Methylphenidate has Differential Effects on Numerical Abilities in ADHD Children with and without Co-Morbid Mathematical Difficulties
Bibliographic record
Abstract
Objective. To investigate effects of methylphenidate (MPH) on numerical performance in children with Attention- Deficit/Hyperactivity Disorder (ADHD) with and without concurrent math difficulties. Method. Data were analyzed from three groups of children with ADHD, who varied in arithmetic abilities. Groups were matched for IQ and reading abilities and classification was based on ICD-10 criteria, using scores on a standardized arithmetic achievement test. Thus, we identified one group with severe difficulties in arithmetic (ADHD+Developmental Dyscalculia; DD), second group with more general and less severe difficulties in arithmetic (ADHD+Mathematical Disabilities; MD), and a third group with good arithmetic abilities (ADHD). All children completed a 10-minute arithmetic task involving subtraction problems, during an acute, randomized, placebo-controlled cross-over trial with three dose levels of methylphenidate (10mg, 15mg, 20mg). Results. ( 1 ) Both ADHD+MD and ADHD+DD were impaired in using strategies that implicate working memory (i.e., borrowing). However, only the ADHD+DD were impaired in using implicit knowledge of quantities (i.e., doing simple subtractions). ( 2 ) MPH improved all children’s performance of arithmetic procedures (borrowing) that involves working memory, but had no effect on basic numerical skills that involves understanding of quantities. Conclusions. We show clear dissociation of MPH functions: it improves working memory functions but does not improve specific cognitive functions such as quantity manipulation. Moreover, MPH shows decreased efficacy for arithmetic performance in ADHD+DD, highlighting the need for additional intervention in this subgroup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".