Prediction of hemodialysis sorbent cartridge urea nitrogen capacity and sodium release from in vitro tests
Bibliographic record
Abstract
In sorbent-based hemodialysis, factors limiting a treatment session are urea conversion capacity and sodium release from the cartridge. In vitro experiments were performed to model typical treatment scenarios using various dialyzers and 4 types of SORB sorbent cartridges. The experiments were continued to the point of column saturation with ammonium. The urea nitrogen removed and amount of sodium released in each trial were analyzed in a multi-variable regression against several variables: amount of zirconium phosphate (ZrP), dialysate flow rate (DFR), simulated blood flow rate (BFR), simulated patient whole-body fluid volume (V), initial simulated patient urea concentration (BUNi), dialyzer area permeability (KoA) product, initial dialysate sodium and bicarbonate (HCO3i) concentrations, initial simulated patient sodium (Nai), pH of ZrP, creatinine, breakthrough time, and average urea nitrogen concentration in dialysate. The urea nitrogen capacity (UNC) of various new SORB columns is positively related to ZrP, BFR, V, BUNi, and ZrP pH and negatively to DFR with an R2 adjusted=0.990. Two models are described for sodium release. The first model is related positively to DFR and V and negatively to ZrP, KoA product, and dialysate HCO3i with an R2 adjusted=0.584. The second model incorporates knowledge of initial simulated patient sodium (negative relationship) and urea levels (negative relationship) in addition to the parameters in the first model with an R2 adjusted=0.786. These mathematical models should allow for prediction of patient sodium profiles and the time of column urea saturation based on simple inputs relating to patient chemistries and the dialysis treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".