Optimal initiation of dialysis in end stage kidney disease patients: is it a resolved question? A Systematic Literature Review
Bibliographic record
Abstract
Abstract Is there a definite universally accepted optimal initiation of maintenance dialysis in end stage kidney disease patients (ESKD)? The decision on optimal initiation of maintenance dialysis is an ongoing common problem faced by nephrologists around he world. However, symptoms or signs of uremia are varied and complex, mainly depending on clinical judgment; what’s more, typical uremic symptoms such as pericarditis and encephalopathy in patients without volume overload often occur at a very low glomerular filtration rate (GFR) and these conditions are often combined with severe metabolic disorders and/or organ damages. The fact is that the exact optimal timing of dialysis for ESKD patients remains unknown. The study systematically reviewed the available evidence with regard to the optimal initiation of maintenance dialysis in ESKD patients, applying PRISMA and the Newcastle-Ottawa scale. The review identified approaches and methods for investigation of optimal dialysis initiation. Evidence suggests that GFR at dialysis initiation was not associated with mortality and that the timing of dialysis initiation should not be based on GFR. Assessments of volume load and patient’s tolerance to volume overload are prospective approaches recommended. The article updates and identifies approaches and methods for investigation of optimal dialysis initiation to support evidence-based clinical decision.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".