Identifying early language predictors: A replication of Gasparini et al. (2023) confirming applicability in a general population cohort.

BACKGROUND: Identifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it. AIMS: To validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome. METHODS & PROCEDURES: A total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11-12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent-reported variables from ages 2-3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11-12 years. Random forests were used to identify any additional variables predictive of language outcome. OUTCOMES & RESULTS: Complete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors. CONCLUSIONS & IMPLICATIONS: We identified an ultra-short set of variables that predicts 11-12-year language outcome with 'fair' accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population-based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context. WHAT THIS PAPER ADDS: What is already known on the subject There are no robust predictor sets of child language disorder despite its prevalence and far-reaching impacts. A previous study identified six variables collected at age 2-3 years that predicted 11-12-year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort. What this study adds to the existing knowledge We used machine learning methods to identify a set of six questions asked at age 2-3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11-12 years, now showing consistent results across two large-scale population-based cohort studies. What are the potential or clinical implications of this work? This predictor set is more accurate than existing feasible methods and can be translated into a low-resource and time-efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties.

Using machine-learning methods to identify early-life predictors of 11-year language outcome.

BACKGROUND: Language is foundational for neurodevelopment and quality of life, but an estimated 10% of children have a language disorder at age 5. Many children shift between classifications of typical and low language if assessed at multiple times in the early years, making it difficult to identify which children will have persisting difficulties and benefit most from support. This study aims to identify a parsimonious set of preschool indicators that predict language outcomes in late childhood, using data from the population-based Early Language in Victoria Study (n = 839). METHODS: Parents completed surveys about their children at ages 8, 12, 24, and 36 months. At 11 years, children were assessed using the Clinical Evaluation of Language Fundamentals 4th Edition (CELF-4). We used random forests to identify which of the 1990 parent-reported questions best predict children's 11-year language outcome (CELF-4 score ≤81 representing low language) and used SuperLearner to estimate the accuracy of the constrained sets of questions. RESULTS: At 24 months, seven predictors relating to vocabulary, symbolic play, pragmatics and behavior yielded 73% sensitivity (95% CI: 57, 85) and 77% specificity (95% CI: 74, 80) for predicting low language at 11 years. [Corrections made on 5 May 2023, after first online publication: In the preceding sentence 'motor skills' has been corrected to 'behavior' in this version.] At 36 months, 7 predictors relating to morphosyntax, vocabulary, parent-child interactions, and parental stress yielded 75% sensitivity (95% CI: 58, 88) and 85% specificity (95% CI: 81, 87). Measures at 8 and 12 months yielded unsatisfactory accuracy. CONCLUSIONS: We identified two short sets of questions that predict language outcomes at age 11 with fair accuracy. Future research should seek to replicate results in a separate cohort.

Synthesizing Core Outcome Sets for outcomes research in cohort studies: a systematic review.

BACKGROUND: Life course studies are designed to "collect once, use multiple times" for observational and, increasingly, interventional research. Core Outcome Sets (COS) are minimum sets developed for clinical trials by multi-stakeholder consensus methodologies. We aimed to synthesize published COS that might guide outcomes selection for early life cohorts with an interventional focus. METHODS: We searched PubMed, Medline, COMET, and CROWN for COS published before January 2021 relevant to four life stages (pregnancy, newborns, children <8 years, and parents (adults aged 18-50 years)). We synthesized core outcomes into overarching constructs. RESULTS: From 46 COS we synthesized 414 core outcomes into 118 constructs. "Quality of life", "adverse events", "medication use", "hospitalization", and "mortality" were consistent across all stages. For pregnancy, common constructs included "preterm birth", "delivery mode", "pre-eclampsia", "gestational weight gain", "gestational diabetes", and "hemorrhage"; for newborns, "birthweight", "small for gestational age", "neurological damage", and "morbidity" and "infection/sepsis"; for pediatrics, "pain", "gastrointestinal morbidity", "growth/weight", "breastfeeding", "feeding problems", "hearing", "neurodevelopmental morbidity", and "social development"; and for adults, "disease burden", "mental health", "neurological function/stroke", and "cardiovascular health/morbidity". CONCLUSION: This COS synthesis generated outcome constructs that are of high value to stakeholders (participants, health providers, services), relevant to life course research, and could position cohorts for trial capabilities. IMPACT: We synthesized existing Core Outcome Sets as a transparent methodology that could prioritize outcomes for lifecourse cohorts with an interventional focus. "Quality of life", "adverse events", "medication use", "hospitalization", and "mortality" are important outcomes across pregnancy, newborns, childhood, and early-to-mid-adulthood (the age range relevant to parents). Other common outcomes (such as "birthweight", "cognitive function/ability", "psychological health") are also highly relevant to lifecourse research. This synthesis could assist new early life cohorts to pre-select outcomes that are of high value to stakeholders (participants, health providers, services), are relevant to lifecourse research, and could position them for future trials and interventional capability.

Ten easy steps to conducting transparent, reproducible meta-analyses for infant researchers.

Meta-analyses provide researchers with an overview of the body of evidence in a topic, with quantified estimates of effect sizes and the role of moderators, and weighting studies according to their precision. We provide a guide for conducting a transparent and reproducible meta-analysis in the field of developmental psychology within the framework of the MetaLab platform, in 10 steps: (1) Choose a topic for your meta-analysis, (2) Formulate your research question and specify inclusion criteria, (3) Preregister and document all stages of your meta-analysis, (4) Conduct the literature search, (5) Collect and screen records, (6) Extract data from eligible studies, (7) Read the data into analysis software and compute effect sizes, (8) Visualize your data, (9) Create meta-analytic models to assess the strength of the effect and investigate possible moderators, (10) Write up and promote your meta-analysis. Meta-analyses can inform future studies, through power calculations, by identifying robust methods and exposing research gaps. By adding a new meta-analysis to MetaLab, datasets across multiple topics of developmental psychology can be synthesized, and the dataset can be maintained as a living, community-augmented meta-analysis to which researchers add new data, allowing for a cumulative approach to evidence synthesis.

Quantifying the role of rhythm in infants' language discrimination abilities: A meta-analysis.

More than 30 years have passed since Mehler et al. (1988) proposed that newborns can discriminate between languages that belong to different rhythm classes: stress-, syllable- or mora-timed. Thereupon they developed the hypothesis that infants are sensitive to differences in vowel and consonant interval durations as acoustic correlates of rhythm classes. It remains unknown exactly which durational computations infants use when perceiving speech for the purposes of distinguishing languages. Here, a meta-analysis of studies on infants' language discrimination skills over the first year of life was conducted, aiming to quantify how language discrimination skills change with age and are modulated by rhythm classes or durational metrics. A systematic literature search identified 42 studies that tested infants' (birth to 12 months) discrimination or preference of two language varieties, by presenting infants with auditory or audio-visual continuous speech. Quantitative data synthesis was conducted using multivariate random effects meta-analytic models with the factors rhythm class difference, age, stimulus manipulation, method, and metrics operationalising proportions of and variability in vowel and consonant interval durations, to explore which factors best account for language discrimination or preference. Results revealed that smaller differences in vowel interval variability (△V) and larger differences in successive consonantal interval variability (rPVI-C) were associated with more successful language discrimination, and better accounted for discrimination results than the factor rhythm class. There were no effects of age for discrimination but results on preference studies were affected by age: the older infants get, the more they prefer non-native languages that are rhythmically similar to their native language, but not non-native languages that are rhythmically distinct. These findings can inform theories on language discrimination that have previously focussed on rhythm class, by providing a novel way to operationalise rhythm in language in the extent to which it accounts for infants' language discrimination abilities.