Developing behavioural indicators for intellectual functioning and adaptive behaviour for <scp>ICD</scp>‐11 disorders of intellectual development
Bibliographic record
Abstract
BACKGROUND: We present the work conducted to arrive at deriving behavioural indicators that could be used to guide clinical judgement in determining the presence and severity of deficits in intellectual functioning and adaptive behaviour for the purpose of making a diagnosis of disorders of intellectual development. METHODS: An interdisciplinary expert panel provided guidance in developing behavioural indicators for intellectual functioning. A national dataset of adaptive behaviour on a sample of individuals with a diagnosis of intellectual disability was used to develop the behavioural indicators for the adaptive behaviour. The adaptive behaviour data were analysed using a cluster analysis procedure to define the different severity groupings by chronological age groups. RESULTS: We present a series of tables containing behavioural indicators across the lifespan for intellectual functioning and adaptive behaviour, including conceptual, social and practical skills. These tables of behavioural indicators have been proposed for use in the clinical version of the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11) to be published by the World Health Organization. CONCLUSIONS: The proposed behavioural indicators for disorders of ID described in the present article and to be included in the ICD-11 Clinical Descriptions and Diagnostic Guidelines are put forth to assist professionals in making an informed clinical decision regarding an individual's level of intellectual functioning and adaptive behaviour for the purpose of making a determination about the presence and severity of disorders of ID.
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.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".