Prevalence of comorbidities in children with attention deficit and hyperactivity disorder at Lady Ridgeway Hospital for Children, Sri Lanka
Bibliographic record
Abstract
Background: Attention deficit and hyperactivity disorder (ADHD) has a high prevalence and is frequently associated with comorbid illnesses.Objective: To assess the comorbid patterns, sociodemographic profiles, management patterns and outcomes amongst children with ADHD presenting to the Child and Adolescent Mental Health Services at the Lady Ridgeway Hospital for Children.Method: Two hundred patients, aged 6-12 years, diagnosed with ADHD, were assessed for their demographic features and comorbidities based on DSM IV criteria.Results: Of the 200 children, 166 (83%) were male and 198 (99%) were schooling. While 108 (54%) had a single neuropsychiatric comorbidity, 30 (15%) had 2 comorbidities and 1 (0.5%) had 3 comorbidities. Specific developmental disorder of scholastic skills (SDDSS) was the most prevalent comorbidity and was seen in 90 (45%) patients. Oppositional defiant disorder (ODD) was seen in 56 (28%) children and 17 (8.5%) had both SDDSS and ODD. In the sample, 90% of children were born of uneventful deliveries. Postnatal complications were found in 12% children. In the sample, 30% had a history of febrile fits and 2.5% had a history of afebrile fits.Conclusions: In this sample of 200 children with diagnosed ADHD, 139 (69.5%) had one or more neuropsychiatric comorbidities. SDDSS was the most prevalent comorbiditySri Lanka Journal of Child Health, 2015: 44(2): 77-81
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".