Reading Outcomes of Children and Adolescents with Attention-Deficit/Hyperactivity Disorder and Dyslexia Following Atomoxetine Treatment
Bibliographic record
Abstract
UNLABELLED: Abstract Objective: This study assessed the efficacy of atomoxetine on attention-deficit/hyperactivity disorder (ADHD) symptoms in children and adolescents having ADHD with comorbid dyslexia (ADHD+D) and the effects of the treatment on reading measures. METHODS: The analyses in this report used data from a study designed to examine the effects of a nonstimulant pharmacological agent, atomoxetine, on reading in children with ADHD+D. Patients ages 10-16 years with ADHD or ADHD+D received open-label atomoxetine for 16 weeks. The ADHD Rating Scale (ADHD-RS) and reading subtests of the Kaufman Test of Educational Achievement (K-TEA) were assessed. Changes in ADHD symptoms and reading scores were also analyzed by ADHD subtype. Treatment effect sizes and correlations between changes in ADHDRS and K-TEA scores were calculated. RESULTS: After atomoxetine treatment, both ADHD and ADHD+D patient groups showed significant reduction in ADHD symptom and improvements in K-TEA reading scores. The range of treatment effect sizes on K-TEA scores was 0.35-0.53 for the ADHD group and 0.50-0.62 for the ADHD+D group. Pearson's correlation coefficients revealed only a few weak correlations between changes in ADHD symptoms and reading scores, regardless of diagnostic group. CONCLUSIONS: ADHD symptoms and K-TEA reading scores improved for both the ADHD and ADHD+D groups following atomoxetine treatment. Correlation analyses indicate that improvements in reading outcomes cannot be explained by a reduction of ADHD symptoms alone. These findings support further exploration of the potential effects of atomoxetine on reading in children with ADHD and dyslexia or dyslexia alone.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".