Frailty Risk After a Cardiovascular Event Among Community-Dwelling Older People: Influence of Sociodemographic, Polypharmacy, and Pre-event Frailty
Bibliographic record
Abstract
BACKGROUND: Frailty is a significant concern for older adults and can increase after a major health event. This study examined the risk of incident frailty after a cardiovascular disease (CVD) event among community-dwelling older people aged ≥ 65 years and explored whether sociodemographic factors, polypharmacy, and pre-event frailty influence their risk of developing frailty after a CVD event. METHODS: This study included a cohort of 738 participants (38.5% women) from the ASPREE study who were not classified as frail before their CVD event. Frailty was measured annually using the 64-item deficit-accumulation frailty index. RESULTS: Over an average 2.6 years after a CVD event, 333 individuals had incident frailty. In logistic regression models, increased chronological age, being a woman, and having polypharmacy were associated with 4% to 83% increased odds of developing frailty after a CVD event. Individuals with CVD residing in the inner regional area had about 50% higher odds of having frailty than those living in cities. This association was more evident among stroke survivors, with both inner regional (adjusted odds ratio [OR] 2.13) and outer regional or remote residents (adjusted OR 2.37) having greater odds of frailty. Individuals who were classified as pre-frail before their CVD event, had notably higher odds of progressing to frailty after the CVD event (adjusted OR 3.41). CONCLUSIONS: Our community-based study provides robust evidence that individuals who were women, older, pre-frail, with polypharmacy, and living in regional or remote areas have a markedly greater odds of developing frailty after a CVD event.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".