*Co-Occurrence and Causality Among ADHD, Dyslexia, and Dyscalculia
Bibliographic record
Abstract
ADHD, dyslexia, and dyscalculia often co-occur, and the underlying continuous traits are correlated (ADHD symptoms, reading, spelling, and math skills). This may be explained by trait-to-trait causal effects, shared genetic and environmental factors, or both. We studied a sample of ≤ 19,125 twin children and 2,150 siblings from the Netherlands Twin Register, assessed at ages 7 and 10. Children with a condition, compared to those without that condition, were 2.1 to 3.1 times more likely to have a second condition. Still, most children (77.3%) with ADHD, dyslexia, or dyscalculia had just one condition. Cross-lagged modeling suggested that reading causally influences spelling (β = 0.44). For all other trait combinations, cross-lagged modeling suggested that the trait correlations are attributable to genetic influences common to all traits, rather than causal influences. Thus, ADHD, dyslexia, and dyscalculia seem to co-occur because of correlated genetic risks, rather than causality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".