Interpreting Patterns of Low Scores on the NIH Toolbox Cognition Battery
Bibliographic record
Abstract
INTRODUCTION: The National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function Cognition Battery is comprised of seven cognitive tests, including two tests measuring crystallized cognitive ability (i.e., vocabulary and reading) and five tests measuring fluid cognitive functioning (i.e., working memory, memory, speed of processing, and executive functioning). This study presents comprehensive base rate tables for the frequency of low scores in adults and older adults from the normative sample. METHODS: Participants were 843 adults, ages 20-85, from the NIH Toolbox standardization sample who completed all seven cognition tests. Rates of low scores were derived for standard age-adjusted and fully-demographically-adjusted scores at multiple cut-scores. Base rates were stratified by education, crystallized intellectual ability, and cognitive domain. RESULTS: Using the five demographically-adjusted fluid cognitive test scores, 45.9% of adults obtained one or more scores at or below the 16th percentile, and 16.8% obtained one or more score at or below the 5th percentile, which is consistent with findings from other neurocognitive test batteries. DISCUSSION: Based on the study findings, nearly 50% of adults in the general population would meet psychometric criteria for a diagnosis of the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) Mild Neurocognitive Disorder (MND). We developed new psychometric criteria for identifying MND using the NIH Toolbox Cognition Battery that reduce the false positive rate. Knowing these multivariate normative base rates will help researchers and clinicians interpret NIH Toolbox scores in people with neurodevelopmental, psychiatric, medical, neurological, and neurodegenerative disorders that affect cognitive functioning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".