Subjective cognitive decline across ethnoracial groups in the A4 study
Bibliographic record
Abstract
Abstract INTRODUCTION The associations between subjective cognitive decline (SCD), cognition, and amyloid were explored across diverse participants in the A4 study. METHODS Five thousand one hundred and fifty‐one non‐Hispanic White, 262 non‐Hispanic Black, 179 Hispanic‐White, and 225 Asian participants completed the Preclinical Alzheimer Cognitive Composite (PACC), self‐ and study partner‐reported Cognitive Function Index (CFI). A subsample underwent amyloid positron emission tomography (18F‐florbetapir) (N = 4384). We examined self‐reported CFI, PACC, amyloid, and study partner‐reported CFI by ethnoracial group. RESULTS The associations between PACC‐CFI and amyloid‐CFI were moderated by race. The relationships were weaker or non‐significant in non‐Hispanic Black and Hispanic White groups. Depression and anxiety scores were stronger predictors of CFI in these groups. Despite group differences in the types of study partners, self‐ and study partner‐CFI were congruent across groups. DISCUSSION SCD may not uniformly relate to cognition or AD biomarkers in different ethnoracial groups. Nonetheless, self‐ and study partner‐SCD were congruent despite differences in study partner type. Highlights Association between SCD and objective cognition was moderated by ethnoracial group. Association between SCD and amyloid was moderated by ethnoracial group. Depression and anxiety were stronger predictors of SCD in Black and Hispanic groups. Study‐partner and self‐reported SCD are congruent across groups. Study‐partner report was consistent despite difference in study partner types.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".