Peritoneal Phosphate Clearance is Influenced by Peritoneal Dialysis Modality, Independent of Peritoneal Transport Characteristics
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hyperphosphatemia is an independent risk factor for mortality in ESRD, but factors regulating phosphate clearance on peritoneal dialysis (PD) are incompletely understood. The objective of this study was to test the hypothesis that peritoneal phosphate clearance is better with continuous ambulatory PD (CAPD) as compared with continuous cyclic PD (CCPD) after adjusting for membrane transport status. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In this cross-sectional and retrospective study, measurements of peritoneal phosphate clearance of 129 prevalent PD patients were reviewed. Patients were divided according to membrane transport status (high, high average, low average-low categories) and PD modality (CAPD or CCPD). RESULTS: Among high transporters, peritoneal phosphate clearances were comparable in both modalities. However, treatment with CAPD was associated with increased peritoneal phosphate clearance compared with CCPD among high-average transporters (42.4 +/- 11.4 versus 36.4 +/- 8.3 L/wk/1.73 m(2), P = 0.01), and low-average-low transporters (35.6 +/- 5.9 versus 28.9 +/- 11 L/wk/1.73 m(2), P = 0.034). On multivariate linear regression, PD modality, membrane transport category, and peritoneal creatinine clearance, but not Kt/V urea, were independently associated with peritoneal phosphate clearance. CONCLUSIONS: Peritoneal phosphate clearance is determined by PD modality and membrane transport category, suggesting that PD regimes with longer dwell times may help control hyperphosphatemia in lower transporters.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".