A THEORETICAL STUDY OF INTERNAL MIXING IN HIGH-FLUX DIALYZERS
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. Possible blood contamination due to back mixing is a concern when ultrapure dialysate is not employed. On the other hand, internal mixing in the dialyzer can lead to significantly higher clearances than would result with purely diffusional mass transfer. It is this latter phenomenon we sought to investigate. We have developed a simple mathematical model for the fluid dynamics of ultrafiltration and back filtration in a dialyzer. We were able to obtain closed solutions (i.e. analytical equations) for the blood and dialysate flow rates and pressure difference along the length of the dialyzer. From this the volume of backmixing could he calculated. Unfortunately, analytical solutions are only possible when blood (and dialysate) viscosity is assumed constant along the flow path. The developed model was employed to investigate the amount of back filtration or internal mixing in a dialyzer and its dependence on dialyzer geometry. We examined the impact of dialyzer design parameters such as dialyzer surface area, fiber diameter and length, and membrane permeability. We also investigated the influence of blood and dialysate flow direction (co-current vs counter-current flow) as well as the introduction of a flow resistance at various points along the dialyzer length. The results of our simulations provide insight and a basis for optimal dialyzer design. In future studies, the computed flow patterns will he used to investigate the theoretical impact of internal mixing on the dialyzer clearance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".