Medication Effects on Symptoms of Attention-Deficit/Hyperactivity Disorder in Children with Fetal Alcohol Spectrum Disorder
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) may be the most common mental health disorder in children with fetal alcohol spectrum disorders (FASD). Despite this, little information is available regarding the effectiveness of ADHD treatment in this population. This study, conducted within a clinical service, aimed to assess the impact of medication on symptoms of ADHD in children with FASD by determining (a) the extent of change in ADHD symptoms with medication, and (b) whether differences in improvement are seen between symptom domains. Data were extracted from the medical records of 27 children with FASD who had been referred to an ADHD medication service at the Alberta Children's Hospital in Canada. Participants were primarily male and ranged in age from 5 years 6 months to 14 years 5 months. Teacher MTA-SNAP-IV scores were the primary outcome measure. Baseline, best, and change scores across three symptom domains (inattention, hyperactivity/impulsivity, and opposition/defiance) were determined. A total of 41 medication trials was conducted. More children obtained normalized best scores for hyperactivity/impulsivity (n = 18) and opposition/defiance (n = 19) than for inattention (n = 9) across medication trials. These findings suggest that inattention may be less responsive to ADHD medication. Replication in larger samples with a placebo-controlled design is required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".