A review of residual kidney function in peritoneal dialysis patients
Bibliographic record
Abstract
Residual kidney function (RKF) has been associated with better survival, less morbidity, and improved quality of life in peritoneal dialysis (PD) patients. Since higher peritoneal clearance does not lead to better outcomes, more emphasis should be put on preserving kidney function. Many other benefits have been reported, including better volume and blood pressure control, better nutritional status, lower rates of PD peritonitis, preserved erythropoietin and vitamin D production, middle molecule clearance, lower Left Ventricular Hypertrophy, and better serum phosphate level. The most practical method of assessing RKF is the mean of 24-h urinary urea and creatinine clearance. Incremental PD prescription is an ideal option to supplement RKF in PD patients, which also offers more flexibility to the patient and, possibly, improved adherence. Angiotensin converting enzyme inhibitors and angiotensin receptor blockers should be used when possible in PD patients to preserve RKF. Loop diuretics are underutilized in PD patients despite providing an additional means of maintaining fluid balance and reducing the need for higher glucose-containing PD solutions. In this paper, we outline the importance of RKF in PD patients and the different strategies for its preservation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 0.001 |
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".