Personalized Treatment of Mothers With ADHD and Their Young At-Risk Children: A SMART Pilot
Bibliographic record
Abstract
Young children of mothers with adult attention-deficit/hyperactivity disorder (ADHD) are at risk for ADHD by virtue of genetics and environmental factors. Moreover, parent ADHD is associated with maladaptive parenting and poor child behavioral treatment response. Thus, a combined approach consisting of behavioral parent training (BPT) and maternal stimulant medication (MSM) may be needed to effectively treat ADHD within families. However, providing combined BPT+MSM initially to all families may be unnecessarily burdensome because not all families likely need combined treatment. The purpose of this study is to examine how to combine, sequence, and personalize treatment for these multiplex families in order to yield benefits to both the parent and child, thereby impacting the course of child ADHD and disruptive behavior symptoms. This article presents our rationale for, design of, and preliminary experiences (based on 26 participants) with an ongoing pilot Sequential Multiple Assessment Randomized Trial (SMART) designed to answer questions regarding the feasibility and acceptability of study protocols and interventions. This article also describes how the subsequent full-scale SMART might change based on what is learned in the SMART pilot and illustrates how the full-scale SMART could be used to inform clinical decision making about how to combine, sequence, and personalize treatment for complex children and families in which a parent has 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".