Correlation between Fractional Reabsorption of Sodium and Erythropoietin dose in Peritoneal Dialysis Patients
Bibliographic record
Abstract
BACKGROUND: Erythropoietin (EPO) deficiency of chronic renal failure (CRF) may be a functional consequence of decreased glomerular filtration rate and fractional reabsorption of sodium (FR(Na)). Decreased FR(Na) reduces renal oxygen consumption and increases tissue oxygen pressure, resulting in less EPO production. We hypothesized that, in CRF patients, there is a positive relationship between EPO production and FR(Na) and that, in such patients receiving EPO, a negative correlation is expected between FR(Na) and EPO dose. METHODS: Creatinine clearance, FR(Na), serum iron, transferrin, transferrin saturation, ferritin, and intact parathyroid hormone (iPTH) levels were measured in 91 peritoneal dialysis patients. The correlation between EPO dose and FR(Na) was studied. RESULTS: Mean EPO dose was 7076 +/- 4821 units/week and mean FR(Na) was 93.40% +/- 6.14%. A negative correlation was found between EPO dose and FR(Na) (r = -0.28, p < 0.01), and a positive correlation was found between both ferritin and iPTH and EPO dose (r = 0.39, p < 0.001 and r = 0.35, p < 0.002 respectively). After adjusting for the effect of creatinine clearance, ferritin, and iPTH, there was still a significant correlation between EPO dose and FR(Na) (p < 0.05). CONCLUSION: In CRF patients there is a negative correlation between FR(Na) and EPO dose, which supports the hypothesis that EPO deficiency may be related to the decreased renal oxygen-consuming work of sodium reabsorption.
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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 | 0.004 |
| 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) | 0.001 | 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".