The relationship between fear of falling and frailty in older adults undergoing hemodialysis
Bibliographic record
Abstract
Background: Frailty and fear of falling (FOF) are highly prevalent in older adults undergoing hemodialysis (HD). However, there is no reliable evidence of the association between FOF and frailty in older adults undergoing HD.Objectives: This study aimed to determine the association between FOF and frailty in older adults undergoing HD.Methods: This cross-sectional study was conducted on 194 older adults undergoing HD in the east of Mazandaran province, Iran. Data were collected using of demographic and clinical characteristics questionnaire, Edmonton Frail Scale (EFS), and Falls Efficacy Scale–International (FES-I). Independent t-test, analysis of variance, Pearson’s correlation coefficient, and stepwise multiple regression were used to analyze the data.Results: The mean score of the FOF was 34.41±12.20, and most of the participants (38.4%) had moderate FOF. The mean score of the frailty was 6.91±3.12 and most of older adults (34.3%) were vulnerable to frailty. There was a positive and significant association between FOF and frailty in older adults undergoing HD (r=0.802, P<0.001).Conclusion: The majority of older adults receiving chronic HD have moderate to high FOF and are prone to frailty. Therefore, it is necessary to perform appropriate educational, behavioral, and cognitive interventions to reduce the FOF in these patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".