Early joint attention abilities measured by the ADOS-2 predict subsequent expressive language development in minimally verbal autistic children
Bibliographic record
Abstract
Abstract Background Most preschool autistic children exhibit substantial language delays, yet only ∼25% remain minimally verbal (MV) throughout life. Developing expressive language is crucial for improving outcomes. This study examined early predictors of later expressive language growth in MV preschool autistic children. Methods Data from 99 MV autistic children (mean age 27.7 months at diagnosis) who completed ADOS‐2 assessments at diagnosis and 12–24 months later were analyzed. Children were stratified into three groups according to their language abilities at follow‐up (MV, one‐word, and phrases). Logistic regression was used to determine whether baseline ADOS‐2 calibrated severity scores (CSS), non‐verbal cognitive abilities, or Joint Attention (JA), derived from six ADOS‐2 items, enabled prediction of expressive language abilities at follow‐up. Results Children who successfully developed expressive language (one‐word/phrases) had significantly lower baseline ADOS‐2 social affect CSS and JA scores, but did not differ in their non‐verbal cognitive abilities or ADOS‐2 restricted and repetitive behaviors CSS. Gains in JA were significantly larger in children who developed expressive language. Conclusion MV preschoolers with better social abilities at diagnosis, specifically JA, were more likely to develop expressive language within 1–2 years. Joint attention scores derived from the ADOS‐2 offer an easily accessible and widely available measure with important prognostic value for MV children.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".