A high blood level in the venous chamber and a wet‐stored dialyzer help to reduce exposure for microemboli during hemodialysis
Bibliographic record
Abstract
During hemodialysis (HD), microemboli develop in the blood circuit of the apparatus. These microemboli can pass through the venous chamber and enter into the patient's circulation. The aim of this study was to investigate whether it is possible to reduce the risk for exposure of microemboli by altering of the treatment mode. Twenty patients on chronic HD were randomized to a prospective cross-over study of three modes of HD: (a) a dry-stored dialyzer (F8HPS, Fresenius, steam sterilized) with a low blood level in the venous chamber (DL), (b) the same dialyzer as above, but with a high level in the venous chamber (DH), and (c) a wet-stored dialyzer (Rexeed, Asahi Kasei Medical, gamma sterilized) with a high blood level (WH). Microemboli measurements were obtained in a continuous fashion during 180 minutes of HD for all settings. A greater number of microemboli were detected during dialysis with the setting DL vs. WH (odds ratio [OR] 4.07, 95% confidence interval [CI] 4.03-4.11, P<0.0001) and DH vs. WH (OR 1.18, 95% CI 1.17-1.19, P<0.0001) and less for DH vs. DL (OR 0.290, 95% CI 0.288-0.293, P<0.0001). These data indicate that emboli exposure was least when using WH, greater with DH, and most with DL. This study shows that using a high blood level in the venous chamber and wet-stored dialyzers may reduce the number of microemboli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".