Association of Psychopharmacological Medication Preference with Autistic Traits and Emotion Regulation in ADHD
Bibliographic record
Abstract
Background: This study intends to evaluate the relationship between medication switching and autistic traits, emotion dysregulation, and methylphenidate side effects in children with attention deficit hyperactivity disorder (ADHD). Methods: Children with ADHD, ages 9-18, treated with methylphenidate (MTP) (n = 23), and switched to atomoxetine (ATX) (n = 20) were included. All participants were interviewed with K-SADS-PL to confirm ADHD diagnosis and exclude comorbid psychiatric disorders. The participants then completed Difficulty in Emotion Regulation Scale (DERS) and Autism-Spectrum Quotient (AQ) and their parents completed Autism Spectrum Screening Questionnaire (ASSQ) and Barkley Stimulant Side Effect Rating Scale(BSSERS). Results: The MTP group scored higher than the ATX group in ASSQ, AQ, and the lack of emotional clarity subscale of DERS, while the ATX group had higher scores in the emotional non-acceptance subscale of DERS. No differences were found between the MTP and ATX groups in methylphenidate side-effect severity. Multiple regression analyses revealed that non-acceptance of emotions predicted the switch to ATX while lack of emotional clarity predicted the maintenance of MTP therapy, rather than autistic traits. Conclusions: This study highlights emotion regulation difficulties and how different emotional profiles may influence medication selection in children with ADHD.
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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 | 0.001 | 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".