Pervasive Developmental Disorders in Preschool Children: Confirmation of High Prevalence
Bibliographic record
Abstract
OBJECTIVE: The rate of reported pervasive developmental disorders has increased, and the authors found a rate of 62.6 per 10,000 in a previous study of preschoolers in Stafford, U.K. They conducted another survey in 2002 to estimate the prevalence in children in a later birth cohort and to compare it to previous findings from the same area. METHOD: Screening for developmental problems included 10,903 children ages 4.0 to 6.0 years who were living in a Midlands town on the survey date. Children with symptoms suggestive of pervasive developmental disorders were intensively assessed by a multidisciplinary team using standardized diagnostic interviews, psychometric tests, and medical workups. RESULTS: Sixty-four children (85.9% boys) were diagnosed with pervasive developmental disorders. The prevalence was 58.7 per 10,000, with a 95% confidence interval (CI) of 45.2-74.9, for all pervasive developmental disorders, 22.0 per 10,000 (95% CI=14.1-32.7) for autistic disorder, and 36.7 per 10,000 (95% CI=26.2-49.9) for other variants. These rates were not significantly different from the previous rates. The mean age at diagnosis was 37.8 months, and 53.1% of the children were originally referred by health visitors. Of the 64 children with pervasive developmental disorders, 29.8% had mental retardation, but this rate varied by disorder subtype. Few children had associated medical conditions. CONCLUSIONS: The rate of pervasive developmental disorders is higher than reported 15 years ago. The rate in this study is comparable to that in previous birth cohorts from the same area and surveyed with the same methods, suggesting a stable incidence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".