Methylphenidate Selectively Improves Story Retelling in Children with Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVES: The goal of this study was to determine stimulant effects on story grammar, comprehension, and errors in the narratives of children with attention deficit hyperactivity disorder (ADHD) with and without comorbid language impairment. Previous research has demonstrated impairments in the narrative abilities of children with ADHD, but the effect of the primary treatment modality (methylphenidate) is unknown. METHODS: Fifty children with ADHD (7 to 12 years of age) were stratified for language impairment that might influence performance. In an acute, randomized, placebo-controlled crossover trial with two single doses (10 and 20 mg) of methylphenidate, the children listened to an audiotaped story while viewing a wordless picture book of the study, then retold the story and answered comprehension questions. The narratives were transcribed and coded for story grammar, length, and errors. RESULTS: Methylphenidate only increased children's reporting of the story characters' internal responses and attempts. It had no effect on story length or responses to comprehension questions. Responses to the factual questions were significantly more accurate than the inferential questions, irrespective of medication dose. Comorbid language impairment had no effect on performance or stimulant response. CONCLUSION: Story grammar analysis was sensitive to drug effects, which were subtle but specific and clinically meaningful. Results provide evidence for the theoretical linkage between internal responses and attempts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".