The impact of quality of life measured by WHOQOL-BREF on mortality in maintenance hemodialysis patients: a single center retrospective cross-sectional study
Bibliographic record
Abstract
BACKGRUOUND: Several previous studies have reported that quality of life (QoL) in hemodialysis patients affects mortality. However, the 36-item Short Form Health Survey, which has been used mainly in previous studies, is complicated in terms of questionnaire composition and interpretation. This study aimed to identify the impact of QoL on mortality in hemodialysis patients using an easier and simpler diagnostic tool. METHODS: This retrospective study included 160 hemodialysis patients. QoL was evaluated using the World Health Organization Quality of Life Questionnaire-Brief version (WHOQOL-BREF). Psychosocial factors were evaluated using the Hospital Anxiety and Depression Scale, Multidimensional Scale of Perceived Social Support, Montreal Cognitive Assessment, and Pittsburgh Sleep Quality Index. We also evaluated medical factors, such as dialysis adequacy and laboratory results. RESULTS: The mean hemodialysis vintage was 70.7±38.0 months. The proportion of patients who were elderly was higher in the mortality group than in the surviving group, and the Charlson Comorbidity Index score was also higher in the former group. Of the four domains of the WHOQOL-BREF, the physical health and psychological scores of the mortality group were significantly lower than those of the survival group. When the score in the physical health domain or psychological domain was ≤10, the 10-year mortality rate after hemodialysis initiation increased by approximately 2.3- and 2-fold, respectively. CONCLUSION: QoL may have a significant effect on mortality in patients undergoing hemodialysis. The WHOQOL-BREF is an instrument that can measure QoL relatively easily and can be used to improve the long-term prognosis of patients undergoing hemodialysis.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".