Recommendations for the Care of Patients Receiving Conservative Kidney Management
Bibliographic record
Abstract
Conservative kidney management is increasingly accepted as an appropriate treatment option for patients with eGFR category 5 CKD who are unlikely to benefit from dialysis and/or who choose a nondialysis care option. However, there remains great variation in the delivery of their care. As part of the development of a conservative kidney management pathway that is undergoing evaluation, a set of recommendations specific to conservative kidney management for managing the complications of CKD and common symptoms was developed. These recommendations focus on the patient's values and preferences and aim to optimize comfort and quality of life. Explanations for the interventions are provided to support the shared decision-making process between health care professionals, patients, and family members. The recommendations generally emphasize the preservation of function (cognitive, physical, and kidney) and address symptom burden, acknowledging that management priorities can change over time. The recommendations should be used in conjunction with other key elements of conservative kidney management, including clear communication and shared decision making for choosing conservative kidney management, advance care planning, and psychosocial support. Although there are limitations to the existing evidence specific to conservative kidney management, these recommendations are intended as a starting point toward reaching consensus and generating further evidence.
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".