Therapeutic effects of methylphenidate for attention-deficit/hyperactivity disorder in children with borderline intellectual functioning or intellectual disability: A systematic review and meta-analysis
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) frequently co-occurs with intellectual disability in children, and may further compromise learning. Methylphenidate is a first-line treatment for ADHD, however no previous meta-analysis has evaluated its overall efficacy for ADHD in children with comorbid intellectual disability (ID) or borderline intellectual functioning. The PubMed/MEDLINE, Cochrane CENTRAL and ScienceDirect databases were systematically searched from inception through 2018/7/15 for clinical studies that investigated the effects of methylphenidate in children with ADHD and ID. A random-effects model meta-analysis was used for data synthesis. Eight studies (average Jadad score = 2.5) enrolling 242 participants receiving methylphenidate and 181 participants receiving placebo were included. The meta-analysis showed that methylphenidate led to a significant improvement in ADHD symptoms relative to placebo (Hedges' g = 0.878, p < 0.001). Meta-regression analysis pointed to an association between the dose of methylphenidate and overall improvement in ADHD severity (slope = 1.334, p < 0.001). Finally, there was no significant difference in drop-out rate [odds ratio (OR) = 1.679, p = 0.260] or rate of treatment discontinuation due to adverse events (OR = 4.815, p = 0.053) between subjects receiving methylphenidate and those taking placebos. Our study suggests that methylphenidate retains its efficacy in children with ADHD and borderline intellectual functioning or ID.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".