Racial‐ethnic differences in baseline and longitudinal change in neuropsychological test scores in the NACC Uniform Data Set 3.0
Bibliographic record
Abstract
Abstract Background Racial/ethnic differences in cross‐sectional assessment of cognitive test performance are well known. However, longitudinal studies of differences in cognitive decline have been mixed. The purpose of this study was to examine racial/ethnic differences in baseline and longitudinal change on the Uniform Data Set (UDS) version 3 of the NIA Alzheimer’s Disease Research Centers program. Method Longitudinal data from 2,806 participants (2336 non‐Hispanic White, 350 non‐Hispanic Black, 120 Hispanics tested in Spanish) were acquired from the National Alzheimer’s Coordinating Center (NACC), and included baseline and at least two follow‐up visits. We used marginal linear regression models to examine racial/ethnic differences in standardized test scores, by including racial group indicators, time (in years) since initial visit and their interaction, controlling for baseline age, sex, education and changes in global CDR scores during the follow‐up. Additional models examining longitudinal change also controlled for baseline score. The outcome variables included the Montreal Cognitive Assessment (MoCA, Number Span (Forward/Backward), Craft Story 21 Recall (Immediate/Delayed), Multilingual Naming Test (MINT), Category Naming (animals and vegetables), Trail Making A and B, and Benson Figure (Copy/Recall). Estimates were obtained from generalized estimating equations with an exchangeable working correlation, and robust standard errors were used to construct confidence intervals and compute p‐values. Result Black and Hispanic participants had lower baseline scores on all tests (difference in standard deviation units: ‐0.029 to ‐0.858 for blacks, and ‐0.014 to ‐1.214 for Hispanics), but showed attenuated decline on all tests (difference in standard deviation units per year: 0.002 to 0.102 for blacks, 0.022 to 0.296 for Hispanics) compared to non‐Hispanic Whites. When baseline test scores were controlled, the differences in longitudinal changes became mostly statistically non‐significant. Conclusion Despite large racial/ethnic differences in baseline test scores, there were no racial/ethnic differences in longitudinal change over time once baseline differences were controlled. Further, results suggest that the utility of baseline test scores in UDS version 3 to predict longitudinal change may not be compromised in racially diverse populations.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".