Urea kinetics are not representative for the behavior of other small and water‐soluble compounds
Bibliographic record
Abstract
Scanty data suggests that large solutes show a kinetic behavior that is different from urea. The question investigated in this study is whether other small water‐soluble solutes such as some guanidino compounds show a kinetic behavior comparable or dissimilar to that of urea. This study included 7 stable conventional hemodialysis patients without residual diuresis undergoing low flux polysulphone dialysis (F8 and F10HPS). Blood samples were collected from the inlet and outlet blood lines before the dialysis session, after 5, 15, 30, 120 minutes, and immediately after discontinuation of the session. Plasma concentrations of urea, creatinine (CTN), creatine (CT), guanidinosuccinic acid (GSA), guanidinoacetic acid (GAA), guanidine (G), and methylguanidine (MG) were used to calculate corresponding dialyzer clearances. A two‐pool kinetic model was fitted to the measured plasma concentration profiles, resulting in the calculation of the perfused volume (V1), the total distribution volume (Vtot), and the inter‐compartmental clearance (K12); solute generation and ultrafiltration were determined independently. No significant differences were observed between V1 and K12 for urea (6.4 ± 3.3 L and 822 ± 345 mL/min) and for the guanidino compounds. However, with respect to Vtot, GSA was distributed in a smaller volume (30.6 ± 4.2 L) compared to urea (42.7 ± 6.0 L − P < 0.001), while CTN, CT, GAA, G, and MG showed significantly larger volumes (54.0 ± 5.9 L, 98.0 ± 52.3 L, 123.8 ± 66.9 L, 89.7 ± 21.4 L, and 102.6 ± 33.9 L, respectively). These differences resulted in markedly divergent effective solute removal: 67%(urea), 58%(CTN), 42%(CT), 76%(GSA), 37%(GAA), 43%(G), and 42%(MG). In conclusion, the kinetics of the guanidino compounds under study are different from that of urea; hence, urea kinetics are not representative for the removal of other uremic solutes, even if they are small and water‐soluble like urea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".