Effects of Methylphenidate on Acute Math Performance in Children with Attention-Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: Examine the short-term (acute) effects of methylphenidate (MPH) on math performance in children with attention-deficit hyperactivity disorder (ADHD) and what factors predict improvement in math performance. METHOD: One hundred ninety-eight children with ADHD participated in a double-blind, placebo-controlled, randomized crossover MPH trial. Math response to MPH was determined through administration of math problems adjusted to their academic level during the Restricted Academic Situation Scale (RASS). Student t tests were conducted to assess change in math performance with psychostimulants. Correlation between change on the RASS and change on the math performance was also examined. Linear regression was performed to determine predictor variables. RESULTS: Children with ADHD improved significantly in their math with MPH (P < 0.001). The degree of improvement on the RASS (which evaluates motor activity and orientation to task) and on math performance on MPH was highly correlated. A child's age at baseline and Wechsler Individual Achievement Test (WIAT)-Numerical Operations standard scores at baseline accounted for 15% of variances for acute math improvement. CONCLUSIONS: MPH improves acute math performance in children with ADHD. Younger children with lower math scores (as assessed by the WIAT) improved most on math scores when given psychostimulants. CLINICAL TRIAL REGISTRATION NUMBER: NCT00483106.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".