Conservative kidney management and kidney supportive care: core components of integrated care for people with kidney failure
Bibliographic record
Abstract
Integrated kidney care requires synergistic linkage between preventative care for people at risk for chronic kidney disease and health services providing care for people with kidney disease, ensuring holistic and coordinated care as people transition between acute and chronic kidney disease and the 3 modalities of kidney failure management: conservative kidney management, transplantation, and dialysis. People with kidney failure have many supportive care needs throughout their illness, regardless of treatment modality. Kidney supportive care is therefore a vital part of this integrated framework, but is nonexistent, poorly developed, and/or poorly integrated with kidney care in many settings, especially in low- and middle-income countries. To address this, the International Society of Nephrology has (i) coordinated the development of consensus definitions of conservative kidney management and kidney supportive care to promote international understanding and awareness of these active treatments; and (ii) identified key considerations for the development and expansion of conservative kidney management and kidney supportive care programs, especially in low resource settings, where access to kidney replacement therapy is restricted or not available. This article presents the definitions for conservative kidney management and kidney supportive care; describes their core components with some illustrative examples to highlight key points; and describes some of the additional considerations for delivering conservative kidney management and kidney supportive care in low resource settings.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| 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".