SP640Medical and psychosocial factors for mortality in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: The survival rate of patients has greatly been improved due to the development of hemodialysis (HD). However, the psychosocial factors including quality of life (QoL) of patients with HD are much lower than those of general population. Various factors are known to affect the mortality of HD patients. The aim of this study was to identify the relationship between mortality and medical and psychosocial factors as well as QoL in HD patients. METHODS: The study included 160 patients with end-stage renal disease who were undergoing HD (91 males with mean age 58.1±11.8 years). The QoL was evaluated using WHO QoL of Life scale abbreviated version (WHOQOL-BREF). Psychosocial factors were evaluated using the Hospital Anxiety and Depression Scale (HADS), Multidimensional Scale of Perceived Social Support (MSPSS), Montreal Cognitive Assessment (MoCA) and Pittsburgh Sleep Quality Index (PSQI). We also evaluated medical factors such as markers of dialysis adequacy and laboratory results (cholesterol, albumin, hemoglobin, intact PTH, calcium, phosphorus, etc.). RESULTS: The mean duration of HD was 29.9±35.1 months. There were 41 deaths and 119 survivals during the period of this study. The physical health and psychological heath domains of QoL were negatively associated with death of HD patients (p=0.011, p=0.013). However, there were no significant correlation between the social relationship/environmental domains and death. The cognition of death patients was lower than survivals (p=0.045). In medical factors, death was associated with coronary heart disease, old age (≥60), serum calcium level and comorbidity (p=0.024, p=<0.001, p=0.014, p=0.006). Multivariate analysis for death using cox proportional hazard regression with forward conditional method showed that the low physical health (<19) of QoL was only independent predictor of mortality in psychosocial factors (HR 2.696, 95% CI 1.112 to 6.535, p=0.006) and old age (≥60), high comorbidity score (≥4), low serum calcium level (<8.7) were independent predictors of mortality (P=0.019, p=0.019, p=0.003) CONCLUSIONS: This study explored the physical health of QoL, along with age, comorbidity and serum calcium level, has an important effect on survival rates of HD patients. Therefore, we should consider psychosocial factors including QoL as well as medical factors when evaluation ways to improve mortality in HD 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".