The Assessment of Psychiatric Disorders in Intellectual Disability: The State of the Art
Bibliographic record
Abstract
Intellectual Disability (ID) represents a meta-syndromic group of several different conditions, each one with specific cognitive and communication features. This constitutes one the main reason for which the diagnosis of psychiatric disorders in adults with ID is challenging. The aim of this paper is to provide a comprehensive and updated state of art on the assessment of psychiatric disorders in people with ID. In order to accomplish this goal, authors performed a systematic literature mapping trying to answer to the following questions: what are the issues in the psychiatric diagnostic process for adults with ID? What methods and procedures have been used for psychiatric assessment in ID? To date, is it possible to identify the most effective diagnostic procedures? Results indicated that the main critical issues in the psychiatric diagnostic process were the following: the identification of psychiatric symptoms, the behavioural equivalents, the diagnostic criteria, the setting, the source of information, the screening and diagnostic instruments. An increasing interest in screening instruments for providing appropriate diagnosis for people with ID has been noted in the recent years. For this reason, available assessment tools have been briefly indicated, with their strengths and weaknesses. Although the recent advances, more research is needed in order to increase diagnostic accuracy in this heterogeneous group of patients.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".