Prevalence of Autism among Adolescents with Intellectual Disabilities
Bibliographic record
Abstract
Objective: To estimate the prevalence of autism in an epidemiologically-derived population of adolescents with intellectual disabilities (ID). Method: The prevalence of autism was examined using the Autism Diagnostic Interview—Revised, with appropriate care taken in assessing lower functioning individuals and those with additional physical and sensory impairments. Individual assessment during psychological evaluation, and consensus classification of complex cases, involving clinicians experienced in the assessment of autism, contributed to the identification of autism. Results: Overall, 28% of individuals, or 2.0 of the 7.1/1000 with ID in the target population (as we have previously identified in another study), were identified with autism. Autism rates did not differ significantly across severe ID (32.0%) and mild ID (24.1%); males predominated (2.3 males to 1 female), but less so for severe ID (2 males to 1 female, compared with 2.8 males to 1 female for mild ID). Socioeconomic status did not distinguish the groups with and without autism. Less than one-half of the adolescents who met diagnostic criteria for autism were previously diagnosed as such. Conclusions: Our overall prevalence estimate for autism is in the higher range of estimates reported in previous studies of ID (more so for mild ID). This likely reflects the changes in diagnostic criteria for autism that have subsequently occurred. Discussion focuses on the identification of autism in the population with ID, and on the implications for service delivery and clinical training. Objectif: Estimer la prévalence de l'autisme dans une population épidémiologique d'adolescents souffrant de déficiences intellectuelles (DI). Méthode: La prévalence de l'autisme a été examinée à l'aide de l'entrevue diagnostique de l'autisme révisée (ADI-R), en prenant soin d'évaluer les personnes au fonctionnement moindre et celles ayant des incapacités physiques et sensorielles additionnelles. L'évaluation individuelle durant l'évaluation psychologique, et la classification par consensus des cas complexes, faisant appel à des cliniciens ayant l'expérience de l'évaluation de l'autisme, ont contribué à l'identification de l'autisme. Résultats: En tout, 28 % des personnes ou 2,0 des 7,1/1000 souffrant de DI dans la population cible (comme nous l'avons précédemment identifiée dans une autre étude) ont été identifiées souffrir d'autisme. Les taux d'autisme ne différaient pas significativement entre les DI graves (32,0 %) et les DI bénignes (24,1 %); les hommes prédominaient (2,3 hommes pour 1 femme), mais moins pour les DI graves (2 hommes pour 1 femme, comparé à 2,8 hommes pour 1 femme pour les DI bénignes). Le statut socioéconomique ne distinguait pas les groupes avec et sans autisme. Moins de la moitié des adolescents qui satisfaisaient aux critères diagnostiques de l'autisme avaient précédemment reçu ce diagnostic. Conclusions: Notre estimation globale de la prévalence de l'autisme est dans la portion supérieure des estimations déclarées dans des études précédentes des DI (encore plus pour les DI bénignes). Ceci reflète probablement les changements des critères diagnostiques de l'autisme qui sont survenus subséquemment. La discussion met l'accent sur l'identification de l'autisme dans une population souffrant de DI, et sur les implications pour la prestation de services et la formation clinique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".