Dialysate saving by automated control of flow rates: Comparison between individualized online hemodiafiltration and standard hemodialysis
Bibliographic record
Abstract
Cost reduction and quality improvement seem to be conflicting issues. However, online hemodiafiltration (oHDF) with new automatic functions offers a cost-efficient therapy compared to hemodialysis (HD). Seven dialysis centers conducted a randomized clinical trial with cross-over design: high-flux HD vs. postdilutional oHDF with functions coupling both dialysate and substitution flow rates to blood flow rates. During the 6 weeks of the study, all treatment parameters remained unchanged for HD and oHDF, apart from dialysate and substitution flow rate. Treatment data were recorded during each treatment, and predialytic and postdialytic concentrations of urea were recorded at the end of each study phase. The analysis involved 956 treatments of 54 patients. The mean dialysate consumption was 123.2 ± 6.4 l for HD and 113.4 ± 14.9 l for oHDF (p < 0.0001), the mean dialysis dose was 1.42 ± 0.23 for HD and 1.47 ± 0.26 for oHDF (p < 0.0001); oHDF resulted in a lower dialysate consumption (8.0% less) and a slightly increased dialysis dose (Kt/V 3.5% higher) compared to HD. oHDF with the investigated automatic functions offers substantial savings in dialysate consumption without decreasing dialysis dose.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".