Effect of Medium Cut‐Off Dialyzers on Calcification Propensity in Hemodialysis Patients: A 6‐Month Prospective Pilot Study
Bibliographic record
Abstract
BACKGROUND: T50 is a serum-based measure of calcification propensity and a predictor of cardiovascular risk and mortality in hemodialysis (HD) patients. Cardiovascular disease is in part driven by uremic toxins. Medium cut-off (MCO) dialyzers enhance the clearance of middle molecular weight (MW) uremic toxins compared to conventional dialyzers. However, the long-term impact of MCO dialyzers on pre-dialysis and post-dialysis T50 scores remains unknown. METHODS: The study included maintenance HD patients on low-flux (LF) dialyzers. Pre- and post-dialysis serum samples were collected in mid-week HD sessions: first with LF dialyzers (baseline) and then 6 months after switching to MCO dialyzers. Change in T50 was analyzed for a single HD treatment under LF and MCO dialyzers, and baseline and sixth-month pre-dialysis T50 scores were compared. RESULTS: Fifteen patients were included. Pre- and post-dialysis percent improvement in T50 for LF and MCO dialyzers were similar (85.2% [45.0, 282.7] and 56.3% [21.1, 238.6], respectively, p = 0.35). Pre-dialysis T50 after 6 months on MCO was comparable to the baseline (p = 0.73). Pre-dialysis T50 at baseline had an inverse association with improvement in T50 after a single HD treatment (β = -0.88; 95% CI: -1.18 to -0.57; p < 0.01), and smoking had a direct association with pre-dialysis T50 after 6 months of MCO treatment (β = -172; 95% CI: -287 to -56.8; p = 0.02). CONCLUSION: MCO dialyzers do not confer additional benefits in improving serum calcification propensity, compared to LF dialyzers. Our data do not support a significant contribution of middle MW uremic toxins' removal to T50 scores.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".