Sleep-disordered breathing in children and adults with intellectual disability: mind the gap!
Bibliographic record
Abstract
BACKGROUND: In adults and children with intellectual disability (ID), sleep -disordered breathing (SDB) is thought to be common. However, large epidemiological studies are lacking, and there are few studies on optimal methods of investigation and even fewer randomised, controlled intervention trials of treatment. METHOD: Peer-reviewed publications from various databases were examined in line with search terms relevant to ID and SDB spanning the years 200-2024. RESULTS: Findings suggest that, due to comorbid conditions, children and adults with ID may experience both an increased risk of SDB, as well as lower frequency of diagnosis. SDB can compromise the emotional, physical and mental health of individuals with ID. Appropriate treatment when tolerated leads to an improvement in health and well-being and several studies emphasized the importance of consistent follow-up of people with ID - something that is not universally occurring during childhood, in the transition to adulthood and during adulthood itself. As the most frequently occurring form of ID worldwide, we use Down syndrome as a specific example of how diagnosing and treating SDB can lead to improved outcomes. CONCLUSIONS: This review highlights the importance of identifying SDB in this heterogenous population, recognising the multi-faceted, deleterious consequences of untreated SDB in people with ID, and presents some strategies that can be harnessed to improve diagnosis and management. Until further ID-specific research is available, we urge flexibility in the approach to people with ID and SDB based in guidelines and standard practice developed for the typically developing population.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".