The puzzle of sex, gender and Alzheimer’s disease: Why are women more often affected than men?
Bibliographic record
Abstract
Objective: There are impressive differences in the incidence, prevalence and experience of women and men with Alzheimer’s Disease (AD). Notably, two-thirds of those with AD, the most common form of dementia, are women. Our objective was to provide a literature-based framework to understand these sex and gender differences in AD. Methods: We conducted a narrative review to examine sex and gender influences on AD. Results: We present a framework to understanding why these sex and gender differences exist in AD. This includes the influence of longevity (women live longer than men), biological differences (hormonal differences, epigenetics and frailty), differences in cognitive performance (women and men tend to perform differently on some cognitive tests), and gendered social roles and opportunities (educational and occupational opportunities, functional roles post-retirement). Our review clearly indicates the complex interaction of these sex and gender differences and variability within each. Conclusions: Given these important sex and gender differences in AD, we provide recommendations and steps forward describing how both sex and gender should be considered in dementia diagnosis and management and in the design and implementation of dementia research, including studies of caregiving interventions and models of dementia care.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".