Association Between Frailty and Mortality, Falls and Hospitalisation Among Patients Undergoing Dialysis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
AIM: Frailty is a risk factor for adverse events in older individuals; however, it has not been fully verified in patients undergoing dialysis. Our aim was to verify the association between frailty and adverse outcomes consisting of mortality, falls and hospitalisation among patients undergoing dialysis by a systematic review and meta-analysis. DESIGN: Systematic review and meta-analysis. METHODS: Multiple internet databases, were searched from the establishment of each database to April 2022, including the PubMed, EMbase, Cochrane, CNKI, WanFang and China Science and Technology Journal (VIP) databases. Cohort studies exploring the association between frailty and adverse outcomes among patients undergoing dialysis were analysed. The Newcastle Ottawa Scale (NOS) was used to assess the risk of bias in the included studies. A random effects model was used to pool the effect size, and comprehensive analyses consisting of subgroup analysis, sensitivity analysis and publication bias were assessed. RESULTS: The search initially identified 2744 studies from six databases. After the screening, 26 studies including 14,089 patients with dialysis aged 44.95-78.10 years were included in the final analysis, all of which were observational cohort studies. The pooled results showed that frailty was a powerful predictor of adverse outcomes (mortality, falls and hospitalisation) among the patients. Therefore, dialysis patients should be screened for early frailty and appropriate interventions should be implemented to improve adverse outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".