10‐year frailty trajectory is associated with Alzheimer’s dementia after considering neuropathological burden
Bibliographic record
Abstract
MAIN PROBLEM: Frailty is an established risk factor for cognitive decline and Alzheimer's disease. Few studies have examined the longitudinal relationship between frailty and cognition. METHODS: = 625, 67.5% female, 83.2 ± 5.9 years at baseline) underwent annual clinical evaluations (average follow-up 5.6 ± 3.7 years) followed by neuropathologic assessment after death. A frailty index was calculated from 41 health variables at each evaluation. Clinical diagnosis of MCI and/or dementia was ascertained by clinical data review (blinded to neuropathological data) after death. Age, sex, education, and neuropathological burden (10-item index) were evaluated as covariates. Frailty trajectories were calculated using a mixed effects model. RESULTS: At baseline the mean frailty index = 0.24 ± 0.12 and increased at rate of 0.026 or ~1 deficit per year. At death, 27.7% of the sample had MCI, and 38.6% had dementia. Frailty trajectories were significantly steeper among those individuals who were ultimately diagnosed as clinically impaired prior to death, even after controlling for age, sex, education, and neuropathological index. CONCLUSIONS: Findings suggest a strong link between health status (frailty index) and dementia, even after considering neuropathology. Frailty trajectories were associated with risk for MCI and dementia, underscoring the importance of addressing frailty to manage dementia risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".