Categorical and dimensional approaches to the developmental relationship between <scp>ADHD</scp> and irritability
Bibliographic record
Abstract
BACKGROUND: Attention deficit hyperactivity disorder (ADHD) and irritability commonly co-occur, and follow similar developmental trajectories from childhood to adolescence. Understanding of the developmental relationship between these co-occurrences is limited. This study provides a longitudinal assessment of how ADHD diagnostic status and symptom patterns predict change in irritability. METHODS: A community sample of 337 participants (45.2% ADHD), recruited for the Childhood Attention Project, completed the Affective Reactivity Index (ARI) to measure irritability at baseline (mean age 10.5 years) and follow-up after 18-months. Latent change score models were used to assess how (a) baseline ADHD vs. control group status, (b) baseline symptom domain (inattention, hyperactivity-impulsivity) and (c) longitudinal change in ADHD symptom severity predicted change in irritability. RESULTS: Irritability was significantly higher among the ADHD group than controls; however, change in irritability over time did not differ between groups. When assessed across the entire cohort, change in irritability was predicted by higher symptom count in the hyperactive-impulsive domain, but not the inattentive domain. Greater declines in ADHD symptoms over time significantly predicted greater declines in irritability. Baseline ADHD symptom severity was found to significantly predict change in irritability; however, baseline irritability did not significantly predict change in ADHD symptoms. CONCLUSIONS: ADHD symptoms-particularly hyperactive-impulsive symptoms-predict the degree and trajectory of irritability during childhood and adolescence, even when symptoms are below diagnostic thresholds. The use of longitudinal, dimensional and symptom domain-specific measures provides additional insight into this relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".