Verbal Memory and IQ Predict Adaptive Behavior in Children and Adolescents with High-Functioning Autism Spectrum Disorders
Bibliographic record
Abstract
Adaptive deficits are commonly found in high functioning autism spectrum disorders (HF-ASD) despite of cognitive potential. Most studies have focused on the relationships between adaptive behavior and intellectual quotient (IQ) and have used correlations to study relationships between them. Few studies have analyzed cognitive variables other than IQ as potential predictors of adaptive behavior in HF-ASD using regression methods. This study aimed to analyze the impact of several cognitive variables on adaptive behavior in a sample of children and adolescents with HF-ASD. METHODS: Sample included 16 child and adolescent boys with HF-ASD (age between 7-17 years). Cognitive assessment included measures of general intelligence, visual memory, verbal memory, working memory and problem solving/flexibility tests. Vineland Adaptive Behavior Scales (VABS) was used to evaluate adaptive behavior. To establish the predictive capacity of the cognitive variables for adaptive functioning, linear regression models were fitted for each adaptive domain using a stepwise method. RESULTS: Verbal memory and IQ emerged as the main independent predictors for VABS adaptive scores. The 41% of the variance in Communication was predicted by IQ. The 35% of the variance in Daily Living Skills was predicted by verbal memory. Almost half of the variance in Socialization was predicted by both, verbal memory and IQ (49%). No other cognitive functions were associated with adaptive scores. CONCLUSIONS: The results highlight the strong impact of IQ and verbal memory on adaptive behavior in HF-ASD patients. These findings could contribute to identify potential targets of intervention.
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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 | 0.003 |
| 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 | 0.000 |
| 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".