Utility of the Language Use Inventory in Young Children at Elevated Likelihood of Autism
Bibliographic record
Abstract
Purpose: The aims of this study were (a) to evaluate the convergent validity of the Language Use Inventory (LUI) with measures of autism spectrum disorder (ASD) symptoms, language, and social skills and (b) to assess discriminant validity of the LUI with measures of nonlanguage skills, including daily living skills and motor development. Method: This study sample included participants from a longitudinal study ( n = 239) of infant siblings with elevated familial likelihood of ASD and lower familial likelihood. Assessment measures completed at 36 months included the LUI, the Autism Diagnostic Observation Schedule–Second Edition (ADOS-2), the Mullen Scales of Early Learning, and the Vineland Adaptive Behavior Scales–Second Edition. Bivariate Pearson correlations were estimated between ADOS-2 comparison scores and four language and social skills measures. Additional correlations were estimated between LUI total scores and standard scores from nonlanguage measures. A series of Fisher's Z transformations were applied to evaluate whether bivariate correlations were significantly different. Results: All four language and social skill measures were moderately to strongly associated with each other and ASD symptom severity scores. The correlation between ADOS-2 comparison scores and LUI total scores was significantly stronger than ADOS-2 correlations with all other measures. Conclusions: Our findings provide support for the LUI as a feasible, pragmatic language–targeted instrument for inclusion in early developmental evaluations prompted by language concerns. Administration of the LUI may accelerate earlier referral for a comprehensive assessment of ASD symptoms. Given the high correlation with ADOS-2 scores, an LUI total score in a clinical range of concern may encourage a clinician to refer families for a full diagnostic evaluation of ASD.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".