MULTIMORBIDITY PATTERNS PROVIDE ADDED PROGNOSTIC INFORMATION BEYOND FRAILTY STATUS IN OLDER ADULTS
Bibliographic record
Abstract
Assessing frailty is useful in measuring heterogeneity of health status and predicting prognosis in older adults. However, individuals in a given frailty state have diverse multimorbidity patterns, some of which may portend poorer prognosis. This retrospective cohort study aimed to evaluate the impact of frailty and multimorbidity patterns on mortality in 7197 community-dwelling older adults in the National Health and Aging Trends Study 2011–2015. Individuals were assessed for the Fried frailty phenotype and 10 chronic conditions in 2011. Latent class analysis uncovered 5 multimorbidity patterns: minimal disease (n=1780), cardiovascular disease (CVD) (n=2087), non-CVD (n=1968), neuropsychiatric disease (n=641), and very sick (n=721). Robust individuals had minimal disease (41.4%), CVD (27.6%), or non-CVD (26.0%), whereas frail individuals had CVD (24.7%), non-CVD (21.9%), neuropsychiatric disease (22.7%), or very sick patterns (23.6%). During the 4-year period, the mortality risk was 6.6% for the robust (n=147/2213), 15.4% for the pre-frail (n=561/3647), and 37.7% for the frail (n=504/1337). Within each frailty state, the mortality varied substantially across the multimorbidity patterns, with minimal disease being the lowest and neuropsychiatric disease being the highest: 5.0–19.2% for the robust, 12.5–25.3% for the pre-frail, and 27.7–55.6% for the frail. Notably, compared with minimal disease, CVD was significantly associated with increased mortality only in robust and pre-frail individuals, not in frail individuals. Neuropsychiatric disease and very sick patterns were significantly associated with increased mortality only in pre-frail and frail individuals. These findings underscore the importance of considering clinically meaningful multimorbidity patterns in addition to frailty for better prognostication in older adults.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".