IDEA (Intellectual Disability Exploring Answers): A population-based database for intellectual disability in Western Australia
Bibliographic record
Abstract
Despite the demands it places on individuals, families and the community, intellectual disability (ID) is a neglected area of public health. Accurate estimates of prevalence are sparse and range from 0.5 to 3.0%. The cause of the condition is unknown in at least 50% of cases. This paper describes the Intellectual Disability Exploring Answers (IDEA) database set up in Western Australia to provide an infrastructure for research and to facilitate the planning of service provision for people with ID. Since 1953 a database for ID has been maintained in Western Australia, a state with a population of 1.95 million in an area of 2.52 million km2. The current IDEA database aims to obtain ongoing population-based ascertainment of ID from providers of clinical and educational services, with the potential for linkage to a network of other state databases. The average prevalence of ID for children born in Western Australia over the years 1983-1996 was 15.2 per 1000 live births, with 50% ascertained only through the education system. During this time period 60% of cases were male. Of children with an ID born in Western Australia in 1980-1999 and surviving to 1 year, 30.1% had a birth defect, and the prevalence ratio of birth defects in this group compared to the population with no birth defects was 6.5 (CI 6.3-6.8).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".