Age at Diagnosis of Autism Spectrum Disorders in Four Regions of Canada
Bibliographic record
Abstract
Objectives: Early diagnosis of autism spectrum disorders (“autism”) may lead to better treatment outcomes, reduces the stress parents experience when they do not understand the reasons for their child’s behaviour, and empowers parents to make choices such as seeking genetic counseling. We examined the age at which Canadian children are diagnosed with autism, and analyzed whether there are geographic or temporal variations or differences by sex or diagnostic subtype. Methods: As part of an autism surveillance program, in 2002/2003 we began collecting information on children with autism in Manitoba, Southeastern Ontario, Prince Edward Island, and Newfoundland and Labrador. For the analysis presented in this paper, we included children identified for our surveillance program who were diagnosed between 1997 and 2005 (n=769). Results: We found significant inter-regional differences in age at diagnosis, with Newfoundland and Labrador having the lowest median age at diagnosis (39.0 months) and Southeastern Ontario the highest (55.0 months). Diagnostic subtype was significantly associated with age at diagnosis in all regions. Southeastern Ontario was the only region where the overall age at diagnosis increased over time (p=0.004), although in Manitoba the age at which children were diagnosed with PDD-NOS also increased significantly over the study period (p=0.021). Conclusions: Our findings demonstrate that there are geographic differences and other sources of variation in the age at which Canadian children are diagnosed with autism. Further study is warranted to understand the factors contributing to these differences. Such research would inform best practices for early detection and timely access to treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".