A simple method to estimate phosphorus mobilization in hemodialysis using only predialytic and postdialytic blood samples
Bibliographic record
Abstract
We have recently developed a pseudo one-compartment model to describe intradialytic and postdialytic rebound kinetics of plasma phosphorus. In this model, individual patient differences in phosphorus kinetics were characterized by a single parameter; the phosphorus mobilization clearance (K(M) ). In this work, we propose a simple method to estimate K(M) from predialytic and postdialytic plasma phosphorus concentrations. Clinical data were collected from 22 chronic hemodialysis patients that underwent a 4-hour treatment session. A simple algebraic equation was derived from the pseudo one-compartment model to determine K(M) from predialytic and postdialytic plasma phosphorus concentrations. K(M) values computed using this equation were compared with values obtained from nonlinear regression of the full kinetic model to frequent intradialytic and postdialytic measurements of plasma phosphorus concentrations. There was good agreement between K(M) values (concordance correlation coefficient of 0.94) obtained from the simple method (105 ± 52 mL/min, mean ± SD) and from the full model (99 ± 47 mL/min). The 95% confidence interval for the difference between estimated K(M) values was -26 to 36 mL/min. The proposed simple method requires the use of only predialytic and postdialytic blood samples to estimate patient specific K(M) ; this approach may allow easy clinical evaluation of phosphorus kinetics in hemodialysis patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".