New modes of continuous renal replacement therapy using a refiltering technique to reduce micronutrient loss
Bibliographic record
Abstract
INTRODUCTION: Micronutrient depletion is a major drawback of high-dose continuous renal replacement therapy (CRRT). We tested two novel CRRT modes, double-filtration hemofiltration (DHF) and dialysate-recycling hemodiafiltration (DHDF), aimed at reducing micronutrient loss while maintaining a high clearance rate of midsized solutes comparable to that of high-volume hemofiltration (HVHF). METHODS: Forty patients with renal failure requiring CRRT were randomly assigned to receive predilutional standard-volume hemofiltration (SVHF, effluent rate 35 mL/kg/h), predilutional HVHF (100 mL/kg/h), DHF (35 mL/kg/h), and DHDF (30 mL/kg/h). In the two novel modes of CRRT, part of the high-volume primary effluent fluid produced by a high-flux filter (AV600S) was refiltered by two low-flux filters (15 L) for recycling as replacement fluid in DHF and dialysate in DHDF, while the remainder was discarded as final effluent fluid. Specimens were collected for measurement of trace elements, folic acid, amino acids (AAs), β2-microglobulin, cystatin C, and creatinine and for calculation of solute clearance. FINDINGS: The clearance of 17 AAs, phosphorus, folic acid, copper, and zinc by DHF and DHDF was much lower than that by HVHF and comparable to that by SVHF. The estimated amount of AA loss by SVHF, HVHF, DHF, and DHDF was 10.3 (7.2-13.4) g/d, 22.1 (17.8-24.0) g/d, 10.6 (8.6-14.0) g/d, and 10.0 (8.6-11.4) g/d, respectively. Clearance of cystatin C and β2-microglobulin by DHF and DHDF was much greater than that by SVHF and equal to that by HVHF. DISCUSSION: Compared to HVHF, DHF, and DHDF have an equal capacity for removal of large solutes but show substantially reduced micronutrient loss.
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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) | 0.000 | 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 | 0.001 |
| 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".