Dialyzer Fiber Bundle Volume During Hemodialysis Using Large-Surface Dialyzers
Bibliographic record
Abstract
Our aim was to measure and compare fiber bundle volume (FBV) of three large-surface dialyzers during chronic hemodialysis; FBV was evaluated in patients without heparin (n = 6), with tight heparinization (n = 6), and with large doses of heparin (n = 6). Each patient was treated consecutively with three dialyzers: Nephral ST500, Tricea210, and Optiflux200NR. FBV was measured hourly by ultrasound dilution in 54 sessions (n = 270). For all patients (without heparin, tight heparin, and large heparin doses) FBV did not vary significantly from 0 to 4 hours for the three types of dialyzers. There was no significant difference in aspect of filters/tubings at the end of dialysis and the presence of clots in the circuit assessed hourly. In addition, 6 other patients on hemodialysis without heparin were treated with Nephral ST500 alternatively with and without saline flushes; thus, 12 more sessions were monitored hourly for FBV (n = 60). There was no change in FBV from 0 to 4 hours between the two approaches, and the appearance of dialyzers/tubings was similar. We conclude that the FBV of large-surface dialyzers is well maintained during 4-hour chronic high-flux hemodialysis and that the loss of dialyzer surface does not explain the difference between prescribed and delivered dialysis dose in such circumstances. The membrane AN69ST does not appear more thrombogenic nor require saline flushes for heparin-free hemodialysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".