected the direction of eye gaze of children. Children aged 3-8 (M = 5.52; N = 199) participated, 87 of whom had been confirmed with autism, 55 of whom were suspected to have autism, and 57 of whom were not considered to cause any concern for having autism. Before a session, a human experimenter instructed HUMANE to narrate a story to a child. HUMANE prompted the child to return his/her eye gaze to the robot if the child looked away, and praised the child when it re-established its eye gaze quickly after a prompt. The reliability of eye gaze detection was checked across all pairs of human raters and HUMANE and reached 0.90, indicating excellent interrater agreement. Using the pre-specified reference standard (Autism Spectrum Quotient), the sensitivity and specificity of the index tests (i.e., the number of robot prompts and duration of inattentiveness) reached 0.88 or above and the Diagnostic Odds Ratios were beyond 190. These results show that social robots may detect atypical eye patterns, suggesting a potential future for screening autism using social robots.

The influence of loss to follow-up in autism screening research: Taking stock and moving forward.

BACKGROUND: How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions-but only through careful consideration of children who do not complete diagnostic follow-up. METHODS: We developed two simulation models that re-analyze the results of a large-scale validation study of the M-CHAT-R/F by Robins et al. (2014, Pediatrics, 133, 37). Model #1 re-analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow-up. Model #2 builds on this by closely examining differential attrition at each point of the multi-step detection process. RESULTS: Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow-up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow-up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. CONCLUSIONS: Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process-including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision-making regarding follow-up may contribute more to detection than is widely assumed.

Applying autism screening research to real-world scenarios: a commentary on Sheldrick et al. (2023).

Early identification of autism spectrum disorder (ASD) continues to be a challenge despite universal screening efforts. One explanation is that screening tools have lower sensitivity and specificity than initial studies report when accounting for incomplete follow-up for all children screened. Sheldrick and colleagues used statistical modeling to demonstrate the impact on sensitivity and specificity when assumptions about the diagnostic outcome of children who do not pursue diagnostic evaluation are altered. Crucially, the work of Sheldrick et al. serves as a reminder that autism screening in primary care is just one component of the clinical assessment and should not be conflated with a diagnostic evaluation. Thus, lack of follow-up after a positive screen is a feature, not only a bug when using a screen in a clinical setting. Engaging families in shared decision-making around screening may help encourage follow-up, and thus, screening tool psychometric performance.

Is tuberous sclerosis complex-associated autism a preventable and treatable disorder?

BACKGROUND: Tuberous sclerosis complex (TSC) is a genetic disorder caused by inactivating mutations in the TSC1 and TSC2 genes, causing overactivation of the mechanistic (previously referred to as mammalian) target of rapamycin (mTOR) signaling pathway in fetal life. The mTOR pathway plays a crucial role in several brain processes leading to TSC-related epilepsy, intellectual disability, and autism spectrum disorder (ASD). Pre-natal or early post-natal diagnosis of TSC is now possible in a growing number of pre-symptomatic infants. DATA SOURCES: We searched PubMed for peer-reviewed publications published between January 2010 and April 2023 with the terms "tuberous sclerosis", "autism", or "autism spectrum disorder"," animal models", "preclinical studies", "neurobiology", and "treatment". RESULTS: Prospective studies have highlighted that developmental trajectories in TSC infants who were later diagnosed with ASD already show motor, visual and social communication skills in the first year of life delays. Reliable genetic, cellular, electroencephalography and magnetic resonance imaging biomarkers can identify pre-symptomatic TSC infants at high risk for having autism and epilepsy. CONCLUSIONS: Preventing epilepsy or improving therapy for seizures associated with prompt and tailored treatment strategies for autism in a sensitive developmental time window could have the potential to mitigate autistic symptoms in infants with TSC.

Factores asociados al diagnóstico tardío del Trastorno del Espectro Autista / Factors associated with delayed diagnosis of Autism Spectrum Disorder

El trastorno del espectro autista (TEA) es una condición crónica del neurodesarrollo caracterizada por déficits persistentes en la comunicación e interacción social y un patrón de intereses restringidos y/o comportamientos repetitivos que pueden afectar el funcionamiento del individuo en la vida diaria familiar y comunitaria. El diagnóstico oportuno intenta mejorar la trayectoria, reducir el impacto funcional y disminuir los efectos de condiciones médicas asociadas. El diagnóstico tardío de TEA es considerado como aquel realizado luego de los 6 años de edad, en coincidencia con el fin de la escolaridad inicial. Si bien esta edad puede resultar arbitraria lo que se busca es una generalización de aquellos casos en los que probablemente hubo múltiples pérdidas de oportunidades diagnósticas y terapéuticas. Objetivo: Reflexionar sobre los determinantes del diagnóstico tardío del TEA con el fin de proponer posibles soluciones a esta problemática. Desarrollo: A partir de tres viñetas clínicas de pacientes que recibieron el diagnóstico en nuestro servicio, luego de los 6 años de edad, nos proponemos identificar y analizar aquellos factores (motivos sociodemográficos, problemas organizacionales, en la etapa de evaluación diagnóstica, respecto al género, cuidadores/ familiares y características clínicas) que determinan la demora diagnóstica. Conclusiones: El diagnóstico tardío del TEA es una problemática compleja y multifactorial, que implica desafíos significativos en el desarrollo de los NNyA con esta condición, sus familias y su entorno. Es importante considerar las causas que demoran el diagnóstico, desde el ámbito clínico, familiar y socio-ambiental para poder intervenir oportunamente (AU)

