Callous-Unemotional Traits and Face-Emotion Recognition as Mediators in Conduct Problems of Children With ADHD
Bibliographic record
Abstract
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is associated with increased risk for conduct problems (CP), as well as with callous-unemotional traits (CUt) and lower accuracy in face emotional recognition (FER). It is unclear, however, whether CUt and low accuracy in FER contribute to the risk for CP in ADHD. The present study investigated the possibility of such contribution. METHODS: This pilot study's participants included 31 children aged 7-17 years, diagnosed with ADHD, and treated in a psychiatric outpatient clinic. The parents rated their children on the ADHD Rating Scale, Inventory of Callous-Unemotional Traits, and the Child Behavior Checklist-Conduct Problems scale. Participants completed the Hebrew version of the children's Reading the Mind in the Eyes Test (cRMET)-a Theory of Mind measure. A bootstrapped multiple mediator model was used, adjusting for age and gender. RESULTS: ADHD symptoms were associated with CP. This association was not mediated by CUt or cRMET. CUt was associated with CP independent of ADHD symptom severity. CONCLUSIONS: ADHD symptoms and CUt both should be considered when assessing risk for CP and devising a treatment plan, in children with ADHD. Current results did not confirm the hypothesis that cRMET and CUt mediate between ADHD symptoms and CP. More studies employing larger samples, longitudinal design, and other emotion recognition measures are needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".