Reprocessing high‐flux polysulfone dialyzers does not negatively impact solute removal in short‐daily online hemodiafiltration
Bibliographic record
Abstract
There are no studies evaluating the impact of dialyzer reprocessing on solute removal in short-daily online hemodiafiltration (OL-HDF). Our aim was to evaluate the impact of dialyzer reuse on solute removal in daily OL-HDF and compare with that in high-flux short-daily hemodialysis (SDH). Fourteen patients undergoing a SDH program were included. Pre-dialysis and post-dialysis blood samples and effluent dialysate were collected in the 1st, 7th, and 13th dialyzer uses in SDH sessions and in daily OL-HDF sessions. Directly quantified small solute (urea, phosphorus, creatinine, and uric acid) total mass removal (TM(DQ)) and clearance (K(DQ)) were similar when the 1st, 7th, and 13th dialyzer SDH uses were compared with the 1st, 7th, and 13th daily OL-HDF uses. TMDQ and K(DQ) of small solutes were similar among analyzed dialyzer uses in SDH sessions and in daily OL-HDF sessions. β2-Microglobulin TM(DQ) and K(DQ) were statistically higher in daily OL-HDF dialyzer uses than in the respective SDH uses. There was no difference in β2-microglobulin TM(DQ) and K(DQ) among dialyzer uses in daily OL-HDF sessions or in SDH sessions. In daily OL-HDF, albumin loss was significantly different among dialyzer uses (P < 0.001), being lower in the 7th and 13th dialyzer uses than in the first use. Dialyzer reprocessing did not impair solute extraction in daily OL-HDF. β2-Microglobulin removal was greater in daily OL-HDF than in SDH sessions, without significant differences in other solutes extraction. There was a significant reduction in intradialytic albumin loss with dialyzer reprocessing in daily OL-HDF sessions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".