Cost-Effectiveness Analysis of High-Efficiency Hemodiafiltration Versus Low-Flux Hemodialysis Based on the Canadian Arm of the CONTRAST Study
Bibliographic record
Abstract
AIM: The aim of this study was to assess the cost effectiveness of high-efficiency on-line hemodiafiltration (OL-HDF) compared with low-flux hemodialysis (LF-HD) for patients with end-stage renal disease (ESRD) based on the Canadian (Centre Hospitalier de l'Université de Montréal) arm of a parallel-group randomized controlled trial (RCT), the CONvective TRAnsport STudy. METHODS: An economic evaluation was conducted for the period of the RCT (74 months). In addition, a Markov state transition model was constructed to simulate costs and health benefits over lifetime. The primary outcome was costs per quality-adjusted life-year (QALY) gained. The analysis had the perspective of the Quebec public healthcare system. RESULTS: A total of 130 patients were randomly allocated to OL-HDF (n = 67) and LF-HD (n = 63). The cost-utility ratio of OL-HDF versus LF-HD was Can$53,270 per QALY gained over lifetime. This ratio was fairly robust in the sensitivity analysis. The cost-utility ratio was lower than that of LF-HD compared with no treatment (immediate death), which was Can$93,008 per QALY gained. CONCLUSIONS: High-efficiency OL-HDF can be considered a cost-effective treatment for ESRD in a Canadian setting. Further research is needed to assess cost effectiveness in other settings and healthcare systems.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".