Calculation of the clearance requirements for the development of a hemodialysis‐based wearable artificial kidney
Bibliographic record
Abstract
Wearable artificial kidney (WAK) has been considered an alternative to standard hemodialysis (HD) for many years. Although various novel WAK systems have been recently developed for use in clinical applications, the target performance or standard dose of dialysis has not yet been determined. To calculate the appropriate clearance for a HD-based WAK system for the treatment of patients with end-stage renal disease with various dialysis conditions, a classic variable-volume two-compartment kinetic model was used to simulate an anuric patient with variable target time-averaged creatinine concentration (TAC), daily water intake volume, daily dialysis pause time, and patient body weight. A 70-kg anuric patient with a HD-based WAK system operating for 24 h required dialysis clearances of creatinine of at least 100, 50, and 25 mL/min to achieve TACs of 1.0, 2.0, and 4.0 mg/dL, respectively. The daily water intake volume did not affect the clearance required for dialysis under various conditions. As the pause time per day for the dialysis increased, higher dialysis clearances were required to maintain the target TAC. The present study provided theoretical dialysis doses for an HD-based WAK system to achieve various target TACs through relevant mathematical kinetic modeling. The theoretical results may contribute to the determination of the technical specifications required for the development of a WAK system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".