Consistency and reliability of automated language measures across expressive language samples in autism.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with substantial clinical heterogeneity, especially in language and communication ability. There is a need for validated language outcome measures that show sensitivity to true change for this population. We used Natural Language Processing to analyze expressive language transcripts of 64 highly-verbal children and young adults (age: 6-23 years, mean 12.8 years; 78.1% male) with ASD to examine the validity across language sampling context and test-retest reliability of six previously validated Automated Language Measures (ALMs), including Mean Length of Utterance in Morphemes, Number of Distinct Word Roots, C-units per minute, unintelligible proportion, um rate, and repetition proportion. Three expressive language samples were collected at baseline and again 4 weeks later. These samples comprised interview tasks from the Autism Diagnostic Observation Schedule (ADOS-2) Modules 3 and 4, a conversation task, and a narration task. The influence of language sampling context on each ALM was estimated using either generalized linear mixed-effects models or generalized linear models, adjusted for age, sex, and IQ. The 4 weeks test-retest reliability was evaluated using Lin's Concordance Correlation Coefficient (CCC). The three different sampling contexts were associated with significantly (P < 0.001) different distributions for each ALM. With one exception (repetition proportion), ALMs also showed good test-retest reliability (median CCC: 0.73-0.88) when measured within the same context. Taken in conjunction with our previous work establishing their construct validity, this study demonstrates further critical psychometric properties of ALMs and their promising potential as language outcome measures for ASD research.

"Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability.

Pragmatic language difficulties, including unusual filler usage, are common among children with Autism Spectrum Disorder (ASD). This study investigated "um" and "uh" usage in children with ASD and typically developing (TD) controls. We analyzed transcribed Autism Diagnostic Observation Schedule (ADOS) sessions for 182 children (117 ASD, 65 TD), aged 4 to 15. Although the groups did not differ in "uh" usage, the ASD group used fewer "ums" than the TD group. This held true after controlling for age, sex, and IQ. Within ASD, social affect and pragmatic language scores did not predict filler usage; however, structural language scores predicted "um" usage. Lower "um" rates among children with ASD may reflect problems with planning or production rather than pragmatic language.

Combining voice and language features improves automated autism detection.

Variability in expressive and receptive language, difficulty with pragmatic language, and prosodic difficulties are all features of autism spectrum disorder (ASD). Quantifying language and voice characteristics is an important step for measuring outcomes for autistic people, yet clinical measurement is cumbersome and costly. Using natural language processing (NLP) methods and a harmonic model of speech, we analyzed language transcripts and audio recordings to automatically classify individuals as ASD or non-ASD. One-hundred fifty-eight participants (88 ASD, 70 non-ASD) ages 7 to 17 were evaluated with the autism diagnostic observation schedule (ADOS-2), module 3. The ADOS-2 was transcribed following modified SALT guidelines. Seven automated language measures (ALMs) and 10 automated voice measures (AVMs) for each participant were generated from the transcripts and audio of one ADOS-2 task. The measures were analyzed using support vector machine (SVM; a binary classifier) and receiver operating characteristic (ROC). The AVM model resulted in an ROC area under the curve (AUC) of 0.7800, the ALM model an AUC of 0.8748, and the combined model a significantly improved AUC of 0.9205. The ALM model better detected ASD participants who were younger and had lower language skills and shorter activity time. ASD participants detected by the AVM model had better language profiles than those detected by the language model. In combination, automated measurement of language and voice characteristics successfully differentiated children with and without autism. This methodology could help design robust outcome measures for future research. LAY SUMMARY: People with autism often struggle with communication differences which traditional clinical measures and language tests cannot fully capture. Using language transcripts and audio recordings from 158 children ages 7 to 17, we showed that automated, objective language and voice measurements successfully predict the child's diagnosis. This methodology could help design improved outcome measures for research.

Estrogen and COVID-19 symptoms: Associations in women from the COVID Symptom Study.

