Phenotyping sleep disturbances in ADHD and identifying harmonised outcome measures
Bibliographic record
Abstract
Abstract Attention deficit hyperactivity disorder (ADHD) is a widespread neurodevelopmental disorder. Currently, the diagnosis and treatment of ADHD in children and adolescents is primarily centred on daytime functioning and the associated impairment of academic performance, although disrupted and restless sleep have been frequently reported in individuals with ADHD. Further, it has been recognised that sleep disorders not only intensify existing ADHD symptoms but in some cases can also mimic ADHD symptoms in the paediatric population with primary sleep disorders. Under the title ‘The blind spot: sleep as a child’s right issue?’, professionals from diverse disciplines, including medicine and social sciences as well as individuals with an interest in ADHD and sleep medicine, including laypeople, have initiated a unifying discourse. The objective of this discourse is to improve our understanding of the diagnosis and treatment of ADHD and disruptive behaviours and to develop personalised and precision medicine. Research has shown that the existing, primarily descriptive and categorical diagnostic systems do not capture the heterogeneous nature of youth with attentional and behavioural difficulties and the phenotypic expressions thereof, including nighttime behaviours and sleep. New strategies for clinical phenotyping and the exploration of patient-reported behaviours are necessary to expand our understanding and develop personalised treatment approaches. In this position paper, we outline gaps in the clinical care of ADHD and related sleep disturbances, review strategies for closing these gaps to meet the needs of individuals with ADHD, and suggest a roadmap for escaping the one-size-fits-all approach that has characterised ADHD treatment algorithms to date.
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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 | 0.046 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".