A meta-analytic examination of comorbid hyperactive-impulsive-attention problems and conduct problems.
Bibliographic record
Abstract
The author quantitatively reviewed prevalence rates, defining features, associated features, developmental trajectory, and etiology to examine 3 taxonomic questions about comorbid hyperactive-impulsive-attention problems (HIA) and conduct problems (CP): Do HIA and CP co-occur randomly? Does comorbid HIA-CP differ from HIA-only and CP-only? Do HIA and CP combine synergistically? Results showed that HIA and CP co-occur at a greater than random rate, that comorbid HIA-CP differs from HIA-only and CP-only in multiple ways, and that there is little evidence that HIA and CP combine synergistically. However, sample type, grouping definition, age, gender, and subtype of disruptive behavior often moderated these findings. Overall, the review suggests that HIA-CP is best conceptualized as an additive combination of HIA and CP rather than as a distinct category.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".