Comparison of the predictive validity of hyperkinetic disorder and attention deficit hyperactivity disorder.
Bibliographic record
Abstract
INTRODUCTION: We compared the predictive validity of attention deficit hyperactivity disorder (ADHD; Diagnostic and Statistical Manual - IV Edition) and hyperkinetic disorder (HKD; International Classification of Diseases - 10th Edition) while controlling for the presence of comorbid psychopathology. METHOD: ADHD and HKD criteria were used to classify 804 clinic-referred children ages 6 to 16 years into one of four non-overlapping groups: HKD, ADHD combined subtype (ADHD-C), ADHD hyper-active-impulsive subtype (ADHD-HI), ADHD inattentive subtype (ADHD-IA). Groups were compared with each other and with normal controls (67) while controlling for age and intelligence on a range of criteria both before and after excluding cases with comorbidity. RESULTS: Of the 804 clinic participants, 72 (8.9 %) met criteria for ICD-10 HKD, 353 (43.9 %) for ADHD-C, 142 (17.7 %) for ADHD-HI and 237 (29.5 %) for ADHD-IA. There were no differences among the four clinic groups in rate of comorbidity, neuro-developmental or psychosocial risk indices, inter-parental or parent-child discord, family history of ADHD, working memory, and academic or intelligence test scores, but all clinic groups differed from normal controls. By contrast, total number of symptoms, teacher-rated impairment and inhibitory control deficit were greatest in HKD and least in ADHD-C, ADHD-HI, ADHD-IA in that order. Results of the comparisons were essentially unchanged after excluding cases (75%) with a comorbid condition. CONCLUSIONS: HKD, ADHD-C, ADHD-HI and ADHD-IA had approximately equivalent predictive validity even when comorbidity was taken into account.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".