Early expressive and receptive language trajectories in high-risk infant siblings of children with autism spectrum disorder
Bibliographic record
Abstract
Background & aims In response to limited research on early language development in infants at high risk for Autism Spectrum Disorder (ASD), the current prospective study examined early expressive and receptive language trajectories in familial high-risk (HR) infants who were and were not later diagnosed with ASD (HR-ASD and HR-N, respectively), and low-risk (LR) controls with no family history of ASD. Methods Participants were 523 children (371 HR siblings, 56% boys; 152 LR controls, 52% boys) followed from age 6 or 12 months to 36 months. Based on independent, best-estimate clinical diagnoses at 36 months, HR participants were classified as HR-ASD (n = 94; 69% boys), or HR-N (n = 277; 52% boys); the sample also included 152 LR controls (52% boys). Expressive and receptive language trajectories were examined based on corresponding domain standard scores on the Mullen Scales of Early Learning ( MSEL) at 6, 12, 24, and 36 months. In the combined sample of HR and LR infants, semi-parametric group-based modeling was used to identify distinct trajectories in MSEL standard scores. Results A 3-group solution provided optimal fit to variation in both expressive and receptive language, with the following patterns of scores: (1) inclining from average to above average, (2) stable-average, and (3) declining from average to well below average. For both expressive and receptive language, membership in these trajectories was related to 3-year diagnostic outcomes. Conclusions Although HR-ASD, HR-N, and LR control infants were in each trajectory group, membership in the declining trajectory (expressive and/or receptive) was associated with an ASD diagnosis. Implications Evidence of declining trajectories in either expressive or receptive language may be a risk marker for ASD in a high-risk sample.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".