Phosphorus dynamics during hemodialysis
Bibliographic record
Abstract
We studied phosphorus (P) dynamics and its relation to urea dynamics in a wide range of dialyses by measuring predialysis and postdialysis serum P levels and all removed P and urea in dialysate during 455 hemodialyses. Dialyses were performed at different frequencies (range 3-6 treatments/wk); duration of dialysis (t) (range 80-560 minutes), varied blood and dialysate flow, and with high-flux and low-flux membranes. Kt/V-P, Kt/V-urea, weekly removal of P-and urea and removal volumes (Vr) and their relationships to varying dialyses, and predialysis concentrations, and protein catabolic rates were studied in linear and multiple regression analyses. A weekly dialysis time of > 30 hours was needed to maintain serum P concentration normal without the use of phosphate binders. Vr-P as a percentage of body weight was dependent on predialysis serum P and increased steeply as predialysis serum P decreased and dialysis time was prolonged. There was no relationship between Vr-urea and Vr-P. Phosphorus removal per week was mainly dependent on weekly frequency, and time on dialysis and > 38 h/wk were necessary to remove the recommended P intake. Phosphorus shows highly variable dynamics during dialysis. The body maintains extracellular P concentration by releasing P from large compartments when the dialysis time is prolonged and the serum concentration of P decreases during dialysis. Vr-P shows huge variation between patients and in an individual patient, depending on predialysis serum P. Kt/V is inaccurate in describing P removal. To remove P efficiently, it is most important to perform long and more frequent hemodialysis.
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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) | 0.000 | 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 | 0.001 |
| 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".