Analysis of frailty determinants in chronic stroke patients
Bibliographic record
Abstract
Objective Frailty is becoming more widely acknowledged as a critical factor that impacts the quality of life and health outcomes of patients with chronic conditions, including those who have experienced a stroke. This study aims to analyze the determinants of frailty in a prospective cohort of chronic stroke patients undergoing rehabilitation via relevant clinical, functional, and quality-of-life measures.Methods In this prospective study, 124 chronic stroke patients (mean age: 63.3 years, SD = 10.5) were assessed for frailty using the Edmonton Frailty Scale (EFS). Variables included age, stroke severity indices, functional status, and quality of life. Descriptive and inferential analyses was performed.Results The majority (81.5%) of patients had ischemic strokes. Frail patients were older (mean age: 64.6 vs. 55.2 years, p < 0.005), had more severe strokes (modified Rankin scale (mRS) 3.87 vs. 2.53, p < 0.005; National Institutes of Health Stroke Scale (NIHSS) 6.08 vs. 3.47, p < 0.005), greater functional impairment (Barthel Index 52.9 vs. 80.6, p < 0.005), and lower quality of life (2.78 vs. 4.02, p < 0.005). Logistic regression showed that advanced age and lower self-efficacy significantly predicted frailty (age: OR = 1.1, 95% CI: 1.01–1.21; Stroke Self-Efficacy Questionnaire (SSEQ): OR = 0.72, 95% CI: 0.55–0.95). The ROC analysis demonstrated that age had an AUC of 0.742 (95% CI: 0.65–0.86, p < 0.001), whereas the AUC for SSEQ was 0.924 (95% CI: 0.86–0.96, p < 0.001).Conclusions In patients with chronic stroke, frailty, as measured with the EFS, is best predicted by age and by the stroke-related impaired self-efficacy. Interestingly, the latter is a stronger frailty predictor, especially in younger patients. These findings indicate that both physiological and disease-related functional declines contribute to the development of frailty. However, additional longitudinal studies are necessary to validate the causal association and to account for potential confounding factors like depression or social support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".