Prevalence and implications of frailty in acute stroke: systematic review & meta-analysis
Bibliographic record
Abstract
BACKGROUND: frailty is common in older adults and associated with poor outcomes following illness. Although stroke is predominantly a disease of older people, our knowledge of frailty in stroke is limited. We aimed to collate the literature on acute stroke and frailty to estimate the prevalence of pre-stroke frailty and its associations with outcomes. METHODS: paired researchers searched multidisciplinary electronic databases for papers describing frailty and acute stroke. We assessed risk of bias using Newcastle-Ottawa tools appropriate to study design. We created summary estimates of pre-stroke frailty using random effects models. We collated whether studies reported significant positive associations between frailty and clinical outcomes in adjusted models. RESULTS: we included 14 studies (n = 27,210 participants). Seven studies (n = 8,840) used a frailty index approach, four studies (n = 14,924) used Hospital Frailty Risk Scores. Pooled prevalence of pre-stroke frailty was 24.6% (95% confidence interval, CI: 16.2-33.1%; low quality evidence, downgraded due to heterogeneity, bias). Combining frailty and pre-frailty (nine studies, n = 23,827), prevalence of any frailty syndrome was 66.8% (95%CI: 49.9-83.7%). Seven studies were at risk of bias, from participant selection or method of frailty assessment. Pre-stroke frailty was associated with all adverse outcomes assessed, including longer-term mortality (positive association in 6 of 6 studies reporting this outcome; odds ratio: 3.75 [95%CI: 2.41-5.70]), length of admission (3 of 4 studies) and disability (4 of 6 studies). CONCLUSIONS: despite substantial heterogeneity, whichever way it is measured, frailty is common in patients presenting with acute stroke and associated with poor outcomes. This has implications for the design of stroke services and pathways.
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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".