Diagnostic Instability of<i>DSM–IV</i>ADHD Subtypes: Effects of Informant Source, Instrumentation, and Methods for Combining Symptom Reports
Bibliographic record
Abstract
Using data from 123 children (aged 6-12 years) referred consecutively to a pediatric neuropsychiatry clinic by community physicians for assessment of Attention-Deficit/Hyperactivity Disorder (ADHD) and related problems, we investigated the effects of informant (parent, teacher), tool (interview, rating scale), and method for combining symptom reports ("and," "or" algorithms), on the diagnosis of ADHD and its subtypes. Results indicated that as many as 50% of cases were reclassified from one subtype to another, depending on whether information was derived from one or two informants, a semistructured clinical interview and/or rating scale, and the algorithm used to combine informant reports. We conclude that the diagnosis of DSM-IV ADHD subtypes is capricious in that it is influenced by clinicians' decisions regarding informants, instrumentation, and method for aggregating information across informants and instruments.
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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 | 0.119 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".