Proportion and Profile of Autistic Children Not Acquiring Spoken Language Despite Receiving Evidence-Based Early Interventions
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion and profile of preschoolers on the autism spectrum who do not acquire spoken language despite receiving evidence-supported interventions that target spoken language. METHODS: We examined an aggregate dataset comprising 707 preschoolers on the autism spectrum who had received evidence-supported interventions to determine the proportion and profile of those who experienced limited progress in spoken language. Interventions were delivered through programs affiliated with university research settings and ranged in duration from 6 to 24 months. Spoken language outcomes were determined from parent-report measures, which were validated against direct assessments and natural language samples. RESULTS: Approximately two-thirds of children who were non-speaking at baseline were using single words or more complex spoken language by intervention exit. Those who remained non-speaking had lower baseline motor imitation scores, derived mainly from parent reports. Approximately half of the children who were minimally speaking (i.e. had single words or no words) at baseline were combining words by intervention exit. Those who did not acquire word combinations had lower baseline scores in cognitive, social, adaptive and motor imitation measures, and shorter intervention duration. Age at intervention start influenced spoken language advancement differently depending on the initial spoken language level. The odds of acquiring spoken language did not differ based on the intervention received. CONCLUSIONS: Approximately one-third of children who had limited or no spoken language at baseline did not advance to spoken language stages following intervention. Development of spoken language was associated with modifiable factors at the child and intervention level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".