Quality of life and nutritional status in peritoneal dialysis patients: a cross-sectional study from Palestine
Bibliographic record
Abstract
Abstract Background End-stage renal disease (ESRD) is a growing cause of morbidity worldwide. Protein malnutrition is common among patients with ESRD. Peritoneal dialysis (PD) offers greater lifestyle flexibility and independence compared to the widely used treatments for ESRD. In this study, we aimed to evaluate the nutritional status and the quality of life (QOL) along with the factors influencing these two outcomes among Palestinian patients undergoing PD. Methods We performed a cross-sectional study on patients receiving PD at Najah Hospital University, Palestine. Malnutrition was assessed by the malnutrition-inflammation scale (MIS) and the QOL score was evaluated by using the Dutch WHOQOL-OLD module. Results A total of 74 patients on PD were included with the mean age of participants was 50.5 ± 16.38, more than half of them were females. We observed a significant association between the MIS and the WHOQOL-OLD scores (p < 0.001). Malnutrition was associated with a lower QOL score among patients receiving PD. Younger age group and those with an occupation had better chances of a good QOL (p = 0.01). Patients with pitting edema and diabetes had higher risk of a lower QOL (p < 0.001). Conclusions Elderly patients, patients showing signs of pitting edema, and those suffering from diabetes should be carefully considered due to their higher risk of malnutrition and low QOL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".