Hot Water Reuse (HWR) of Dialyzers Gives Smoother Dialysis than Single Use (SU) or Chemical Reuse (CRU)
Bibliographic record
Abstract
Background: Hemodialyzers can be used once or reused after treatment with chemicals or hot water. SU results in infusion of plastic compounds, particularly phthalic acid metabolites, into patients and chemical reuse releases formaldehyde, glutaraldehyde, or peracetic acid into the blood during dialysis. Methods: We studied the increase in pulse rate (PR) and fall in systolic and diastolic blood pressure (BP) and patients' subjective overall quality evaluation (OE) of dialysis (1 worst, 5 best) during 3706 daily dialyses in 23 patients. Fall in blood pressure and rise in PR during dialysis and overall quality evaluation were compared as patients changed from SU or chemical reuse to hot water reuse. During SU and chemical reuse, dialysis time was shorter (121 vs. 148 min), urea clearance higher (241 vs. 175 ml/min) but ultrafiltration lower (1.5 vs. 1.7 kg/dialysis) than during hot water reuse. Results: The results are summarized in the table. Methods n Systolic BP Diastolic BP PR OE CRU 98 −30 ± 19 −17 ± 21 4 ± 4 3.8 ± 0.4 SU 2443 −17 ± 21 −6 ± 13 2 ± 13 4.0 ± 0.7 HWR 1165 −8 ± 21 −1 ± 11 −0.2 ± 12 4.2 ± 0.7 All comparisons were of SU and chemical reuse to hot water reuse, p < 0.0001. The results were the same whether cellulosic or polysulfone membranes were used. Hot water reuse, up to 25 times, did not result in changes in urea clearance, albumin leakage or Kuf, and β‐2‐microglobulin reduction rates declined by only 10% over 15 reuses. Conclusion: Hot water reuse results in the most comfortable dialysis and the best cardiovascular stability, with less decline in blood pressure and less tachycardia, when compared to chemical reuse or SU of dialyzers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".