Discriminating Between Children With ADHD and Classmates Using Peer Variables
Bibliographic record
Abstract
OBJECTIVE: Impaired peer relationships have long been recognized as one of the major functional problems of children with ADHD, but no specific guidelines on clinical levels of impairment in this domain exist. METHOD: This study used Receiver Operating Characteristics methodology to determine what aspects of peer functioning best discriminate between children with ADHD and their classmates. Optimal cutoffs indicative of clinical levels of impairment associated with ADHD diagnosis were determined for all variables. The participants were 165 children with AD/HD who were part of the Multimodal Treatment Study of Children With ADHD and their 1,298 classmates. RESULTS: Variables that best discriminated between children with ADHD and their classmates included peer rejection and negative imbalance between given and received liking ratings (i.e., children with ADHD liked others more than they were liked). CONCLUSION: Peer rejection and negative imbalance show most promise for identifying clinically significant levels of peer relationship impairment in children with ADHD. (J. of Att. Dis. 2009; 12(4) 372-380).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".