Predictive validity of DSM‐IV and ICD‐10 criteria for ADHD and hyperkinetic disorder
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to compare the predictive validity of the two main diagnostic schemata for childhood hyperactivity - attention-deficit hyperactivity disorder (ADHD; Diagnostic and Statistical Manual- IV) and hyperkinetic disorder (HKD; International Classification of Diseases- 10th Edition). METHODS: Diagnostic criteria for ADHD and HKD were used to classify 419 children ages 6 to 16 years referred to a clinic for behavioral problems into one of four groups: HKD, ADHD combined subtype (ADHD-C), ADHD hyperactive-impulsive subtype (ADHD-HI), ADHD inattentive subtype (ADHD-IA). These groups were compared on clinical characteristics including total symptom severity, overall impairment, exposure to psychosocial and neuro-developmental risks, family history of ADHD in first-degree family members, rate and type of comorbidity, intelligence, academic achievement, and on laboratory tests of motor response inhibition and working memory with each other and with normal controls (47). RESULTS: Of the 419 cases, there were 46 HKD (11.0%), 200 ADHD-C (47.7%), 60 ADHD-HI (14.3%) and 113 ADHD-IA (27.0%) cases. The HKD group had more symptoms and was more impaired on teachers' ratings than were the other groups. The ADHD-C and HKD groups had poorer inhibitory control than the ADHD-IA, ADHD-HI and control groups, and all four clinic groups showed inhibition deficit compared to controls. Groups did not differ in working memory. Compared to controls, the HKD, ADHD-C, ADHD-HI and ADHD-IA groups had higher familial risk of ADHD, greater psychosocial risk exposure, lower intellectual level and poorer academic attainment. However, we observed no differences among the clinic groups in these characteristics. CONCLUSIONS: Like earlier versions, ICD-10 and DSM-IV continue to delineate diagnostic entities with substantially different prevalence in clinic samples. However, HKD, ADHD-C, ADHD-IA and ADHD-HI groups overlap substantially in terms of important clinical characteristics, although HKD and ADHD-C may be somewhat more severe variants of the condition than ADHD-IA and ADHD-HI.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".