Sleep Variables as Predictors of Treatment Effectiveness and Side Effects of Stimulant Medication in Newly Diagnosed Children with Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: There is a growing body of research on the impact of stimulant medication on sleep in children with attention-deficit/hyperactivity disorder (ADHD). Negative sleep side effects are a common reason for nonadherence or for discontinuing a course of treatment. However, there is no published evidence as to whether pretreatment sleep can predict responses to treatment and the emergence of side effects. METHOD: In this study, baseline sleep variables were used to predict therapeutic effect (i.e., reduction of ADHD symptoms) and side effects (both sleep and global side effects) in a sample of newly diagnosed, medication-naive children (n = 50). RESULTS: The results of hierarchical regression analysis showed that parent-reported shorter sleep duration before medication treatment significantly predicted better response to treatment, independent of pretreatment ADHD symptoms. Baseline sleep features did not significantly predict global (nonsleep) side effects but did predict increased sleep side effects during treatment. CONCLUSION: These results indicate that baseline sleep variables may be helpful in predicting therapeutic response to medication and sleep disturbance as a side effect of stimulant medication.
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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 | 0.003 |
| 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 | 0.001 |
| 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".