Autism spectrum disorder (ASD) is a chronic neurodevelopmental condition characterized by persistent deficits in communication and social interaction and a pattern of restricted interests and/or repetitive behaviors that can affect the individual's daily functioning both at home and in the community. Early diagnosis is important to improve the developmental trajectory, reduce functional impairment, and decrease the impact of comorbid medical conditions. Delayed diagnosis of ASD is defined as a diagnosis made after the age of 6 years, coinciding with the end of preschool. Although this age may be arbitrary, it serves to encompass cases in which there were probably multiple missed diagnostic and therapeutic opportunities. Objective: To explore the causes of late diagnosis of ASD in order to propose possible solutions to this problem. Development: Based on three clinical vignettes of patients who were diagnosed at our department after 6 years of age, we aimed to identify and analyze factors influencing diagnostic delays. These factors included sociodemographic causes, organizational challenges, issues during the diagnostic workup, considerations related to gender, caregivers/families, and clinical characteristics. Conclusions: Delayed diagnosis of ASD is a complex and multifactorial problem leading to significant challenges in the development of children and adolescents with this condition as well as their families and their environment. Identification of the causes of diagnostic delay is important from the clinical, family and socio-environmental point of view, in order to start timely interventions (AU)

Prevalence and nature of prior developmental and medical concerns in toddlers who screen positive for autism in primary care.

LAY ABSTRACT: The American Academy of Pediatrics recommends that all children be screened for autism at their 18- and 24-month well-child visit. For children who screen positive for autism, it is unknown whether this usually represents the first time a developmental concern has been raised or if other developmental concerns typically precede a positive autism screen. Such knowledge could help guide providers in how to appropriately convey feedback regarding autism screening. This study found that, for close to 80% of children with a positive autism screen, caregivers or providers had a prior autism, language, motor, or other developmental concern documented in the electronic health record. Many also had other prior concerns frequently linked to autism, such as sleep and gastrointestinal problems, and received physical or speech therapy. On average, prior to screening children who received a positive Modified-Checklist for Autism in Toddlers had two documented concerns by at 1 year of age and three concerns by 2 years of age. These findings imply that screening for autism as a part of routine pediatric care likely takes place in the context of larger conversations regarding existing developmental concerns, allowing for a less stigmatizing discussion of autism. Framing the presence of prior concerns in the setting of a positive screen in this context may create a reaffirming space for existing caregiver concerns and a lessened emotional burden on caregivers.

Factor analysis of the autism spectrum screening questionnaire in a population-based child sample.

PURPOSE: The aim of this study was to investigate several possible factor structures of the Autism Spectrum Screening Questionnaire (ASSQ). MATERIALS AND METHODS: We used the 27-item screening tool for school-aged children in a general population of 8-year-old children (n = 3,538) and compared the occurring solutions to previously published factor models. RESULTS: A one-factor solution and a four-factor solution were identified in Exploratory Factor Analysis (EFA) and confirmed with Confirmatory Factor Analysis (CFA), while two-, three-, five- and six-factor solutions were rejected. In CFA, our four-factor solution showed the best goodness-of-fit indexes when compared with factor models previously presented by Posserud et al. and Ehlers et al. CONCLUSIONS: The results indicate a strong underlying connection between all ASSQ items which is elicited by the one-factor solution. Although as a screening tool, ASSQ is functioning with the unifactorial solution, the four factors can help to identify certain clusters of autism spectrum traits.

Meta-analysis of the Modified Checklist for Autism in Toddlers, Revised/Follow-up for Screening.

