Epidemiology of autistic disorder and other pervasive developmental disorders.
Bibliographic record
Abstract
Is the incidence of autistic disorder and other pervasive developmental disorders (PDDs) increasing? Recent epidemiological surveys of autistic disorder and other PDDs have heightened awareness of and concern about the prevalence of these disorders; however, differences in survey methodology, particularly changes in case definition and case identification over time, have made comparisons between surveys difficult to perform and interpret. Recent surveys suggest that the rate of all PDDs is about 60 per 10,000. The prevalence of autism today is estimated at 13 per 10,000, Asperger's disorder is approximately 3 per 10,000, and childhood disintegrative disorder is very rare at about 0.2 per 10,000. The assessment process, sample size, publication year, and geographic location of studies all have an effect on prevalence estimates. In addition, data from many of these surveys indicate correlates of autistic disorder and other PDDs with IQ, gender, and other medical disorders.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".