INTERNAL MIXING IN TWO HIGH-FLUX DIALYZERS IN SERIES: A THEORETICAL STUDY
Bibliographic record
Abstract
Filtration and back filtration (convective flow from blood to dialysate and vice-versa) occur in high flux dialyzers, even in the absence of net ultrafiltration. This internal mixing in the dialyzer can lead to significantly higher clearances than would result with purely diffusional mass transfer. Internal mixing can be promoted by utilizing two dialyzers in series with a flow resistance in the dialysate flowpath between the two dialyzers. We explored the fluid mechanics occurring in this dialyzer set-up using a mathematical simulation. We developed previously a simple mathematical model describing the fluid dynamics of ultrafiltration and back filtration in a dialyzer. The model assumes constant blood viscosity and oncotic pressure along the dialyzer flowpath. We were able to adapt this model to the case of two dialyzers in series to obtain algebraic equations for the blood and dialysate flow rates and pressure profiles along the length of each dialyzer. The relevant equations were solved using iterative trial and error solution in Excel. The volume of filtration and/or back filtration occurring in each filter could then be calculated. The developed model was employed to investigate the amount of internal mixing in this dialyzer configuration and its dependence on filter geometry and the dialysate flow resistance between filters. We examined the impact of dialyzer design parameters such as dialyzer surface area, fiber diameter and length, and membrane permeability. The results of our simulations provide insight and a basis for optimal dialyzer design.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".