Multimorbidity Patterns, Frailty, and Survival in Community-Dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: Frailty and multimorbidity are independent prognostic factors for mortality, but their interaction has not been fully explored. We investigated the importance of multimorbidity patterns in older adults with the same level of frailty phenotype. METHODS: In a cohort of 7,197 community-dwelling adults aged 65 years and older, physical frailty status (robust, pre-frail, frail) was defined using shrinking, exhaustion, inactivity, slowness, and weakness. Latent class analysis was used to identify individuals with multimorbidity patterns based on 10 self-reported chronic conditions. We estimated hazard ratios (HR) and incidence rate differences (IRDs) for mortality comparing multimorbidity patterns within each frailty state. RESULTS: Five multimorbidity classes were identified: minimal disease (24.7%), cardiovascular disease (29.0%), osteoarticular disease (27.3%), neuropsychiatric disease (8.9%), and high multisystem morbidity (10.0%). Within each frailty state, the mortality rate per 1,000 person-years over 4 years was greatest in the neuropsychiatric class and lowest in the minimal disease class: robust (56.3 vs 15.7; HR, 2.11 [95% CI: 1.05, 4.21]; IRD, 24.1 [95% CI: -11.2, 59.3]), pre-frail (85.3 vs 40.4; HR, 1.74 [95% CI: 1.28, 2.37]; IRD, 27.1 [95% CI: 7.6, 46.7]), and frail (218.1 vs 96.4; HR, 2.05 [95% CI: 1.36, 3.10]; IRD, 108.4 [95% CI: 65.0, 151.9]). Although HRs did not vary widely by frailty, the excess number of deaths, as reflected by IRDs, increased with greater frailty level. CONCLUSIONS: Considering both multimorbidity patterns and frailty is important for identifying older adults at greater risk of mortality. Of the five patterns identified, the neuropsychiatric class was associated with lower survival across all frailty levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".