Role of frailty in predicting outcomes after stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Stroke is considered the second most common cause of death and the third leading cause of disability worldwide. Frailty, characterized by increased vulnerability to stressors, is emerging as a key factor affecting outcomes in older adults and stroke patients. This study aimed to estimate the prevalence of frailty in acute stroke patients and assess its association with mortality and poor functional outcome. Methods: Medline, Google Scholar, and Science Direct databases were systematically searched for English-language studies that included adult stroke patients (>16 years), have defined frailty, and reported mortality and functional outcomes. Meta-analysis was done using STATA 14.2, and the results were expressed as pooled odds ratios (OR) with 95% confidence intervals (CI). Heterogeneity was assessed using the I2 statistic and the Chi-square test. Study quality was evaluated using the Newcastle Ottawa Scale (NOS). Results: Twenty-five studies were included in the analysis. Frailty prevalence in stroke patients was 23% (95% CI 22% - 23%). Unadjusted analysis showed an OR of 2.66 (95% CI: 1.93 - 3.67) for mortality and 2.04 (95% CI: 1.49 - 2.80) for poor functional outcome. Adjusted estimates indicated an OR of 1.22 (95% CI: 1.1 - 1.35) for mortality and 1.21 (95% CI: 1.04 - 1.41) for poor functional outcome, with substantial heterogeneity for both adjusted and unadjusted analyses. No publication bias was detected for the prevalence of frailty. However, there was a publication bias for the association between frailty and mortality. Conclusions: Frailty was significantly associated with increased mortality and poorer functional outcomes in stroke patients. Our study highlights the need to focus on frailty in stroke patients to improve outcomes and quality of life. Further research should aim to standardize assessment of frailty and reduce heterogeneity in study outcomes. Systematic review registration: https://www.crd.york.ac.uk/prospero/#searchadvanced, CRD42023470325.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".