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Similar developmental trajectories in autism and Asperger syndrome: from early childhood to adolescence

2009· article· en· W1995661277 on OpenAlexafffund
Péter Szatmári, Susan E. Bryson, Eric Duku, Liezanne Vaccarella, Lonnie Zwaigenbaum, Teresa Bennett, Michael H. Boyle

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2009
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of AlbertaIzaak Walton Killam Health CentreDalhousie UniversityMcMaster University
FundersCanadian Institutes of Health Research
KeywordsAutismPsychologyDevelopmental psychologyHigh-functioning autismDevelopmental disorderAsperger syndromeSocializationPervasive developmental disorderIntelligence quotientClinical psychologyAutism spectrum disorderPsychiatryCognition

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.283
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations137
Published2009
Admission routes2
Has abstractyes

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