Attention Deficit Hyperactivity Disorder in Children With Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cerebral palsy (CP) is the most prevalent physical disability in children and is often accompanied by other neurodevelopmental disorders (NDDs) such as attention deficit hyperactivity disorder (ADHD). Both conditions are influenced by genetic and environmental factors and significantly affect daily functioning. This study aims to estimate the prevalence of ADHD in school-aged children with CP from a large, population-based registry and explore associated factors including sex, material and social deprivation, epilepsy, prematurity, CP subtype, and motor functioning. METHODS: This cross-sectional study linked a population-based registry (the Registre de la paralysie cérébrale du Québec [CP Registry]) and 2 administrative health claims databases (the Régie de l'assurance maladie du Québec [RAMQ] and Maintenance et Exploitation des Données pour l'Étude de la Clientèle Hospitalière). The study included children diagnosed with CP born between 1999 and 2002, tracked through these databases. ADHD diagnosis was identified using International Classification of Diseases codes and specific ADHD medication prescriptions. Odds ratios and 95% confidence intervals were used to explore factors associated with an ADHD diagnosis. RESULTS: The study comprised 302 children with CP and 6,040 controls matched by age, sex, and region. The prevalence of ADHD in the CP cohort was significantly higher (38%) compared with the control group (12%). Univariate analysis showed that odds of ADHD in the CP cohort were higher in male children (OR 1.63, 95% CI 1.02-2.62) and individuals with no epilepsy diagnosis (OR 1.70, 95% CI 1.02-2.87), a spastic hemiplegic CP subtype (OR 1.87, 95% CI 1.10-3.20), and less severe motor impairment (OR 2.48, 95% CI 1.37-4.65). In the multivariate analysis, odds of ADHD were only higher in those with less severe motor impairment (OR 2.02, 95% CI 1.07-3.94). DISCUSSION: ADHD is significantly more prevalent among children with CP compared with their peers, aligning with previous literature that suggests a neurodevelopmental overlap. The study highlights the importance of considering NDDs in CP management, particularly ADHD, which may contribute to the challenges faced by these children. Future research is needed to explore the neurobiological links between CP and ADHD and the impact of NDDs on health outcomes in this 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".