Computerized Cognitive Test Batteries for Children and Adolescents—A Scoping Review of Tools For Lab- and Web-Based Settings From 2000 to 2021
Bibliographic record
Abstract
OBJECTIVE: Cognitive functioning is essential to well-being. Since cognitive difficulties are common in many disorders, their early identification is critical, notably during childhood and adolescence. This scoping review aims to provide a comprehensive literature overview of computerized cognitive test batteries (CCTB) that have been developed and used in children and adolescents over the past 22 years and to evaluate their psychometric properties. METHOD: Among 3192 records identified from three databases (PubMed, PsycNET, and Web of Science) between 2000 and 2021, 564 peer-reviewed articles conducted in children and adolescents aged 3 to 18 years met inclusion criteria. Twenty main CCTBs were identified and further reviewed following PRISMA guidelines. Relevant study details (sample information, topic, location, setting, norms, and psychometrics) were extracted, as well as administration and instrument characteristics for the main CCTBs. RESULTS: Findings suggest that CCTB use varies according to age, location, and topic, with eight tools accounting for 85% of studies, and the Cambridge Neuropsychological Test Automated Battery (CANTAB) being most frequently used. Few instruments were applied in web-based settings or include social cognition tasks. Only 13% of studies reported psychometric properties. CONCLUSIONS: Over the past two decades, a high number of computerized cognitive batteries have been developed. Among these, more validation studies are needed, particularly across diverse cultural contexts. This review offers a comprehensive synthesis of CCTBs to aid both researchers and clinicians to conduct cognitive assessments in children in either a lab- or web-based setting.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".