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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".