The Relationship Between Xerostomia, Nutrition, and Frailty in Older Patients Undergoing Hemodialysis
Bibliographic record
Abstract
Older adults receiving hemodialysis are at increased risk for xerostomia, poor nutritional status, and frailty, all of which significantly impact clinical outcomes and quality of life. This cross-sectional study examined interrelationships among xerostomia, nutrition, and frailty in patients aged 60 and older undergoing maintenance hemodialysis. Conducted between October 2022 and June 2023 in five dialysis centers, the study included 176 participants on hemodialysis for at least 3 months. Data were collected through face-to-face interviews using validated instruments: the Short Xerostomia Inventory, Mini Nutritional Assessment-Short Form, and Edmonton Frailty Scale. Pearson's correlation and path analysis using the Maximum Likelihood method were employed. Although xerostomia was reported at a low rate, many patients were at risk of malnutrition and showed varying degrees of frailty. Xerostomia was negatively associated with nutritional status and positively with frailty, while better nutritional status was linked to lower frailty. Path analysis revealed that xerostomia and nutritional status together explained nearly 50% of frailty variance. These findings underscore the importance of early identification and multidisciplinary management to reduce frailty and improve outcomes in older adults undergoing hemodialysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".