Sex and intelligence quotient differences in age of diagnosis among youth with attention‐deficit hyperactivity disorder
Bibliographic record
Abstract
OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental condition and is characterized by inattention, hyperactivity, and impulsivity. Research suggests that some populations, such as females and individuals with high intelligence quotients may be a risk for late ADHD diagnosis and subsequent treatment. Our goal is to advance our understanding of ADHD diagnosis, by examining (1) how child sex and cognitive abilities together are related to the age of diagnosis and (2) whether symptom presentation, current internalizing and externalizing symptoms, and demographic factors are related to age of diagnosis. METHODS: Our analyses contained children who completed the required tests (N = 568) from a pre-existing dataset of 1380 children with ADHD from the Province of Ontario Neurodevelopmental Disorders (POND) Network (pond-network.ca). First, we conducted a moderation analysis with sex as the predictor, cognitive abilities as the moderator, and age of diagnosis as the outcome. Second, we conducted correlation analyses examining how symptom presentation, current internalizing and externalizing symptoms, and demographic factors are related to age of diagnosis. RESULTS: Higher IQ was related to a later age of diagnosis. Higher hyperactive-impulsive symptoms and externalizing symptoms were related to an earlier age of diagnosis. Internalizing symptoms were trend associated with a later age of diagnosis in girls. Higher socioeconomic status and non-White maternal ethnicity were related to later age of diagnosis. CONCLUSIONS: IQ, sex, ADHD symptomology, internalizing symptoms, externalizing symptoms, and socio-demographic factors affect the age of diagnosis.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".