Guidelines for the use of economic evaluation to inform policies around access to treatment for kidney failure
Bibliographic record
Abstract
Kidney failure is the most advanced stage of chronic kidney disease, at which point patients require kidney replacement therapy (KRT) in the form of kidney transplant or lifelong dialysis to survive. Although many governments seek to provide KRT for patients with kidney failure under publicly funded health schemes, KRT requires considerable financial and human resources, which may need to be diverted from other health programmes. In deciding which KRT services to provide, to whom, and under which conditions, economic evaluation can show the trade-off between the cost and benefit of different policy options. This Guideline has been written for nephrologists, clinicians and policymakers, to build confidence in requesting, contributing towards, and using the results from economic evaluation studies. It is aimed at outlining the cases in which economic evaluation may support KRT policymaking and to lay out good practice for economic evaluation of KRT services. Recommendations cover the process of developing the policy and research questions, conducting the economic evaluation and interpreting results for policy. An economic evaluation can provide insights into the cost-effectiveness of different kidney replacement therapy modalities to inform decisions on how to best allocate limited resources. This evidence-based Guideline is aimed at equipping policymakers and medical personnel with insight into the principles of economic evaluation within the context of policies for kidney-failure services, and increasing their confidence in requesting and using economic evidence derived from such evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.104 | 0.406 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.032 | 0.021 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".