Mathematical Learning Disorder in School-Age Children with Attention-Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVES: To explore the prevalence of mathematics disorder (MD) relative to reading disorders (RD) in school-age children with attention-deficit hyperactivity disorder (ADHD) and examine the effects of age, sex, cooccurring conduct disorder (CD), and ADHD subtype on this comorbidity. METHODS: Participants were school-age children (n = 476) with confirmed DSM-IV diagnosis of ADHD. The assessment included semistructured parent and teacher interviews and standardized measures of intelligence, academic attainment, and language abilities. Based on the presence or absence of concurrent learning disorders, we compared the emerging 4 groups: ADHD-only, ADHD + MD, ADHD + RD, and ADHD + MD + RD. RESULTS: Overall prevalence of comorbid ADHD + MD was 18.1%. Age, sex, ADHD subtypes, or comorbid CD did not affect the frequency of MD. Children with concurrent ADHD and either MD or RD attained lower IQ, language, and academic scores than those with ADHD alone. Children with ADHD + MD + RD were more seriously impaired and demonstrated distinct deficits in receptive and expressive language. CONCLUSION: MDs are relatively common in school-age children with ADHD and are frequently associated with RDs. Children with ADHD + MD + RD are more severely impaired. These deficits simply cannot be explained as consequences of ADHD and might have unique biological underpinnings, with implications for diagnostic classification and therapeutic interventions.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".