Correlates of age at diagnosis of autism spectrum disorders in six Canadian regions
Bibliographic record
Abstract
INTRODUCTION: Early identification of autism spectrum disorders (ASD) is important, since earlier exposure to behavioural intervention programs may result in better outcomes for the child. Moreover, it allows families timely access to other treatments and supports. METHODS: Using generalized linear modeling, we examined the association between child and family characteristics and the age at which 2180 children were diagnosed with ASD between 1997 and 2005 in six Canadian regions. RESULTS: A diagnosis of pervasive developmental disorder-not otherwise specified (PDD-NOS) or Asperger syndrome, rural residence, diagnosis in more recent years, and foreign birthplace were associated with a later age at diagnosis. Children who are visible minorities or who have siblings with ASD were more likely to be diagnosed earlier. Collectively, these factors explained little of the variation in age at diagnosis, however. CONCLUSION: While it is encouraging that ethnocultural identity, neighbourhood income, urban or rural residence, and sex of the child were not major contributors to disparities in the age when children were identified with ASD, more work is needed to determine what does account for the differences observed. Regional variations in the impact of several factors suggest that aggregating data may not be an optimal strategy if the findings are meant to inform policy and clinical practice at the local level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".