Incidence and cohort prevalence for autism spectrum disorders in the Avalon Peninsula, Newfoundland and Labrador
Bibliographic record
Abstract
BACKGROUND: Recent studies have reported increased prevalence for autism spectrum disorders in a number of geographical locations. Our objective was to determine the incidence and 1-year cohort prevalence for autism spectrum disorders in children less than 15 years of age and living in the Avalon Peninsula at the time of diagnosis. METHODS: Retrospective and prospective data were obtained from the Janeway Children's Health and Rehabilitation Centre (St. John's), including the identification and specific diagnosis for all children assessed for autism spectrum disorder from 2006 to 2010. Additional clinic data were reviewed to update the data until the end of 2013. RESULTS: From 2006 to 2010, 272 children had a diagnosis of autism spectrum disorder, averaging 54 new cases per year. The incidence of new cases increased from 10.1 to 16.7 cases per 10 000 per year from 2006 to 2010. At the end of 2013, the prevalence among children born in 2006 was 1 case of autism spectrum disorder per 46 children or 215.77 per 10 000. INTERPRETATION: We found higher rates of autism spectrum disorder than previously reported for this population. The prevalence in this region is also high when compared with other global populations. The high rate of diagnosis supports the need for a provincial autism spectrum disorder registry and further research on autism spectrum disorder within this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".