Should sex-specific norms be used to assess attention-deficit/hyperactivity disorder or oppositional defiant disorder?
Bibliographic record
Abstract
The authors investigated whether sex-specific norms should be used to assess symptoms of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) in girls. It was hypothesized that (a) there would be a group of girls who exhibit ADHD or ODD symptoms using sex-specific norms but not using Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria; (b) these girls would be significantly impaired relative to typically developing girls. These hypotheses were examined using behavior ratings completed by mothers and teachers of 1,491 elementary school students. Results showed that there was a small group of girls who did not meet DSM-IV criteria for ADHD or ODD but who had elevated ADHD and ODD scores when sex-specific norms were used. The same was not true for boys. The girls identified with sex-specific norms were more impaired than other girls. These results suggest that there may be a small number of girls who have behaviors and impairment that are consistent with ADHD and ODD, but they are not currently being identified by DSM-IV criteria.
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 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 | 0.033 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".