Erythropoietin-Induced Changes in Protein Nutrition: Quantitative Assessment by Urea Kinetic Modeling Analysis
Bibliographic record
Abstract
To evaluate objectively the effects of recombinant human erythropoietin (rHuEPO) administration on nutritional status in stable dialyzed patients, we used urea kinetic modeling (UKM) analysis and dietary protein intake evaluation by dietary assessment. Fifteen patients (9 females, 6 males; mean age 46.9 +/- 15.6 years) dialyzed for 9.4 +/- 6.3 years were studied longitudinally for 18 months, consisting of a control period (6 months) and an rHuEPO treatment period (12 months). Treatment modalities based on 3 weekly sessions were hemodialysis in 12 patients (6 cuprophane, 3 cellulose acetate and 3 highly permeable membranes), hemodiafiltration in 2 patients and postdilutional hemofiltration in 1 patient. Bicarbonate buffered dialysate was used in 9 patients and acetate in 6 patients. Urea kinetic modeling using a single-pool model was performed monthly over 1-3 cycles. rHuEPO was administered intravenously at the end of dialysis according to a two-phase protocol: (1) correction of anemia by stepwise increment of rHuEPO dose, and (2) maintenance dose to keep hemoglobin at 10-11 g/dl. rHuEPO administration corrected anemia in all patients, improving their general clinical condition. Dialysis efficacy was significantly reduced (15%) after the 3rd month of rHuEPO therapy. Clearnces were restored by increasing dialysis time and/or improving dialyzer performances, and adequacy of dialysis was maintained in all patients. During the 12 months of rHuEPO therapy, the protein catabolic rate remained stable at 1.2 g/kg/24 h in spite of an increase in appetite. At the same time, dry body weight increased significantly after 9 months, and the ratio dietary protein intake/protein catabolic rate a gross estimation of nitrogen balance, increased.(ABSTRACT TRUNCATED AT 250 WORDS)
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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 | 0.000 | 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 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".