Advancing Palliative Care in Patients With CKD: From Ideas to Practice
Bibliographic record
Abstract
A palliative approach to care focuses on what matters most to patients with life-limiting illness, including chronic kidney disease (CKD). Despite recent publication of related clinical practice guidelines in nephrology, there is limited information about how to practically implement these recommendations. In this Perspective, we describe our experience integrating a palliative approach within routine care of patients with CKD glomerular filtration rate categories 4 and 5 (G4-G5) across a provincial kidney care network during the past 15 years. The effort was led by a multidisciplinary group, tasked with building capacity and developing tools and resources for practical integration within a provincial network structure. We used an evidence-based framework that includes recommendations for 4 pillars of palliative care to guide our work: (1) patient identification, (2) advance care planning, (3) symptom assessment and management, and (4) caring of the dying patient and bereavement. Activities within each pillar have been iteratively implemented across all kidney care programs using existing committees and organizational structures. Key quality indicators were used to guide strategic planning and improvement. We supported culture change through the use of multiple strategies simultaneously. Altogether, we established and integrated palliative care activities into routine CKD G4-G5 care across the continuum from nondialysis to dialysis populations.
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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 | 0.037 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.013 | 0.036 |
| 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".