Self-reported health change in haemodialysis recipients modulates the effect of frailty upon mortality and hospital admissions: outcomes from a large prospective UK cohort
Bibliographic record
Abstract
BACKGROUND: Frailty among haemodialysis patients is associated with hospitalization and mortality, but high frailty prevalence suggests further discrimination of risk is required. We hypothesized that incorporation of self-reported health with frailty measurement may aid risk stratification. METHODS: Prospective cohort study of 485 prevalent haemodialysis recipients linked to English national datasets. Frailty Phenotype (FP), Frailty Index (FI), Edmonton Frail Scale (EFS), Clinical Frailty Scale (CFS) and self-reported health change were assessed. Mortality was explored using Fine and Gray regression, and admissions by negative binomial regression. RESULTS: Over a median 678 (interquartile range 531-812) days, there were 111 deaths, and 1241 hospitalizations. Increasing frailty was associated with mortality on adjusted analyses for FP [subdistribution hazard ratio (SHR) 1.26, 95% confidence interval (CI) 1.05-1.53, P = .01], FI (SHR 1.21, 95% CI 1.09-1.35, P = .001) and CFS (SHR 1.32, 95% CI 1.11-1.58, P = .002), but not EFS (HR 1.08, 95% CI 0.99-1.18, P = .1). Health change interacted with frailty tools to modify association with mortality; only those who rated their health as the same or worse experienced increased mortality hazard associated with frailty by FP (Pinteraction = .001 and 0.035, respectively), FI (Pinteraction = .002 and .007, respectively) and CFS (Pinteraction = .009 and 0.02, respectively). CFS was the only frailty tool associated with hospitalization (incidence rate ratio 1.12, 95% CI 1.02-1.23, P = .02). CONCLUSIONS: We confirm the high burden of hospitalization and mortality associated with haemodialysis patients regardless of frailty tool utilized and introduce the discriminatory ability of self-reported health to identify the most at-risk frail individuals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".