The cumulative effect of frailty and cognition on mortality – results of a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Both physical frailty and cognitive impairment predict death, but the joint effect of these two factors is uncertain. The objectives are to determine if the Mini-mental state examination (MMSE) and the Frailty Index (FI) predict death over a five-year interval after accounting for the effect of the other; and if there is an interaction in this effect. METHODS: An analysis of an existing prospective cohort study of 1,751 community living older adults followed over a five-year time frame. Age, gender, and education were self-reported. The predictor variables were the FI - a measure of frailty based on the "Accumulation of Deficits" model of frailty; and the MMSE. Cox proportional hazards models were constructed for the outcome of time to death. RESULTS: The unadjusted Hazard Ratio (HR) (95% CI) for mortality was 2.17 (1.69, 2.80) for those who were only cognitively impaired, 2.02 (1.53, 2.68) for those who were only frail, and 3.57 (2.75, 4.62) for those who were both frail and cognitively impaired with the reference group of those who were neither frail nor cognitively impaired. Adjusted for age, gender, and education, the HR (95% CI) was 1.49 (1.13. 1.95) for those who were only cognitively impaired, 1.81 (1.35, 2.41) for those who were only frail, and 2.28 (1.69, 3.09) for those who were both frail and cognitively impaired. CONCLUSIONS: Both frailty and cognitive impairment are predictors of mortality and the effect is cumulative. There was no interaction in this effect.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".