CONTEXT: The Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) is used worldwide to screen for autism spectrum disorder (ASD). OBJECTIVE: To calculate psychometric properties of the M-CHAT-R/F for subsequent diagnosis of ASD. DATA SOURCES: Systematic searches of Medline, Embase, SCOPUS, and Trip Pro databases from January 2014 to November 2021. STUDY SELECTION: Studies were included if they (1) used the M-CHAT-R/F (2) applied standard scoring protocol, (3) used a diagnostic assessment for ASD, and (4) reported at least 1 psychometric property of the M-CHAT-R/F. DATA EXTRACTION: Two independent reviewers completed screening, full-text review, data extraction, and quality assessment, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A random-effects model was used to derive pooled estimates and assess for between-study heterogeneity. RESULTS: Of 667 studies identified, 15 with 18 distinct samples from 10 countries (49 841 children) were used in the meta-analysis. Pooled positive predictive value (PPV), was 57.7% (95% confidence interval [CI] 48.6-66.8, τ2 = 0.031). PPV was higher among high-risk (75.6% [95% CI 66.0-85.2]) than low-risk samples (51.2% [95% CI 43.0-59.5]). Pooled negative predictive value was 72.5% (95% CI 62.5-82.4 τ2 = 0.031), sensitivity was 82.6% (95% CI 76.2-88.9) and specificity 45.7% (95% CI 25.0-66.4). LIMITATIONS: Negative predictive value, sensitivity, and specificity were calculated based on small sample sizes because of limited or no evaluation of screen-negative children. CONCLUSIONS: These results support use of the M-CHAT-R/F as a screening tool for ASD. Caregiver counseling regarding likelihood of an ASD diagnosis after positive screen should acknowledge the moderate PPV.

Development and validation of a machine learning-based tool to predict autism among children.

Autism is a lifelong condition for which intervention must occur as early as possible to improve social functioning. Thus, there is great interest in improving our ability to diagnose autism as early as possible. We take a novel approach to this challenge by combining machine learning with maternal and infant health administrative data to construct a prediction model capable of predicting autism disorder (defined as ICD10 84.0) in the general population. The sample included all mother-offspring pairs from the Australian state of New South Wales (NSW) between January 2003 and December 2005 (n = 262,650 offspring), linked across three health administrative data sets including the NSW perinatal data collection (PDC); the NSW admitted patient data collection (APDC) and the NSW mental health ambulatory data collection (MHADC). Our most successful model was able to predict autism disorder with an area under the receiver operating curve of 0.73, with the strongest risk factors for diagnoses found to include offspring gender, maternal age at birth, delivery analgesia, maternal prenatal tobacco disorders, and low 5-min APGAR score. Our findings indicate that the combination of machine learning and routinely collected admin data, with further refinement and increased accuracy than achieved by us, may play a role in the early detection of autism disorders.

Validation of the Electronic Modified Checklist for Autism in Toddlers, Revised with Follow-Up: A Nonrandomized Controlled Trial.

OBJECTIVE: To examine the classification rates and screening properties, including sensitivity and specificity, of the web-based Modified Checklist for Autism in Toddler, Revised with Follow-Up (M-CHAT-R/F) compared with paper-phone administration, and to determine the extent to which electronic M-CHAT-R/F streamlines screening, increases screening fidelity, increases diagnostic evaluation participation, and decreases waiting time from screening to evaluation compared with paper-phone modality. STUDY DESIGN: Primary-care practices in urban and suburban settings administered either the web-based or paper-phone M-CHAT-R/F using a prospective nonrandomized control design. Toddlers (n = 17 900) were screened between 2009 and 2016 at routine well-child check-ups. Toddlers who screened at risk on the M-CHAT-R/F were invited to complete diagnostic evaluations; 176 children were diagnosed with autism. The χ2, Fisher exact, and t-tests, as well as regression and screening properties, were used to compare outcome distributions, screening properties, and implementation by modality. RESULTS: Classification rates of the initial M-CHAT-R into low, medium, and high risk were significantly different across modalities with very small effect sizes. Sensitivity and specificity were high across both modalities. For children in the medium-risk range, the web-based modality had a greater rate of predicting risk for autism after Follow-Up compared with the paper-phone modality, and the web eliminated delay between initial screen and Follow-Up. The web-based modality showed increased screening fidelity, no data loss, and similar rates of evaluation attendance and time to evaluation from Follow-Up administration. CONCLUSIONS: The web-based M-CHAT-R/F is a valid tool for universal autism screening. Systems-level decisions should balance the increased feasibility of the electronic administration with the increase in Follow-Up accuracy provided by skilled clinician interview.

Sex differences in early autism screening using the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F).

LAY ABSTRACT: This study examined a widely used autism screening tool, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up to identify differences in screening for autism between toddler males and females. Examining sex differences in screening for autism in toddlerhood is important as it determines who will be referred for evaluations and receive diagnoses, which is critical for access to autism-specific early intervention. This study found that females were less likely to screen positive and be invited for evaluations compared with males. Females at high likelihood for autism were less likely to be diagnosed with autism, which decreases confidence in the screener's results. Importantly, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up accurately identified both males and females with autism. Future research should examine ways to improve accuracy in screening results for females.

Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach.

BACKGROUND: Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. METHODS: Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader-Willi n = 278, Lowe n = 89, Smith-Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein-Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan-McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. RESULTS: Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. LIMITATIONS: The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. CONCLUSIONS: These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

Early Diagnosis of Autism Spectrum Disorder: What the Pediatricians Should Know.

