Efficacy and safety of methylphenidate on attention deficit hyperactivity disorder in children with Down syndrome
Bibliographic record
Abstract
BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common co-morbidity that affects up to 44% of children with Down syndrome (DS). There is a need for reliable, good quality research on the use of methylphenidate within this population. The objective of this study is to report our experience regarding the management of ADHD in these children using methylphenidate. METHODS: This study is a retrospective observation of 21 children with DS, followed at Jérôme Lejeune Institute between 2000 and 2018. The diagnosis of ADHD was made using the Diagnostic and Statistical Manual of Mental Disorders criteria. Efficacy was measured as response or non-response on two main symptoms: attention/concentration and hyperactivity/impulsivity. Safety was evaluated by the presence or absence of side effects. RESULTS: Sixteen out of the 21 children (76%) showed improvement with methylphenidate. The average age of treatment onset in responding children was 8 years and 10 months versus 6 years and 3 months in non-responders (P = 0.05). Average dose/weight was significantly different in responders and non-responders (0.82 vs. 0.54 mg/kg/day, respectively; P = 0.03). Twelve children out of 21 (57%) experienced side effects; only three experienced side effects severe enough to require treatment interruption. Most common side effects were loss of appetite and difficulties in falling asleep. CONCLUSION: Methylphenidate was effective and safe in treating ADHD in 76% of cases in children with DS, with few serious side effects to report. Early diagnosis of ADHD is important to improve the quality of life, learning, inclusion and socialisation of children with DS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".