Pivotal response treatment for preschoolers with autism spectrum disorder: Defining a predictor profile
Bibliographic record
Abstract
Behavioral characteristics of children with autism spectrum disorder (ASD) who respond positively to Pivotal Response Treatment (PRT) have been described previously, based on single-subject design research. The present study examined several such characteristics, as well as positive affect, as predictors of expressive language (EL) gains in a representative sample of preschoolers with ASD (n = 57) enrolled in a PRT-based community early intervention program. Children's cognitive ability, positive affect, and levels of appropriate toy contact measured at the start of intervention each contributed significantly to the prediction of EL outcomes. Together these variables accounted for 40% of the total outcome variance. In addition, a profile of increased EL ability, positive affect and appropriate toy contact, and decreased social avoidance and stereotyped and repetitive vocalizations was associated with greater gains during intervention. Results are discussed in relation to their implications for understanding both the variable treatment response documented in children with ASD and how to tailor treatment to optimize individual benefit. Autism Res 2018, 11: 153-165. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The study examined behavior of 57 preschoolers who made the greatest and least gains from 1 year of a community Pivotal Response Treatment program. Using pre-treatment videos, we saw that children who made the most progress showed more language, positive affect, and appropriate interactions with toys, also less avoidance of people and fewer repetitive vocalizations. Behavior profiles can be used to match treatment to individual children's needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".