Kinetic Studies on Urea Extraction with Hemodialysis in Adolescents by On‐line Monitoring of Dialysate Urea
Bibliographic record
Abstract
Kinetics of urea extraction during a single dialysis session in children are unknown, because analysis of solutes in dialysate is difficult due to their extreme dilution. >Objective: A novel urea monitor of the Gambro Company might be of help in studying urea kinetics also in children. Methods: We studied 107 urea kinetics in 5 adolescents aged 13–19 years, weighing 26–58 kg, and looked for influences of membrane size, blood flow, and duration of one dialysis session. Urea measurement applies to the change of electric dialysate conductivity due to ionization because of urea splitting by urease. Bicarbonate dialysis regimen was 4–5 h each, 3 times a week, using polysulfone high‐flux dialyzers (Fresenius F60 or F80, depending on body size). Results: Average 4‐h urea Kt/V values for F60 (n = 85) were 1.69±0.53 and for F80 (n = 21) 1.63±0.25, extracted urea mass was 16.0±5.4 g and 32.5±5.4 g, respectively (p < 0.05); Kt/V urea results for blood flows of 180–220 mL/min were 1.36±0.52 and for <180 mL/min 1.10±0.43; extracted urea mass was 17.3±8.0 and 11.7±4.9 g, respectively (p < 0.05). Total average urea extraction ratio after 2 h of dialysis (n = 107) was 64.8±5.6%. Extraction ratio during the 4th h of dialysis was only 15.3±4.1% and during the 5th h not more than 9.0±3.6% of total urea extraction. Conclusion: Kinetics of urea extraction helps understanding dialysis processes in children. Adapting the size of the dialyzer according to body size raises urea extraction and maintains urea clearance Kt/V at the desired quality level. An inadequate blood flow lowers both urea extraction and urea clearance Kt/V. Prolonging dialysis beyond 4 h is, at least in concern of urea kinetic modelling, a rather ineffective means. We speculate that children with blood flow problems should be dialysed more often.
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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) | 0.000 | 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".