An Examination of the Impact of Attention-Deficit Hyperactivity Disorder on IQ: A Large Controlled Family-Based Analysis
Bibliographic record
Abstract
OBJECTIVE: Although children with attention-deficit hyperactivity disorder (ADHD) have, on average, lower intelligence quotient (IQ) scores than control subjects, the reasons for these deficits remain unknown. Because IQ is highly familial, we investigated whether children with ADHD have a decrement in IQ from expectations based on parental IQ. METHOD: Subjects were 276 children with ADHD and 239 control subjects of similar age and sex. Expected IQ was calculated based on biological parents' estimated IQ. A significant discrepancy between observed and expected estimated IQ was defined by a child scoring 15 IQ points or more lower than expected, based on parental IQ. RESULTS: Compared with control subjects, children with ADHD were significantly more likely to have lower than expected estimated IQ scores based on parental IQ, though this finding was accounted for by a small subgroup of children with ADHD who had an IQ 15 points or more lower than expected, based on parental IQ. These children were more likely to be female, have higher psychopathological, neuropsychological, educational, and interpersonal deficits, as well as higher rates of perinatal complications. CONCLUSIONS: Group differences in IQ scores between children with and without ADHD reported in the literature may be accounted for by a subgroup of children with ADHD who have a large decrement in IQ from expectations based on parental IQ. Although perinatal complications may explain these findings, more work is needed to better understand the etiology of these IQ deficits.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".