Albumin Loss in Post-Dilution On-Line Hemodiafiltration Compared With Pre-Dilution On-Line Hemodiafiltration and Conventional Hemodialysis
Bibliographic record
Abstract
Background: The purpose of the study was to investigate the amounts of albumin lost in the dialysate in a dialysis session using either a high-flux (on-line hemodiafiltration (HDF)) or a low-flux filter (conventional hemodialysis (HD)). Methods: The loss of albumin was studied in 10 hemodialyzed patients, with on-line HDF (pre- and post-dilution) and with conventional HD. We determined the albumin loss in the total ultrafiltrate for four different dialysis models. Results: No change was found in serum albumin levels when switching from conventional HD to on-line HDF. The loss of albumin in on-line HDF post-dilution, with a high-flux filter of 2.5 m 2 (group A) was marginally significantly greater than the loss with the same filter with a surface area of 2.1 m 2 (group B) (P = 0.05). However, there was no difference in albumin loss when comparing groups A and B with group C (conventional HD) (P = NS). Albumin loss was significantly less in group D (pre-dilution on-line HDF, with filter 2.5 m 2 surface area) compared to groups A (P < 0.01), B (P < 0.01) and C (P < 0.03). The urea reduction ratio in each case (groups A, B, C and D) was, on average, > 73.5%, but in group C, it was significantly lower than in groups A and B (P < 0.05). Transmembrane pressure in group D was clearly lower than in groups A and B. Conclusion: The polyethersulfone filters (polynephron) used in the on-line HDF lost very little albumin in a session (more with post-dilution), but this increased when their surface area and the transmembrane pressure increased. The urea reduction ratio was above the desired target in each model of dialysis using this filter, including both surface areas. World J Nephrol Urol. 2023;12(1):1-7 doi: https://doi.org/10.14740/wjnu438
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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 | 0.000 |
| 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".