Using the Preschool Language Scale, Fourth Edition to Characterize Language in Preschoolers With Autism Spectrum Disorders
Bibliographic record
Abstract
PURPOSE: The Preschool Language Scale, Fourth Edition (PLS-4; Zimmerman, Steiner, & Pond, 2002) was used to examine syntactic and semantic language skills in preschool children with autism spectrum disorders (ASD) to determine its suitability for use with this population. We expected that PLS-4 performance would be better in more intellectually able children and that receptive skills would be relatively more impaired than expressive abilities, consistent with previous findings in the area of vocabulary. METHOD: Our sample consisted of 294 newly diagnosed preschool children with ASD. Children were assessed via a battery of developmental measures, including the PLS-4. RESULTS: As expected, PLS-4 scores were higher in more intellectually able children with ASD, and overall, expressive communication was higher than auditory comprehension. However, this overall advantage was not stable across nonverbal developmental levels. Expressive skills were significantly better than receptive skills at the youngest developmental levels, whereas the converse applied in children with more advanced development. CONCLUSIONS: The PLS-4 can be used to obtain a general index of early syntax and semantic skill in young children with ASD. Longitudinal data will be necessary to determine how the developmental relationship between receptive and expressive language skills unfolds in children with 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".