The effect of egg white diet on phosphorus control in dialysis patients
Bibliographic record
Abstract
INTRODUCTION: Nutritional interventions have been envisaged to improve hyperphosphatemia and malnutrition, two important risk factors associated with mortality in dialysis patients. We evaluated the effects of egg white consumption on serum phosphate and malnutrition in dialysis patients. METHODS: In an open-label, per protocol clinical trial, conducted in Kerman dialysis centers, 150 hemodialysis patients aged ≥18 years with serum phosphorus ≥5.5 mg/dl were included in the study. All participants limited their intake of foods containing phosphorus for 4 weeks, and then they were divided into a control and an intervention group. The control group continued their ordinary diet and the participants in the intervention group consumed a Telavang egg white pack (containing six egg whites, 96 calories, 24 g protein) as a substitute for meat products 3 days a week for 8 weeks. Finally, changes in serum albumin, phosphorus, calcium, PTH, and cholesterol were measured. FINDINGS: At the baseline, there were no significant differences in the laboratory variables between the two groups. After 8 weeks, serum cholesterol (124.3 ± 38.1, vs. 135.8 ± 28.8, p = 0.003) and phosphorus levels (4.5 ± 1.03, vs. 6.7 ± 1.5, p = 0.001) were significantly lower in the intervention group compared with the control group. Also, serum albumin (4.5 ± 0.07 vs. 3.7 ± 0.4, p = 0.001) was significantly higher in the intervention group. Moreover, phosphorus, PTH, and cholesterol levels in the intervention group were significantly lower than their baseline values (p = 0.001). CONCLUSION: The results showed that the egg white could be a useful source of protein for dialysis patients, as it simultaneously reduces serum phosphorus and cholesterol, and increases serum albumin.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".