Phenotypic and genetic associations between reading and attention-deficit/hyperactivity disorder dimensions in adolescence
Bibliographic record
Abstract
Multiple studies have shown that reading abilities and attention-deficit/hyperactivity disorder symptoms, mainly inattention symptoms, are phenotypically and genetically associated during childhood. However, few studies have looked at these associations during adolescence to investigate possible developmental changes. The aim of the study is to examine the genetic and environmental etiology of the associations between inattention and hyperactivity reported by parents, and reading accuracy, reading speed, and word reading in a population-based twin sample (Quebec Newborn Twin Study). Participants were between 14 and 15 years of age at the time of testing (N = 668-837). Phenotypic results showed that when nonverbal and verbal abilities were controlled, inattention, but not hyperactivity/impulsivity, was a modest and significant predictor of reading accuracy, reading speed, and word reading. The associations between inattention and all reading abilities were partly explained by genetic and unique environmental factors. However, the genetic correlations were no longer significant after controlling for verbal abilities. In midadolescence, inattention is the attention-deficit/hyperactivity disorder dimension associated with reading abilities, but they could also share genetic factors with general verbal skills.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".