The economics of home nocturnal hemodialysis: how should we cost the benefits?
Bibliographic record
Abstract
Home nocturnal hemodialysis (HNHD) has been established as a safe and effective way to provide dialysis for patients who require renal replacement therapy. Non-randomized studies have shown that patients switched to HNHD have improvements in blood pressure, left ventricular mass and quality of life. At present, there are no RCTs or long-term observational studies demonstrating a clear reduction in cardiovascular events or mortality. Several HNHD centers have published articles documenting the costs of this modality as compared to conventional HD. Some of these studies have found HNHD to provide significant cost savings, while others have found the two modalities to be relatively equivalent in terms of costs. In this paper, we review the results of these costing studies and illustrate some of the limitations associated with these studies including the lack of randomization, inconsistent reporting of HNHD start-up costs, potential patient selection biases and limited follow-up. On balance, it appears premature to conclude that HNHD is cost-saving in comparison to conventional hemodialysis. However, two ongoing randomized trials, which are collecting resource use information, will help to answer this question. Once these data are available, a formal economic evaluation should be done to determine the impact of HNHD on both clinical outcomes and costs. This information will assist decision-makers in determining whether to make HNHD more widely available.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".