Cognitive performance and normative data between Hispanic and non‐Hispanic cohorts: Results from the South Texas Alzheimer’s Disease Research Center (ADRC)
Bibliographic record
Abstract
Abstract Background The prevalence of Alzheimer’s disease and related dementias (ADRD) in the United States was estimated as 6.5 million people in 2022, with a five‐fold increase for the Hispanic/Latinx population expected by 2060. The South Texas Alzheimer’s Disease Center (STAC) was designated as a new ADRC in 2021 by the National Institute on Aging (NIA) with a specific aim to serve the growing needs of the local underrepresented Hispanic population. As cultural and linguistic factors can impact performance on cognitive tests, the goal of the study was to compare UDS‐3 cognitive test raw scores and normative data in Hispanic and non‐Hispanic adults without cognitive impairment residing in South Texas. Method Participants from the STAC cohort completed the Uniform Data Set (UDS), V.3.0, which includes demographics and neuropsychological battery. All batteries were administered in the participants’ preferred language, English. Normative data was calculated using Weintraub et al. (2018)’s age, sex, and education adjusted regression models for UDSNB 3.0. Mean differences between baseline visit raw scores and normative data were compared using independent sample t‐tests among Hispanic and non‐Hispanic participants. Result Thirty‐four Hispanic (mean age = 70.4, 67.6% female) and thirty‐eight non‐Hispanic (mean age = 71.9, 57.9% female) participants were included. Hispanic participants had fewer years of education relative to non‐Hispanic participants [M(SD)] = [14.7(2.5)] to [16.5(2.5)], respectively; (t(70.1) = 3.0, p = 0.004); although, the groups did not differ in age or sex distribution (p>0.05). Hispanic and non‐Hispanic participants generally performed equivalently on the neuropsychological battery. However, Hispanics had lower mean raw scores on the Montreal Cognitive Assessment (MoCA) (t(70.8) = 3.6, p<0.001) and the Multilingual Naming Test (MINT) (t(71) = 4.0, p<0.001) relative to non‐Hispanic participants, which persisted when normative data was applied (MoCA: t(71) = 2.3, p = 0.024, MINT: t(71) = 2.8, p = 0.006). Conclusion Overall, Hispanic and non‐Hispanic participants performed similarly on the UDS‐3 neuropsychological battery. However, Hispanics had lower mean raw and normative scores on the MINT, as well as the MoCA which also includes language measures. Our findings highlight the importance of future research validating the sensitivity and specificity of normative data used in underrepresented populations, especially those at higher risk for ADRD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".