Exploring Trajectories of Language Development in Children with Autism Spectrum Disorder Across Multiple Measures
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a developmental disability that affects the cognitive development of up to 1 in 59 children globally, particularly in language abilities (Baio et al., 2018). With increasing prevalence and research showing the benefits of early intervention, there is value in diagnosing ASD as soon as possible. However, ASD is typically not diagnosed until after age two, when many developmental milestones should have passed and parental concerns may have already risen (Falck-Ytter, 2012; Landa et al., 2013). Research must include measures from earlier in childhood to improve diagnosis methods and capture a full picture of this disability. This study examined how different language measures capture the range of expressive and receptive language vocabulary in children from 6 to 36 months of age; longitudinally comparing children eventually diagnosed with ASD to typically developing peers. Children were assessed repeatedly using the Mullen Scales of Early Learning (MSEL; Mullen, 1995), MacArthur-Bates Communicative Development Inventory (CDI; Fenson et al., 2007), and One-Word Picture Vocabulary Tests (PVT; Martin & Brownell, 2011a; 2011b). Results from mixed regression analysis showed that most measures could distinguish children with ASD as a group by 24 months. However, the Expressive PVT did not distinguish the ASD group from typically developing groups, despite being correlated with all other measures. Further examination of individual trajectories for children with ASD showed high, but inconsistent heterogeneity from scale to scale. This combination of varying group and individual differences suggests that these common assessments may not capture children’s abilities in the same way or to the same extent. Thus, this study supports that, to accurately observe the wide range developmental trajectories seen in ASD, professionals must consider the characteristics of the tools being used. Capturing this developmental variability is vital for creating effective targeted early interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".