Investigating the Factor Structure of the Preclinical Alzheimer Cognitive Composite and Cognitive Function Index across Racial/Ethnic, Sex, and Aβ Status Groups in the A4 Study
Bibliographic record
Abstract
BACKGROUND: Disparities in Alzheimer's disease (AD) are well-documented among different racial/ethnic groups and between sex/genders. Neuropsychological assessment provides important information about cognitive changes and can offer valuable insights into disparities. However, neuropsychological measures must be comparable across racial/ethnic and sex/gender groups to accurately interpret disparities. OBJECTIVES: To evaluate measurement invariance (equivalence) of the Preclinical Alzheimer Cognitive Composite (PACC) and the Cognitive Function Index across racial/ethnic, sex/gender, and β-amyloid (Aβ) status groups. DESIGN, SETTING, PARTICIPANTS: Cross-sectional analysis of screening data from the Anti-Amyloid in Asymptomatic AD (A4) Study. The study enrolled participants aged 65-85 from sites across the United States, Canada, Australia, and Japan. MEASUREMENTS: Participants completed the PACC and the Cognitive Function Index. Participants classified as cognitively normal also underwent a Positron Emission Tomography (PET) scan to determine Aβ status. RESULTS: Participants self-identified as non-Hispanic White (n=5241), non-Hispanic Black (n=267), Asian (n=228), or Hispanic White (n=225) as well as male (n=2885) or female (n=3076). Among those who underwent a PET scan, 3115 were classified as Aβ- and 1309 were classified as Aβ+. We found support for a one-factor model for both the PACC and Cognitive Function Index across the full sample and in samples stratified by race/ethnicity, sex/gender, and Aβ status. The one-factor model of the PACC and Cognitive Function Index demonstrated scalar measurement invariance across racial/ethnic, sex/gender, and Aβ status groups. CONCLUSIONS: Our findings suggest that performance on the PACC and Cognitive Function Index can be compared across the racial/ethnic, sex/gender, and Aβ status groups examined in this study.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".