Neurodevelopmental Disabilities in Canadian Children: Prevalence Data from the National Longitudinal Study of Children and Youth
Bibliographic record
Abstract
Abstract Aim Using population surveys of chronic health conditions, the present study aimed to examine changing trends in the prevalence of neurodevelopmental disabilities (NDD) with age and determine population-based estimates of prevalence and census-based estimates of absolute numbers of affected children. Methods We analyzed data from three cycles (1994–1999) of Canada's National Longitudinal Survey of Children and Youth (NLSCY) (Statistics Canada Survey). Results Cross-sectional prevalence rates for chronic NDD in children from birth to 15 years across cycle 1 to 3 of the NLSCY show an increasing trend over the years from 1994 to 1999. Population-based estimates were also calculated from census data. Weighted prevalence rates for four conditions in children aged birth to 15 years increased across the three cycles, except for cerebral palsy. Prevalence estimates in cycle 3 were: epilepsy 5.26/1,000 (95% confidence interval [CI]: 5.01, 5.52), cerebral palsy 2.81/1,000 (95% CI: 2.62, 2.99), intellectual disability 4.77/1,000 (95% CI: 4.53, 5.02), and learning disability 57.06/1,000 (95% CI, 56.36, 57.76). A male gender preponderance was noted for each NDD using logistic regression. Interpretation Prevalence rates of NDD in Canadian children show an incremental trend across three cycles in four conditions covered in the survey. The changing trends over the three cycles are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".