Extended Reuse of Polysulfone Hemodialysis Membranes Using Citric Acid and Heat
Bibliographic record
Abstract
The concomitant use of citric acid and prolonged exposure to heat (CAH) is an increasingly common alternative to purely chemical means of reusing dialyzers. However, there are no data on the effects of reprocessing dialyzers with CAH beyond 15 uses. Increasing the number of reuses with CAH cannot be systematically undertaken unless its safety is documented. We hypothesized that discarding polysulfone dialyzers after the 25th rather than the 15th use would result in increased clearance of beta2-microglobulin (beta2MG) without clinically significant changes in small solute clearance or albumin loss. We studied 15 Fresenius F80B polysulfone dialyzers in five chronic hemodialysis patients. Dialyzers were reprocessed using 1.5% citric acid solution heated to 95 degrees C. Representative fractional collection and 10 minute timed collections of dialysate were performed at baseline and during uses 5, 10, 15, 20, and 25 for each dialyzer. Dialysate-side urea, creatinine, and beta2MG clearances were calculated, and total albumin was measured in dialysate. We used a mixed model to adjust for repeated measures (both within a given dialyzer and for the multiple dialyzers per patient). Of the 15 dialyzers studied, 3 (20%) failed before the 25th use. There was no significant change in urea or creatinine clearance with additional reuse (overall p values 0.20 and 0.60, respectively). A sustained increase in beta2MG clearance was observed after the fifth treatment compared with the first use (p < 0.001). Fractional collection showed that dialysate albumin loss increased significantly with additional reuses (p < 0.001) but did not increase significantly above baseline until treatment 25. Reprocessing of polysulfone dialyzers with CAH 25 times significantly increased albumin loss and beta2MG clearance but did not appear to affect urea or creatinine clearance. Increasing the maximum number of uses to 20 may permit cost savings compared with current practice without additional risk.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".