It has been widely observed that adult men of all ages are at higher risk of developing serious complications from COVID-19 when compared with women. This study aimed to investigate the association of COVID-19 positivity and severity with estrogen exposure in women, in a population based matched cohort study of female users of the COVID Symptom Study application in the UK. Analyses included 152,637 women for menopausal status, 295,689 women for exogenous estrogen intake in the form of the combined oral contraceptive pill (COCP), and 151,193 menopausal women for hormone replacement therapy (HRT). Data were collected using the COVID Symptom Study in May-June 2020. Analyses investigated associations between predicted or tested COVID-19 status and menopausal status, COCP use, and HRT use, adjusting for age, smoking and BMI, with follow-up age sensitivity analysis, and validation in a subset of participants from the TwinsUK cohort. Menopausal women had higher rates of predicted COVID-19 (P = 0.003). COCP-users had lower rates of predicted COVID-19 (P = 8.03E-05), with reduction in hospital attendance (P = 0.023). Menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P = 2.22E-05) for HRT users alone. The findings support a protective effect of estrogen exposure on COVID-19, based on positive association between predicted COVID-19 with menopausal status, and negative association with COCP use. HRT use was positively associated with COVID-19, but the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential unaccounted for confounders and comorbidities.

A Pseudo-Value Approach to Analyze the Semantic Similarity of the Speech of Children With and Without Autism Spectrum Disorder.

Conversational impairments are well known among people with autism spectrum disorder (ASD), but their measurement requires time-consuming manual annotation of language samples. Natural language processing (NLP) has shown promise in identifying semantic difficulties when compared to clinician-annotated reference transcripts. Our goal was to develop a novel measure of lexico-semantic similarity - based on recent work in natural language processing (NLP) and recent applications of pseudo-value analysis - which could be applied to transcripts of children's conversational language, without recourse to some ground-truth reference document. We hypothesized that: (a) semantic coherence, as measured by this method, would discriminate between children with and without ASD and (b) more variability would be found in the group with ASD. We used data from 70 4- to 8-year-old males with ASD (N = 38) or typically developing (TD; N = 32) enrolled in a language study. Participants were administered a battery of standardized diagnostic tests, including the Autism Diagnostic Observation Schedule (ADOS). ADOS was recorded and transcribed, and we analyzed children's language output during the conversation/interview ADOS tasks. Transcripts were converted to vectors via a word2vec model trained on the Google News Corpus. Pairwise similarity across all subjects and a sample grand mean were calculated. Using a leave-one-out algorithm, a pseudo-value, detailed below, representing each subject's contribution to the grand mean was generated. Means of pseudo-values were compared between the two groups. Analyses were co-varied for nonverbal IQ, mean length of utterance, and number of distinct word roots (NDR). Statistically significant differences were observed in means of pseudo-values between TD and ASD groups (p = 0.007). TD subjects had higher pseudo-value scores suggesting that similarity scores of TD subjects were more similar to the overall group mean. Variance of pseudo-values was greater in the ASD group. Nonverbal IQ, mean length of utterance, or NDR did not account for between group differences. The findings suggest that our pseudo-value-based method can be effectively used to identify specific semantic difficulties that characterize children with ASD without requiring a reference transcript.

Evaluating atypical language in autism using automated language measures.

Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p [Formula: see text]), nonparametric ANOVAs showed significant group differences (p[Formula: see text]). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p[Formula: see text]) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9-75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.

Epidemiological surveys of ASD: advances and remaining challenges.

Recent worldwide epidemiological surveys of autism conducted in 37 countries are reviewed; the median prevalence of autism is .97% in 26 high-income countries. Methodological advances and remaining challenges in designing and executing surveys are discussed, including the effects on prevalence of variable case definitions and nosography, of reliance on parental reports only, case ascertainment through mainstream school surveys, innovative approaches to screen school samples more efficiently, and consideration of age in interpreting surveys. Directions for the future of autism epidemiology are discussed, including the need to systematically examine cross-cultural variation in phenotypic expression and developing surveillance programs.

A comprehensive hospital agile preparedness (CHAPs) tool for pandemic preparedness, based on the COVID-19 experience.

