Similar developmental trajectories in autism and Asperger syndrome: from early childhood to adolescence
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to chart the developmental trajectories of high-functioning children with autism spectrum disorders (ASD) from early childhood to adolescence using the presence and absence of structural language impairment (StrLI) as a way of differentiating autism from Asperger syndrome (AS). METHOD: Sixty-four high-functioning children with ASD were ascertained at 4-6 years of age from several different regional diagnostic and treatment centers. At 6-8 years of age, the ADI-R and the Test of Oral Language Development were used to define an autism group (those with StrLI at 6-8 years of age) and an AS group (those without StrLI). Growth curve analysis was then used to chart the developmental trajectories of these children on measures of autistic symptoms, and adaptive skills in communication, daily living and socialization. RESULTS: Differentiating the ASD group in terms of the presence/absence of StrLI provided a better explanation of the variation in growth curves than not differentiating high-functioning ASD children. The two groups had similar developmental trajectories but the group without StrLI (the AS group) was functioning better and had fewer autistic symptoms than the group with StrLI (the autism group) on all measures across time. The differences in outcome could not be explained by non-verbal IQ or change in early language skills. CONCLUSION: Distinguishing between autism and Asperger syndrome based on the presence or absence of StrLI appears to be a clinically useful way of classifying ASD sub-types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".