Measuring Executive Function in the Differential Diagnosis of Attention-Deficit/Hyperactivity Disorder: Does It Really Tell Us Anything?
Bibliographic record
Abstract
Research initially supported the theory that deficits in executive function (EF) underlie the core neuropsychological sequelae of attention-deficit/hyperactivity disorder (ADHD), particularly deficits in working memory and inhibitory control arising from dysfunction in the prefrontal cortex. Consequently, neuropsychologists commonly employ measures of EF or prefrontal cortex dysfunction in the differential diagnosis of ADHD and its subtypes in children. However, recent findings have called the EF deficit theory of ADHD into question, and research on the specificity of both direct and indirect measures of EF has not yielded promising results. This article presents a brief, critical review of the past and current research on neuropsychological assessment of EF and ADHD and suggests how EF measures can, in light of the most current science, still remain a useful part of a neuropsychological test battery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".