Autism is a spectrum disorder marked by considerable heterogeneity and characterized by impairments in the social communication domain along with the presence of restricted and repetitive behaviors or interests. Comprehensive autism evaluation generally consists of assessments by a multidisciplinary team. Having multiple specialists in the evaluation team aids in diagnosis and in chalking out a comprehensive management plan. Diagnosis is generally based on detailed developmental history, clinical judgment, and the use of standardized diagnostic instruments. Differential diagnosis is complicated as many of the mental health and neurodevelopmental conditions that routinely coexist with autism also have some symptoms that overlap with autism. Several barriers are linked to delay in diagnosis including lack of comfort in diagnosing autism by primary care providers, delayed referrals, the inability of parents to raise critical developmental concerns, confusion of autism with other conditions, and health system that is not responsive to the needs of the underserved communities. The etiology of autism spectrum disorder (ASD) is complex and still not completely understood; it involves genetics, neurobiology, and environmental exposures, leading to a diverse presentation of behaviors and symptoms. There is an imperative need to start therapeutic interventions as soon as a diagnosis of autism is suspected rather than wait for a definitive diagnosis. Early diagnosis is vital as timely intervention can lead to better outcomes for children and their families.

Sex- and age-related differences in autistic behaviours in children with neurofibromatosis type 1.

This study investigated sex and age differences in autistic behaviours in children with neurofibromatosis type 1 (NF1) who scored within the clinical range on the Social Responsiveness Scale - Second Edition (T score ≥ 60). Thirty-four males and 28 females (3-16 years) were assessed with the Autism Diagnostic Observation Schedule - Second Edition and Autism Diagnostic Interview - Revised. Across both measures, males exhibited greater social communication deficits relative to females. Age-related abatement of social communication difficulties was observed for males but not females. Conversely, no sex differences were found for restricted/repetitive behaviours, which were stable over time for both males and females. The findings are discussed within the context of broader neurodevelopmental considerations that are common in NF1.

Cultural Adaptation and Validation of the Modified Checklist for Autism in Toddlers, Revised with Follow-up in Moroccan Arabic dialect.

BACKGROUND: The prevalence of autism spectrum disorder is increasing worldwide, making screening and early intervention necessary. Several screening instruments have been developed in recent years. The Modified Checklist for Autism in Toddlers Revised with Follow-up (M-CHAT-R/F) is considered to be one of the specific measures designed to identify toddlers at risk for autistic spectrum disorder. OBJECTIVE: The aim of the study was to translate and adapt the original version of M-CHAT-R/F from the English to the Moroccan Arabic language. STUDY DESIGN: Specialized translators and clinicians ensured forward and backward translation of the scale into Moroccan Arabic. Then, a two-stage screening of the M-CHAT-R/F-T was applied to a study sample comprised of 56 toddlers with autistic spectrum disorder (category I) and 96 toddlers with normal development (category II). "Kappa test", "Cronbach's alpha" test, the intra class correlation coefficient, and the area under the curve were determined. RESULT: The average score results of M-CHAT-R/F were 13.12 for category I, while it was 2.24 for category II. The Cronbach's alpha coefficient of the checklist was 0.929. The kappa values ranged from k=0.78 to k=0.97 with a confidence interval of 95% indicating good convergence. The intra-class correlation coefficient ranged from 0.97 to 0.99, which is excellent. The area under the curve in our study was 0.988, an excellent result. CONCLUSION: Efficiency of the Moroccan Arabic version of the MCHAT was demonstrated for screening in the general population.

The implementation of the screening tool for autism in toddlers in Part C early intervention programs: An 18-month follow-up.

LAY ABSTRACT: The early detection of autism spectrum disorder can lead to access to autism spectrum disorder-specific services that have been shown to have a large impact on a child's overall development. Although a stable diagnosis of autism spectrum disorder can be made by age 2 years, most children are not diagnosed until much later. To address this issue, this study examined the effectiveness of training Part C Early Intervention providers to use an interactive autism spectrum disorder screening tool, the Screening Tool for Autism in Toddlers. Sixty-nine providers attended a 1-day training workshop on the use of the Screening Tool for Autism in Toddlers. After the workshop, providers reported increased knowledge about recognizing the early signs of autism spectrum disorder, and about 45% of the providers reported using the Screening Tool for Autism in Toddlers with families in their caseloads 18 months after the training. These results suggest that the Screening Tool for Autism in Toddlers is feasible for use within Early Intervention settings. In addition, they suggest that specific providers might serve as a screening "point-person," rather than expecting the Screening Tool for Autism in Toddlers to be used by all providers. Future research should aim to identify specific characteristics of agencies or providers that might be best suited for using the Screening Tool for Autism in Toddlers.