IDEA (Intellectual Disability Exploring Answers): A population-based database for intellectual disability in Western Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it