Can residual kidney function affect quality of life and cognitive function in hemodialysis patients?
Bibliographic record
Abstract
BACKGROUND: Residual kidney function (RKF) may provide many benefits to patients on permanent renal replacement therapy that are reflected in better control of biochemical parameters. In hemodialysis patients, quality of life (QOL) and cognitive function are often impaired. This study aimed to assess the predictors of RKF and its impact on QOL and cognitive function in chronic hemodialysis patients. PATIENTS AND METHODS: The study involved seventy-eight patients suffering from end-stage renal disease on regular hemodialysis. The patients were divided into two groups according to the presence or absence of RKF (24-hour urine volume ≥ 100 ml). Beside basic laboratory investigations, all patients were subjected to Kidney Disease Quality of Life-Short Form (KDQOL-SF) version 1.3 for assessing the quality of life and Montreal cognitive assessment (MoCA) score for assessing cognitive function. RESULTS: There was a significantly higher score for KDQOL domains and MoCA score in patients with RKF compared to patients without RKF. There was a significant positive correlation between RKF and both of MoCA score and the physical composite score (PCS) of QOL. Moreover, there were statistically significant positive correlations between the MoCA score and both PCS and mental composite score (MCS). On multivariate analysis, hemodialysis duration was the only predictor for RKF; whereas age was a significant predictor for PCS; and MoCA score could be significantly predicted by the measured RKF and patients' age. CONCLUSION: HD patients with maintained RKF had better QOL and cognitive function. The duration of HD and the age of the patients were found to be related to RKF and PCS in this study. RKF was associated with the cognitive performance of hemodialysis patients.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".