Symptoms Defined by Parents' and Teachers' Ratings in Attention-Deficit Hyperactivity Disorder: Changes with Age
Bibliographic record
Abstract
OBJECTIVE: To determine whether Child Behavior Checklist/4-18 (CBCL) and Teacher Report Form (TRF) scores of children and adolescents with a first-time diagnosis of attention-deficit hyperactivity disorder (ADHD) are different and whether there is a similar difference in normal control subjects. METHOD: We analyzed the CBCL and TRF scores of 146 patients (124 boys and 22 girls, aged 6 to 18 years; mean age 11.0 years, SD 3.6). We analyzed the same scores for 274 age and sex-matched control subjects recruited from a nationally representative sample. RESULTS: Subjects with ADHD had significantly higher CBCL and TRF scores than control subjects. Age was significantly correlated with scores on the CBCL and TRF subscales Social Withdrawal, Somatic Complaints, and Internalization Problems; with scores on the CBCL subscale Attention Problems; and with scores on the TRF subscale Anxiety-Depression. In the group with ADHD, age was negatively correlated with scores on the CBCL and TRF subscale Externalizing Problems and with scores on the TRF subscale Aggressive Behavior. In the control group, the only significant correlation was between age and the CBCL subscale Somatic Complaints score. CONCLUSIONS: These results indicate that underdiagnosis of ADHD in childhood may cause the emergence of greater internalization problems in adolescence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".