COVID-19 presents an unprecedented challenge to hospitals and the systems in which they operate. The primary exponential surge of COVID-19 cases is arguably the most devastating event a hospital will face. In some countries, these surges during the initial outbreak of the disease have resulted in hospitals suffering from significant resource strain, leading to excess patient mortality and negatively impacting staff wellbeing. As experience builds in managing these surges, it has become evident that agile, tailored planning tools are required. The comprehensive hospital agile preparedness (CHAPs) tool provides clinical planners with six key domains to consider that frequently create resource strain during COVID-19; it also allows local planners to identify issues unique to their hospital, system or region. Although this tool has been developed from COVID-19 experiences, it has potential to be modified for a variety of pandemic scenarios according to transmission modes, rates and critical care resource requirements.

Consensus Statement of Standards for Interventional Cardiovascular Nursing Practice.

Interventional cardiovascular nursing is a critical care nursing specialty providing complex nursing interventions to patients prone to clinical deterioration, through the combined risks of the pathophysiology of their illness and undergoing technically complex interventional cardiovascular procedures. No guidelines were identified worldwide to assist health care providers and educational institutions in workforce development and education guidelines to minimise patients' risk of adverse events. The Interventional Nurses Council (INC) developed a definition and scope of practice for interventional cardiac nursing (ICN's) in 2013. The INC executive committee established a working party of seven representatives from Australia and New Zealand. Selection was based on expertise in interventional cardiovascular nursing and experience providing education and mentoring in the clinical and postgraduate environment. A literature search of the electronic databases Science Direct, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline and Health Source was performed, using the search terms: clinical deterioration, ST elevation myocardial infarction, vital signs, primary percutaneous coronary intervention, PCI, AMI, STEMI, acute coronary syndrome, peri-procedural care, unstable angina, PCI complications, structural heart disease, TAVI, TAVR, cardiac rhythm management, pacing, electrophysiology studies, vascular access, procedural sedation. Articles were limited to the cardiac catheterisation laboratory and relevance to nursing based outcomes. Reference lists were examined to identify relevant articles missed in the initial search. The literature was compared with national competency standards, quality and safety documents and the INC definition and scope of practice. Consensus of common themes, a taxonomy of education and seven competency domains were achieved via frequent teleconferences and two face-to-face meetings. The working party finalised the standards on 14 July 2017, following endorsement from the CSANZ, INC, Heart Rhythm Council, CSANZ Quality Standards Committee and the Australian College of Critical Care Nurses (ACCCN). The resulting document provides clinical practice and education standards for interventional cardiac nursing practice.

Quantitative analysis of disfluency in children with autism spectrum disorder or language impairment.

Deficits in social communication, particularly pragmatic language, are characteristic of individuals with autism spectrum disorder (ASD). Speech disfluencies may serve pragmatic functions such as cueing speaking problems. Previous studies have found that speakers with ASD differ from typically developing (TD) speakers in the types and patterns of disfluencies they produce, but fail to provide sufficiently detailed characterizations of the methods used to categorize and quantify disfluency, making cross-study comparison difficult. In this study we propose a simple schema for classifying major disfluency types, and use this schema in an exploratory analysis of differences in disfluency rates and patterns among children with ASD compared to TD and language impaired (SLI) groups. 115 children ages 4-8 participated in the study (ASD = 51; SLI = 20; TD = 44), completing a battery of experimental tasks and assessments. Measures of morphological and syntactic complexity, as well as word and disfluency counts, were derived from transcripts of the Autism Diagnostic Observation Schedule (ADOS). High inter-annotator agreement was obtained with the use of the proposed schema. Analyses showed ASD children produced a higher ratio of content to filler disfluencies than TD children. Relative frequencies of repetitions, revisions, and false starts did not differ significantly between groups. TD children also produced more cued disfluencies than ASD children.

WWW. Do not forget older people.

BACKGROUND: internet use continues to grow. It is often assumed that most users are younger or middle aged. We set out to establish how access to and use of the Internet varied with age. METHODS: we surveyed a sample of patients attending urology outpatient clinics in a one week period. RESULTS: use of the internet decreases with increasing age. However 75% of ages 65-69 and 55% of over 80's access the internet, with most doing so at least weekly. Whilst health-related information is very relevant, only 20% of patients had looked at the hospital departmental website prior to a visit. CONCLUSION: as health professionals we must ensure that we have relevant up-to-date information on our websites for patients to access of all age ranges. Departments should give consideration to courses or other novel methods (e.g. computers in GP surgeries, text to speech software, etc) to improve internet access in older people.