Effects of sex, educational background, and chronic kidney disease grading on longitudinal cognitive and functional decline in patients in the Japanese Alzheimer's Disease Neuroimaging Initiative study
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to determine whether sex or education level affects the longitudinal rate of cognitive decline in Japanese patients in the Alzheimer's disease Neuroimaging Initiative study with defined mild cognitive impairment (MCI). METHODS: We accessed the entire Japanese Alzheimer's Disease Neuroimaging Initiative data set of 537 individuals, among whom 234 had MCI and 149 had Alzheimer's disease. We classified participants into three categories of educational history: (1) low, 0 to 9 years; (2) moderate, 10 to 15 years; and (3) high ≥16 years. We examined the main effects and interactions of visit, sex, and educational achievement on scores for the Clinical Dementia Rating Sum of Boxes, Alzheimer's Disease Assessment Scale-cognitive subscale 13, Mini-Mental State Examination, and Functional Activities Questionnaire in a longitudinal manner. RESULTS: ) ε4 status but were absent in Alzheimer's disease over a 2-year period. Subtle differences in chronic kidney disease grade affected the rate of decline. A higher Fazekas periventricular hyperintensity score was associated with a lower estimated glomerular filtration rate in women only. DISCUSSION: In patients with MCI, sex and educational history significantly affected the rate of change in cognitive and clinical assessments. Furthermore, a subtle decline in chronic kidney disease grade was associated with a faster rate of decline regardless of amyloid pathology in women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".