The Child Behavior Checklist Together with the ADHD Rating Scale Can Diagnose ADHD in Korean Community-Based Samples
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the clinical validities and efficiencies of the Child Behavior Checklist (CBCL) and the ADHD Rating Scale-IV (ARS) in identifying children with attention-deficit hyperactivity disorder (ADHD) in Korean community-based samples. METHOD: A large sample of elementary school students (n = 1668) participated in this study. We used the CBCL and the ARS as the screening instruments. Diagnoses were determined by clinical psychiatric interviews and confirmed by DSM-IV-based structured interviews. RESULTS: Of the 46 subjects who underwent clinical psychiatric interviews, 33 were diagnosed as having ADHD. A T score of 60 with regard to the Attention Problems profile of the CBCL resulted in a reasonable level of sensitivity or positive predictive value in the diagnosis of ADHD. In both the parent and teacher reports of the ARS, 90th percentile cut-off points resulted in a high level of predictive value. The highest levels of specificity and positive predictive value were obtained when we combined the CBCL (T > or = 60 in Attention Problems) and the ARS (parent-teacher total > or = 90th percentile) reports. CONCLUSIONS: These findings suggest that the combined use of the CBCL and the ARS could serve as a rapid and useful clinical method of predicting or even diagnosing children with ADHD in epidemiologic case definitions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".