A Two-Filter Adaptation to Achieve Hemodiafiltration with Enhanced Performance
Bibliographic record
Abstract
ABSTRACT Introduction Advancements in hemodialysis (HD) instrumentation have resulted in numerous breakthroughs in technology and significantly enhanced patient outcomes. Today, hemodiafiltration (HDF) which combines HD and hemofiltration has been widely used as an alternative to conventional HD in many countries. HDF is known to outperform conventional HD, offering more effective waste clearance and better fluid balance to patients. However, HDF requires newer-generation machines that are not accessible in many under-resourced geographical regions and societies. This study investigates a facile adaptation of conventional HD machines to achieve HDF. The objectives are to address the premature obsolescence of older but fully functional machines and to advocate for equal access to improved medical care and treatment. Methods A bench-top experimental setup was established to evaluate the performance of HDF using a two-filter adaptation in comparison to that of standard HD. Urea clearance, human serum album loss, and hemolysis were assessed under identical operational conditions for both configurations. Findings Our results show that the HDF configuration outperformed the HD configuration, with significantly higher urea clearance (268.31±44.17 mL/min via HDF vs. 53.33±13.20 mL/min via HD), but comparable human serum albumin loss and hemolysis levels. Discussion The explored two-filter adaptation presents a cost-effective method to achieve HDF with improved performance using conventional HD machines, with no added risk to patients. Further validation on patients in a hospital setting is necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".