Development of a Composite Primary Outcome Score for Children with Attention-Deficit/Hyperactivity Disorder and Emotional Dysregulation
Bibliographic record
Abstract
Objective: Study goals were to (1) provide a rationale for developing a composite primary outcome score that includes symptom severity for attention-deficit/hyperactivity disorder (ADHD) and emotional dysregulation, plus symptom-induced impairment; (2) demonstrate weighting methods to calculate the composite score using a sample of children diagnosed with ADHD and aggression; and (3) identify the optimal weighting method most sensitive to change, as measured by effect sizes. Methods: We conducted secondary data analyses from the previously conducted Treatment of Severe Childhood Aggression (TOSCA) study. Children aged 6–12 years were recruited through academic medical centers or community referrals. The composite primary outcome comprised the ADHD, oppositional defiant disorder, disruptive mood dysregulation disorder, and peer conflict subscales from the Child and Adolescent Symptom Inventory (CASI), a DSM ( Diagnostic and Statistical Manual )-referenced rating scale of symptom severity and symptom-induced impairment. Five weighting methods were tested based on input from senior statisticians. Results: The composite score demonstrated a larger (Cohen's d ) effect size than the individual CASI subscales, irrespective of the weighting method (10%–55% larger). Across all weighting methods, effect sizes were similar and substantial: approximately a two-standard deviation symptom reduction (range: −1.97 to −2.04), highest for equal item and equal subscale weighting, was demonstrated, from baseline to week 9, among all TOSCA participants. The composite score showed a medium positive correlation with the Clinical Global Impressions-Severity scores, 0.46–0.47 for all weighting methods. Conclusions: A composite score that included severity and impairment ratings of ADHD and emotional dysregulation demonstrated a more robust pre–post change than individual subscales. This composite may be a more useful indicator of clinically relevant improvement in heterogeneous samples with ADHD than single subscales, avoiding some of the statistical limitations associated with multiple comparisons. Among the five similar weighting methods, the two best appear to be the equal item and equal subscale weighting methods.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".