Haemodiafiltration: not effective or cost-effective compared with haemodialysis
Bibliographic record
Abstract
Patients on dialysis continue to experience high mortality rates, in excess of 10- to 100-fold the expected mortality for non-dialysis patients [1], and report a quality of life comparable with that experienced by patients with advanced colon cancer [2]. Furthermore, dialysis is a costly treatment with annual costs in the range of €45 000–85 000 across international settings [3–6]. Renal replacement therapy is paid for publicly within most countries, and before a technology can be funded within health care systems, a comprehensive description of its costs and its impact on health care outcomes more broadly is commonly required. The authors should therefore be congratulated for conducting a high-quality randomized trial that measured the impact of haemodiafiltration, compared with conventional haemodialysis, on survival, quality of life and costs [7]. In so doing, they inform patients, providers and health care payers about the impact of haemodiafiltration on all relevant endpoints. Haemodiafiltration is a new dialytic therapy that could be considered an alternative to conventional haemodialysis since it is provided within the context of a thrice-weekly 4 h per session therapy. Given the fact that it has been shown to improve dialytic clearance of uraemic toxins, including the removal of phosphate [8], there was hope that this therapy would lead to improvements in clinical outcomes and quality of life. To address this question, the clinical effectiveness of haemodiafiltration, compared with haemodialysis, delivered over the same amount of time was recently tested in a large-scale randomized controlled trial, the Convection Transport Study (CONTRAST) [9].
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".