Relationship Between Frailty and Quality of Life and Pain Levels in Older Patients Undergoing Hemodialysis
Bibliographic record
Abstract
Objective: Frailty is associated with poorer outcomes in dialysis patients, including higher mortality.The purpose of this study was to investigate the connection between pain levels, frailty, and quality of life in older hemodialysis (HD) patients with chronic kidney disease (CKD). Materials and Methods:This cross-sectional study included 103 patients with CKD undergoing HD.Assessment tools included the Edmonton Frail Scale (EFS), the World Health Organization Quality of Life (WHOQOL-BREF) assessment, and the Geriatric Pain Measure (GPM).Patients who scored <24 on the Standardized Mini-Mental test and >7 on the Hamilton Depression Rating Scale were excluded.Results: The patients' mean age was 68.9±2.4 years, with a male-to-female ratio of 54:49.Significant correlations were found between GPM and WHOQOL-BREF (p=0.01,r=-0.659),GPM and EFS (p=0.02,r=0.622), and EFS and WHOQOL-BREF (p=0.01,r=-0.475).In a generalized linear regression model adjusted for age, comorbid conditions, unemployment, body mass index and education level, GPM was associated with higher EFS scores (β=1.69±0.31,p<0.001) and lower WHOQOL-BREF scores (β=-0.456±0.059,p<0.001). Conclusion:In older patients receiving HD, pain appears to contribute to worsening frailty and reduced quality of life.Effective pain management should be considered to mitigate frailty in this population.
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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 | 0.000 | 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) | 0.002 | 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".