Effect of a medium cut‐off dialyzer on protein‐bound uremic toxins and mineral metabolism markers in patients on hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Hemodialysis (HD) with medium cut-off (MCO) dialyzers may expand molecular clearance, predominantly larger middle molecules (molecular weight 25-60 kDa). However, the impact of MCO dialyzers on long-term clearance of various other components of the uremic milieu is unknown. The tRial Evaluating Mid cut-Off Value membrane clearance of Albumin and Light chains in HemoDialysis patients (REMOVAL-HD) provided an opportunity to assess the effect of MCO dialyzers on protein-bound uremic toxins and novel markers of mineral metabolism. METHODS: This exploratory sub-study of REMOVAL-HD evaluated changes in protein-bound solutes (total and free indoxyl sulfate [IS] and p-cresyl sulfate [PCS]) and mineral metabolism markers (intact fibroblast growth factor-23 [iFGF23], fetuin-A and endogenous calciprotein particles [CPP-1 and CPP-2]). Mid-week, pre-HD serum samples were collected at baseline and after 12 and 24 weeks of MCO use in stable adult patients. Change from baseline to Week 12 and 24 was estimated using linear mixed effects models. FINDINGS: Eighty-nine participants were studied (mean age 67 ± 15 years, 38% female, 51% diabetic, median urine output 200 ml/24 h). Serum iFGF23 was reduced at Week 12 compared to baseline (-26.8% [95%CI -39.7, -11.1], p = 0.001), which was sustained at Week 24 (-21.7% [95%CI -35.7, -4.5], p = 0.012). There was no significant change in serum IS, PCS, fetuin-A, CPP-1, or CPP-2. DISCUSSION: The use of a MCO dialyzer over 24 weeks was associated with a sustained reduction in FGF23, while other measured components of the uremic milieu were not significantly altered. Further studies are required to determine whether FGF23 reduction is associated with improved patient outcomes.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".