Randomized trial of high‐flux vs low‐flux haemodialysis: effects on homocysteine and lipids
Bibliographic record
Abstract
BACKGROUND: Uncontrolled studies have found that high-flux haemodialysis favourably modifies homocysteine and lipid profiles. We sought to confirm these findings by carrying out a randomized prospective comparison of high-flux and low-flux polysulphone in chronic, stable dialysis patients. METHODS: Forty-eight patients were randomly assigned to either high or low-flux dialysis for 3 months. Serum levels of homocysteine, lipoprotein (a), and lipids were compared between the treatment groups at monthly intervals. RESULTS: All patient characteristics and laboratory variables were equally distributed between the groups at baseline. Over the study duration, we observed no differences between high- and low-flux treatment groups for the following outcomes: pre-dialysis homocysteine, lipoprotein (a), total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides (all P>0.05). Geometric mean (interquartile range) homocysteine at baseline was 20.0 (16.8-24.5) and 19.5 (15.3-22.0) micromol/l for the high-and low-flux groups respectively (P=0.80), and levels did not change significantly during the study. We did demonstrate a more pronounced intradialytic effect of high-flux dialysis on homocysteine levels, which fell during dialysis by 42%, compared to 32% with low-flux dialysis (P<0. 001). CONCLUSIONS: In this randomized controlled trial, the effects of high-flux and low-flux haemodialysis on homocysteine and lipid profiles were comparable. The greater intradialytic effect of high-flux dialysis on homocysteine did not translate into a significant difference in pre-dialysis levels after 3 months of study.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".