Identifying Cognitive Problems in Children and Adolescents with Depression Using Computerized Neuropsychological Testing
Bibliographic record
Abstract
Depression in children and adolescents can negatively impact cognitive functioning, social development, and academic performance. The purpose of this study was to determine whether a computerized battery of neuropsychological tests could detect neurocognitive difficulties in children and adolescents with depression. Participants included 30 children and adolescents between the ages of 9 and 17 years (M = 14.6, SD = 2.1) with a clinical diagnosis of depression. Healthy control participants were individually matched on age, education, sex, race, primary language, handedness, and self-reported computer familiarity. All participants completed the Central Nervous System Vital Signs computerized battery. This battery of seven tests yields 23 test scores and 5 domain scores (Memory, Psychomotor Speed, Reaction Time, Complex Attention, and Cognitive Flexibility). Children and adolescents with depression performed worse on the Memory (Cohen's d = .43) and Complex Attention domains (d = .58) than matched controls. On the individual test scores, children and adolescents with depression performed worse on delayed verbal memory (d = .63), delayed visual memory (d = .34), measures of reaction time (d = .34-.53), and accuracy/inhibition on complex attention tasks (d = .49-.65). When considering the five domain scores simultaneously, children and adolescents with depression were more likely to have two or more scores at or below the 5th percentile (p = .05). Children and adolescents with depression have problems with reduced processing speed, memory for verbal information, and executive functioning on this computerized battery of tests, which represents a feasible method for neuropsychological screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".