Comparison of Dual Dialyzers in Parallel and Series to Improve Urea Clearance in Large Hemodialysis Patients
Bibliographic record
Abstract
Dialysis adequacy targets are frequently difficult to achieve in large hemodialysis patients. Dual dialyzers can be used to improve clearance. It is unknown whether series or parallel configurations are superior. Objective: to improve urea clearance in large patients using parallel and series dual dialyzers. Patients and Methods: Eighteen large hemodialysis patients (mean 92.4 kg) were enrolled in a randomized, crossover trial to directly compare dual dialyzers in parallel and series configurations. Treatments times, blood flow rates, and dialysate flow rates were kept constant. Results: Compared to single dialyzers, parallel dual dialyzers increased the spKt/V from 1.25 +/− 0.22 to 1.43 +/− 0.29 (p < 0.003). Series dual dialyzers improved the spKt/V to 1.46 +/− 0.26 (p < 0.0003 compared to single dialyzer). The Kt/V and URR of dual dialyzers in parallel were not significantly different from dual dialyzers in series. Half of the subjects failed to meet the NKF-K/DOQI recommended adequacy target of spKt/V urea >/= 1.2 using a single dialyzer. With the use of dual dialyzers 83% of subjects achieved this adequacy target. Serum levels of ‘middle molecule,’ beta-2 microgobulin, were reduced 34% after two months of dual dialyzer therapy. Cost analysis estimates annual net savings of $1260 with dual dialyzer therapy, primarily from projected savings in inpatient expenses. Conclusions: In large hemodialysis patients, our study demonstrates that dual dialyzers in parallel and series are equally effective in improving urea clearance without prolonging dialysis treatment times.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".