Quality of Early Intensive Behavioral Intervention as a Predictor of Children's Outcome
Bibliographic record
Abstract
of Early Intensive Behavioral Intervention (EIBI) in autism spectrum disorder (ASD) as a potential predictor of outcome. Therefore, using a preschool delivery model within a sample of 30 children, we examined the predictive power of EIBI quality on treatment outcome. EIBI quality was assessed at baseline by the York Measure of Quality of Intensive Behavioral Intervention (YMQI) and treatment outcome was evaluated after a period of 4 to 6 months using a battery of behavioral tests and scales to evaluate treatment success. Multinomial logistic regressions demonstrated that general EIBI quality predicted clinically significant change at follow-up. Particularly improvements in basic language and learning skills and global clinical impression were observed. Specific quality indicators that influenced overall treatment success were treatment organization, teaching level and differential reinforcement. In addition to previously examined predictors of EIBI treatment effects, such as child characteristics and intervention quantity, our findings highlight the importance of adequate EIBI quality assurance.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".