Predictors of outcome among high functioning children with autism and Asperger syndrome
Bibliographic record
Abstract
BACKGROUND: The objective of this paper is to assess the extent to which measures of cognitive abilities taken in an inception cohort of young high functioning children with autism and Asperger syndrome predict outcome roughly two and six years later. METHOD: Children who received a diagnosis of autism or Asperger syndrome (AS) and who had a nonverbal IQ score in the 'non-retarded' range were included in the inception cohort. Measures of language and nonverbal skills were taken when the children were 4-6 years of age and outcome assessments were completed when the children were 6-8 and 10-13 years of age. The three outcome measures consisted of scales of adaptive behaviours in socialisation and communication and a composite measure of autistic symptoms (abnormal language, abnormal body and object use, difficulties relating to others, sensory issues and social and self-help difficulties). RESULTS: The explanatory power of the predictor variables was greater for communication and social skills than for autistic symptoms. The power of prediction was stable over time but did differ by PDD subtype. In general, the association between language skills and outcome was stronger in the autism group than in the AS group. CONCLUSIONS: These results support the emphasis of early intervention programmes on language but more work needs to be done on understanding variables that influence outcome in social skills and autistic behaviours, particularly in those with AS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".