Sensitivity and Specificity of an Executive Function Screener at Identifying Children With ADHD and Reading Disability
Bibliographic record
Abstract
Objective: This study evaluated the sensitivity/specificity of a global sum score (GSS) from the Behavior Assessment System for Children, Second Edition, Executive Function screener (BASC-2-EF) at classifying children with/without ADHD and/or reading disability (RD). Method: The BASC-2 Teacher/Parent Rating Scales (TRS/PRS) were completed for children (8-12 years old; 43.1% female) with no diagnosis ( n = 53), RD ( n = 34), ADHD ( n = 85), co-morbid RD/ADHD ( n = 36), and other diagnoses ( n = 15). Receiver operating characteristic (ROC) curve analyses evaluated the sensitivity/specificity of the BASC-2-EF GSS at discriminating between children with/without ADHD or RD. Results: Area under the curve (AUC) scores indicated the sensitivity/specificity of the BASC-2-EF GSS at discriminating between children with/without ADHD (TRS: AUC = .831, p < .001; PRS: AUC = .919, p < .001), with/without RD (TRS: AUC = .724, p = .001; PRS: AUC = .615, p = .101), and with ADHD or RD through post hoc analysis (TRS: AUC = .674, p = .006; PRS: AUC = .819, p < .001). Conclusion: The findings support utilizing the BASC-2-EF GSS when differentiating ADHD from RD and typical development.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".