Supportive Care: Economic Considerations in Advanced Kidney Disease
Bibliographic record
Abstract
Kidney supportive care describes multiple interventions for patients with advanced CKD that focus on improving the quality of life and addressing what matters most to patients. This includes shared decision making and aligning treatment plans with patient goals through advance care planning and providing relief from pain and other distressing symptoms. Kidney supportive care is an essential component of quality care throughout the illness trajectory. However, in the context of limited health care resources, evidence of its cost-effectiveness is required to support decisions regarding appropriate resource allocation. We review the literature and outline the evidence gaps and particular issues associated with measuring the costs, benefits, and cost-effectiveness of kidney supportive care. We find evidence that the dominant evaluative framework of a cost per quality-adjusted life year may not be suitable for evaluations in this context and that relevant outcomes may include broader measures of patient wellbeing, having care aligned with treatment preferences, and family satisfaction with the end of life care experience. To improve the evidence base for the cost-effectiveness of kidney supportive care, large prospective cohort studies are recommended to collect data on both resource use and health outcomes and should include patients who receive conservative kidney management without dialysis. Linkage to administrative datasets, such as Medicare, Hospital Episode Statistics, and the Pharmaceutical Benefits Scheme for prescribed medicines, can provide a detailed estimate of publicly funded resource use and reduce the burden of data collection for patients and families. Longitudinal collection of quality of life and functional status should be added to existing cohort or kidney registry studies. Interventions that improve health outcomes for people with advanced CKD, such as kidney supportive care, not only have the potential to improve quality of life, but also may reduce the high costs associated with unwanted hospitalization and intensive medical treatments.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".