Equations for the Calculation of the Protein Catabolic Rate from Predialysis and Postdialysis Urea Concentrations and Residual Renal Clearance in Stable Hemodialysis Patients
Bibliographic record
Abstract
Several simple equations exist for the calculation of K1/V from predialysis (Cpre) and postdialysis (Cpost) measurements of urea concentration. Analogous equations are needed for precise determination of patients protein catabolic rate (nPCR) from Cpre and Cpost. In this study we develop three simple nPCR equations from urea mass balance theory. The equations, which include a term for residual function, may be applied to any session of the week for patient dialyzed three times weekly who are in steady state with respect to dialysis dose and protein catabolism. The precision of each equation was tested with Cpre Cpost data obtained from steady state simulations of 540 patients without residual renal clearance (KR) and 972 simulated patients with significant residual KR. The simplest equation has the form: [formula: see text] where V is urea distribution volume and a and d are constants varying with session of the week. When compared to nPCR values calculated from formal urea kinetic modeling, the error determined with this formula never exceeded 5% for the midweek or final session. A more complicated equation of the form: [formula: see text] provided nPCR estimates with a maximum error < 1.3% for any dialysis session of the week and for KR up to 4 ml/min for a 70-kg patient. The only data required for the latter equation are Cpre, Cpost, length of dialysis session, volume ultrafiltered (delta BW), and an approximate value of the patient's urea distribution volume. The proposed equations permit nPCR to be calculated simply and accurately for stable patients dialyzed three times a week.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".