Potassium kinetics during hemodialysis
Bibliographic record
Abstract
Hyperkalemia in hemodialysis patients is associated with high mortality, but prescription of low dialysate potassium concentrations to decrease serum potassium levels is associated with a high incidence of sudden cardiac arrest or sudden death. Improved clinical outcomes for these patients may be possible if rapid and substantial intradialysis decreases in serum potassium concentration can be avoided while maintaining adequate potassium removal. Data from kinetic modeling sessions during the HEMO Study of the dependence of serum potassium concentration on time during hemodialysis treatments and 30 minutes postdialysis were evaluated using a pseudo one-compartment model. Kinetic estimates of potassium mobilization clearance (K(M)) and predialysis central distribution volume (V(pre)) were determined in 551 hemodialysis patients. The studied patients were 58.8 ± 14.4 years of age with predialysis body weight of 72.1 ± 15.1 kg; 306 (55.4%) of the patients were female and 337 (61.2%) were black. K(M) and V(pre) for all patients were non-normally distributed with values of 158 (111, 235) (median [interquartile range]) mL/min and 15.6 (11.4, 22.8) L, respectively. K(M) was independent of dialysate potassium concentration (P > 0.2), but V(pre) was lower at higher dialysate potassium concentration (R = -0.188, P < 0.001). For patients with dialysate potassium concentration between 1.6 and 2.5 mEq/L (N = 437), multiple linear regression of K(M) and V(pre) demonstrated positive association with predialysis body weight and negative association with predialysis serum potassium concentration. Potassium kinetics during hemodialysis can be described using a pseudo one-compartment model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".