P.124 Baseline Assessment of Attention and Executive Function Deficits in Children with Neurodevelopmental Disorders: Data from a Speciality Attention Clinic
Bibliographic record
Abstract
Background: Attention and executive function (EF) deficits in children negatively impact academics, social interactions, and overall quality of life. Children with other brain-based disorders are at high risk for attention and EF concerns, but the effects of these impairments are not well studied in the literature. The Complex Attention and Executive Function Clinic at the Alberta Children’s Hospital collected baseline data on patients referred for concerns of attention deficits co-occurring with diagnosed neurologic illness/injury, or neurodevelopmental disorder (NDD). Methods: The Behaviour Rating Inventory of Executive Function (BRIEF-2), Behaviour Assessment System for Children (BASC-3), Parenting Stress Index (PSI-4) and medical and past treatment information were collected on initial clinic visit for patients aged 5-15 years. Results: BRIEF-2 Global Executive Composite demonstrated 88.9% of children had clinically elevated scores. Clinically significant scores were observed in 55.5% for BASC-3 Adaptive Skills index and 40% of parents in PSI-4 Total Stress scores. Conclusions: Children with neurologic illness/NDDs are at high risk of clinical impairments in attention and EF. In children referred for attention and behavioural regulation, there is clinically significant increased reporting of executive function impairment out of proportion to other behavioural difficulties. The clinic aims to improve overall functioning through treatment of unmanaged attention and EF deficits.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".