Differential Molecular Modeling Predictions of Mid and Conventional Dialysate Flows
Bibliographic record
Abstract
BACKGROUND: High dialysate flow rates (QD) of 500-800 mL/min are used to maximize urea removal during conventional hemodialysis. There are few data describing hemodialysis with use of mid-rate QD (300 mL/min). METHODS: We constructed uremic solute (urea, beta2-microglobulin and phosphate) kinetic models at varying volumes of distribution and blood flow rates to predict solute clearances at QD of 300 and 500 mL/min. RESULTS: Across a range of volumes of distribution a QD of 300 mL/min generally yields a predicted urea spKt/V greater than 1.2 during typical treatment times with a small difference in urea spKt/V between a QD of 300 and 500 mL/min. A larger urea KoA dialyzer and 15 min of additional time narrows the urea spKt/V difference. No substantial differences were observed regarding the kinetics of beta2-microglobulin and phosphate for QD of 300 vs. 500 mL/min. CONCLUSION: A QD of 300 mL/min can achieve urea clearance targets. Hemodialysis systems using mid-rate QD can be expected to provide adequate hemodialysis, as currently defined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".