Prescriptions of dialysate potassium concentration during short daily or long nocturnal (high dose) hemodialysis
Bibliographic record
Abstract
The prescription of dialysate potassium concentration during short daily and long nocturnal (high dose) hemodialysis (HD) is challenging due to limited clinical experience with such modalities. The aim here is to propose a quantitative approach for prescribing dialysate potassium concentrations during high-dose HD. Potassium kinetic parameters based on a pseudo one-compartment model from 547 patients participating in the HEMO Study were used for prediction purposes in this study. Patients were categorized based on the prescribed dialysate potassium concentration during thrice weekly HD as 1K (mean of 1.02 mEq/L, N = 60), 2K (2.01 mEq/L, N = 437), or 3K (3.01 mEq/L, N = 50). Dialysate potassium concentrations were then predicted for each patient during short daily and long nocturnal HD based on a pseudo one-compartment model to maintain the identical weekly dialytic potassium removal and predialysis serum potassium concentration as during thrice weekly HD. Predicted prescribed dialysate potassium concentrations for short daily HD were 0.18-0.45 mEq/L higher than during thrice weekly HD but were approximately 4 (3.72-4.26) mEq/L for all patients during long nocturnal HD. The intradialytic decrease in serum potassium concentration was predicted to be reduced by more than one-half during short daily HD and by approximately three-quarters during long nocturnal HD of that during thrice weekly HD. Prescribed dialysate potassium concentration during high-dose HD modalities can be quantitatively predicted using a pseudo one-compartment kinetic model. High-dose HD modalities may improve clinical outcomes by reducing intradialytic decreases in serum potassium.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".