Mild Cognitive Impairment, Dementia, and Their Subtypes in Oldest Old Women
Bibliographic record
Abstract
BACKGROUND: The population of oldest old is increasing, but the prevalence of cognitive impairment is not well characterized in this group. OBJECTIVES: To determine the prevalence of mild cognitive impairment (MCI), dementia, and their subtypes in oldest old women and to examine whether some groups of oldest old women were more likely to have cognitive impairment. DESIGN: Prospective cohort study. SETTING: Women Cognitive Impairment Study of Exceptional Aging. PARTICIPANTS: A total of 1299 oldest old (≥85 years) women. MAIN OUTCOME MEASURES: All the women completed a neuropsychological test battery. Those who screened positive for possible cognitive impairment (n = 634) were further assessed for a diagnosis of dementia, MCI, or normal cognition. The remaining women (n = 665) were considered cognitively normal. Dementia and MCI subtypes were determined using standard criteria. RESULTS: The women had a mean age of 88.2 years, and 27.0% were 90 years or older; 231 women (17.8%) were diagnosed as having dementia and 301 (23.2%) as having MCI, for a combined cognitive impairment prevalence of 41.0%. Clinical features consistent with Alzheimer disease and mixed dementia were most common, each accounting for 40% of dementia cases. Amnestic multiple domain and nonamnestic single domain were the most common MCI types, accounting for 33.9% and 28.9% of cases, respectively. Cognitive impairment was more frequent in women 90 years or older compared with those 85 to 89 years (dementia, 28.2% vs 13.9%, P < .001; MCI, 24.5% vs 22.7%, P = .02) and was more common in women with less education, a history of stroke, and prevalent depression. CONCLUSIONS: In this large sample of oldest old women, 41.0% had clinically adjudicated cognitive impairment. Subtypes of dementia and MCI were similar to those in younger populations. Women in the fastest growing demographic, the oldest old, should be screened for cognitive disorders, especially high-risk groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".