Rate of Decline of Residual Kidney Function before and after the Start of Peritoneal Dialysis
Bibliographic record
Abstract
UNLABELLED: ♦ BACKGROUND: There is a paucity of information on whether peritoneal dialysis (PD) slows the decline of residual kidney function (RKF) compared to the natural slope of RKF decline prior to dialysis start. Our aim was to analyze the RKF decline before and after initiating PD, and to determine the principal factors affecting this decline during the PD period. ♦ METHODS: We determined individual glomerular filtration rates (GFR) for approximately 12 months before and after PD in 77 new PD patients in a large academic medical center (2008 - 2012). The GFR was estimated by the Modification of Diet in Renal Disease (MDRD) equation in the predialysis period and by averaging 24-hour urine creatinine and urea clearances in the PD period. The rate of RKF decline was calculated using unadjusted linear regression analysis. Wilcoxon signed rank test was used to compare RKF decline before and after PD initiation. Multivariate linear regression was used to identify independent risk factors for RKF decline in the PD phase. ♦ RESULTS: A significantly slower mean rate of RKF decline was observed in the PD period compared with the predialysis period (-0.21 ± 0.30 vs -0.59 ± 0.55 mL/min/1.73 m(2)/month, p < 0.01). Higher baseline RKF, higher serum phosphate, and older age were independently associated with faster decline of RKF (all p < 0.01). ♦ CONCLUSIONS: In patients with advanced chronic kidney disease, initiating PD was associated with a slower rate of RKF decline compared to the rate in the predialysis period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".