The importance of residual renal function for patients on dialysis
Bibliographic record
Abstract
Division of Nephrology, 1University Health Network, University of Toronto, Canada and 2Vanderbilt University Medical Center, Nashville, TN, USA It is the goal of every practitioner involved in the care of dialysis patients to maximize survival and quality of life. The last two decades have seen a plethora of investigations that have sought to determine how this goal can be achieved. The bulk of the studies have, unfortunately, concentrated on small solute clearance and outcome (measured principally as mortality). The HEMO [1] and ADEMEX [2] studies suggested that this is not a fruitful avenue of investigation. However, the residual kidney function in patients on dialysis, particularly in those on peritoneal dialysis (PD), has proven to be a consistent and powerful predictor of mortality. We will review the evidence supporting the importance of residual renal function (RRF) on outcome, propose some explanations as to why this relationship exists, and suggest ways to prolong the renal function in dialysis patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".