Prescribing high-quality peritoneal dialysis: The role of preserving residual kidney function
Bibliographic record
Abstract
Maintenance of residual kidney function (RKF) is independently associated with increased survival in patients with end-stage renal disease. Presence of RKF is also associated with improved volume status, better nutritional status, reduced erythropoietin requirement, and decreased rate of peritonitis in patients on peritoneal dialysis (PD). Thus, the preservation of RKF is an important therapeutic end point in the management of patients on PD. Measurement of RKF in PD patients should be based on the mean of 24-h urinary creatinine and urea clearances, and ideally, this should be done quarterly. Compared to those started on hemodialysis, patients initiated on PD appear to have slower decline in RKF. The choice of PD modality should be based on patient preference, as there is no clear evidence to date showing one modality is superior than the other in preserving RKF. Peritoneal dialysates with neutral pH and low glucose degradation products seem to have a favorable effect on RKF. An angiotensin-converting enzyme inhibitor or angiotensin receptor blocker should be used whenever possible to preserve RKF and reduce cardiac mortality. Both loop diuretics and icodextrin can be utilized to maintain fluid balance in PD patients. However, caution should be taken to avoid volume depletion which could accelerate RKF decline. Short-term use of aminoglycosides does not have a detrimental impact on RKF, but prolonged use (>3 weeks) should be avoided to minimize the risk of ototoxicity. Lastly, potential nephrotoxic agents such as intravenous contrast should be used judiciously.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".